diff --git a/CHANGELOGS.rst b/CHANGELOGS.rst
index c4308093..3dd98137 100644
--- a/CHANGELOGS.rst
+++ b/CHANGELOGS.rst
@@ -1,6 +1,11 @@
 Change Logs
 ===========
 
+0.5.0
++++++
+
+* :pr:`66`: add dependency on patsy in requirements-dev.txt for new content
+
 0.4.0
 +++++
 
diff --git a/_data/2017/persons.txt b/_data/2017/persons.txt
new file mode 100644
index 00000000..107b3e60
--- /dev/null
+++ b/_data/2017/persons.txt
@@ -0,0 +1,1001 @@
+age	gender	idc	name
+37	0	4ba0b473-f8ca-4466-a65b-40e9b8ba5029	Cendrillon
+41	0	f44b004b-b01e-4835-b86d-1a42846c6d93	Cendrillon
+46	1	304895f0-f686-4b0e-8854-a705bb5a6982	Balthazar
+42	1	3f0d31d2-0ef4-4e7e-b876-07d10225cc8c	Balthazar
+41	1	f29273f4-a76c-4158-b5f5-b3e5a080a0c7	Balthazar
+39	1	dfc8cf34-37d6-4376-ba7c-e94bafbf19f0	Balthazar
+32	1	2384f13f-ecef-4472-8b23-71e153dcfa53	Balthazar
+57	1	7ffb8a8b-a829-41f1-9572-2985ee9ba05e	Balthazar
+27	0	348bac2b-8d5e-4210-9434-49199bab74cb	Cendrillon
+38	0	68ae444e-ec94-43b3-bf60-a8f81e9a0100	Cendrillon
+29	1	2fc1f9d4-64e7-4b31-ad9d-ea8e09f421c4	Balthazar
+30	0	0153b785-9acd-4d28-aad1-62f8bf2faea3	Cendrillon
+28	1	47e415f0-f4e9-40c5-a59d-adc4c0621c8a	Balthazar
+27	1	9d81079d-167a-4cc1-9ae0-b10dfa2415d1	Balthazar
+34	1	14fbc7ee-aa28-44b3-a797-ddcb11526573	Balthazar
+46	0	7391c06c-fd92-4775-99c8-e05fc7f14dd5	Cendrillon
+52	1	869ae684-2c65-4d59-af56-9c5c2e12f1d6	Balthazar
+41	1	110d68d7-ae9a-4264-9a7d-20b199b37703	Balthazar
+23	1	b8e83209-c7b6-41a9-bd38-7d2c8c9d8a4c	Balthazar
+60	1	d08db176-1a88-4634-9076-e71caca5f0a5	Balthazar
+55	1	c7bf3523-95ef-4a0f-a3b4-c8b168732708	Balthazar
+37	0	d1a40d24-915f-4b17-a62b-4df81ac3def6	Cendrillon
+52	0	8b969a7a-4175-4c5c-96ff-3b00bb63ef99	Cendrillon
+48	1	39eb901d-c4c4-40d3-9601-e1de1bdc8e95	Balthazar
+29	1	92d006fa-0cec-41a0-a2d3-514afa5e07fe	Balthazar
+39	0	ba85dbcd-d543-4183-8b2e-a48cc0ded868	Cendrillon
+24	1	a57cf4a8-b5b4-4b99-83b8-5720b5eb6196	Balthazar
+26	1	9e231521-fa2a-4cb2-9010-c550cc48e1db	Balthazar
+20	0	37f9374e-89ed-4642-b73f-4fa25b67c761	Cendrillon
+60	1	d8182ecc-0e45-4481-ab41-61a3f175ef44	Balthazar
+33	1	5946198c-c8e5-4c5b-bab6-ce4399a2877c	Balthazar
+51	1	a25d166a-6b1a-49c8-9834-c5648410b331	Balthazar
+32	0	ad0a63af-7451-43ff-8d8d-a53f2c27c8a4	Cendrillon
+35	1	d8013e44-2582-4c11-9738-edd0126cee20	Balthazar
+41	1	e456deb0-1a1d-4367-b472-9d631eeda985	Balthazar
+26	0	b02d0e50-e1a0-435d-afa8-de2aabbb38c1	Cendrillon
+23	1	ea44cfbb-6a4b-4248-b99c-25582a6f3ca2	Balthazar
+31	0	247cedea-d984-4e3f-b70a-66b8dcdc40f1	Cendrillon
+47	1	346201f2-c7e0-42a4-bda1-4841cbd16d5b	Balthazar
+23	1	e7f91541-33bd-49cc-bdf7-a4845816bcaf	Balthazar
+38	0	cd7fe0d9-6b6c-4451-adaa-2ba3b3ac90d1	Cendrillon
+25	1	3e633b47-9351-4226-9a16-d4485012958f	Balthazar
+35	0	244bf2ac-3487-45fa-9048-24508ce550b8	Cendrillon
+37	0	ab46c35f-2812-4368-89d1-496f9a7420a3	Cendrillon
+55	1	a13d33f9-7dcd-4ec6-ba3e-b072147c4879	Balthazar
+32	1	73f22dcd-c5e5-4863-87b3-efdd7ed477a7	Balthazar
+29	0	7738ec2d-2f80-46c2-b4a9-49a77ae82d02	Cendrillon
+59	0	f6c1a1e5-f188-4486-95b3-28833e128c24	Cendrillon
+22	1	8f7eb2ac-5f07-4325-8b96-15c9afa116ad	Balthazar
+35	1	43d8d18b-d0f3-4b57-9dfb-30ff083c6ba0	Balthazar
+53	0	8bfa0a71-5842-471f-95e8-733f4e5e6c45	Cendrillon
+23	0	130cf59c-92cf-4205-b005-39cebbb154ae	Cendrillon
+33	1	d076df6e-a2aa-44bc-bbb8-f38905a7e169	Balthazar
+48	0	7a5ef446-a13b-4eaa-9164-b84fc62b591b	Cendrillon
+20	0	10611f52-b76d-4202-9255-165459e1e6c7	Cendrillon
+58	1	96114449-5531-4ca8-8958-5e1d944dd5ce	Balthazar
+25	1	90987cb2-01e1-400f-b6ed-640c5866f584	Balthazar
+30	1	e1ec3d0f-f010-4007-be55-3d7d46daa94e	Balthazar
+53	1	d48edeeb-8dbc-4191-b4aa-82593189a6b0	Balthazar
+33	1	97bc51d4-5b41-49ee-bc21-b2f8adbcbf57	Balthazar
+41	1	210e4c1b-863d-4c6e-86d6-033a850b32ff	Balthazar
+40	0	5c14d886-e25b-4d3d-bbd9-3e29acb686ef	Cendrillon
+55	0	7a06ca60-5a86-4ed5-8969-211006945bec	Cendrillon
+29	0	e1da517b-29af-4634-a0d7-3248c434b290	Cendrillon
+28	0	5be4ea39-d33d-46e0-aee7-8f9a6dcf7f21	Cendrillon
+36	0	bd3a11ee-9d7e-4b83-84fb-22a0c7451fa9	Cendrillon
+54	1	3cbbf3f2-eba0-4001-b10b-e76fc9213120	Balthazar
+23	1	fc1a0c01-7836-4bc8-b88d-19fe9dd4db7e	Balthazar
+43	1	b0701437-c202-4cda-bf3f-7f71d3feaefb	Balthazar
+18	1	b4b6fde1-3924-48a9-aa95-1343aaf8cced	Balthazar
+40	0	c2ec8865-960b-454c-94a1-6baf2bda1739	Cendrillon
+44	0	a31d5588-66e1-4ac7-a364-c454c65f088e	Cendrillon
+42	0	14cca874-ca7b-4f1f-a805-1693f42ad085	Cendrillon
+54	0	6983f09a-d090-46f0-9526-a64d1b307683	Cendrillon
+53	1	a784fba2-4f42-42f0-b33e-590bc5506a74	Balthazar
+35	0	80ba1490-979a-4f1a-a6b5-c953a1622d4b	Cendrillon
+21	0	632ce034-25a2-4910-a617-bc9c97ff8b5a	Cendrillon
+32	1	384bb3b3-90bf-4c24-8234-ffc7b689a248	Balthazar
+42	1	cf593c6d-28e4-4ad4-963d-20b024e2ef62	Balthazar
+43	0	ea4af74f-1302-4aed-a6a4-196abae786d9	Cendrillon
+50	0	feacd1d0-01a6-4681-99b0-96eb07f2446a	Cendrillon
+30	1	43295dde-bfa4-42b2-b1db-b8846ece6a35	Balthazar
+51	0	6fc83f4f-7206-4a77-8a11-6319d8cca9c8	Cendrillon
+20	1	d6d597a0-b9ac-42c2-91df-d068eee35ed2	Balthazar
+58	0	4f99dea8-56be-498f-a149-5c6a540c2ca8	Cendrillon
+43	0	0a5c9859-fc35-4aee-9545-a1d13c3735d7	Cendrillon
+34	0	5e6acb3e-bf01-412f-92aa-d36a40185023	Cendrillon
+33	0	c24919a5-f8c5-4c07-92b9-3e5f23ecab73	Cendrillon
+41	1	4423efb9-1c4f-4154-8353-1c89d0e1d571	Balthazar
+25	0	af0e4bfb-ff4a-4934-a6a5-c8848b23bd7d	Cendrillon
+50	0	c2481887-bd75-486f-9827-fc8206a145d4	Cendrillon
+57	1	a9e74dae-ab5c-4e17-9629-9233534c36e5	Balthazar
+53	0	a1ed0ac9-0ea9-4217-afe4-34ea050996e7	Cendrillon
+30	1	eb202e1d-523f-4851-88b6-4d758b306b91	Balthazar
+26	1	90e28336-ab5a-4175-9f4c-6dd0e760e163	Balthazar
+39	1	427e0003-995f-4028-b06d-7f4ad69f2424	Balthazar
+22	1	41f69991-6b6c-4e58-90e1-70429f2f8a0b	Balthazar
+45	0	69fd9abc-fde2-49d8-8ff4-d4fc649fa562	Cendrillon
+60	1	7c5e15c8-dc6d-4f26-ac78-e3ace7841a26	Balthazar
+38	1	b51dc8c7-6e9a-4a85-845a-fc1b6468e848	Balthazar
+20	0	15ec6dc1-7fb1-46ff-89d2-5109c0f93b08	Cendrillon
+18	0	7b24748a-7f3c-400f-bf0b-c2be970dc9a4	Cendrillon
+50	1	003b0195-2acb-4f46-b7fa-28cf266a8f60	Balthazar
+32	0	7583884e-bd49-448a-b468-218cff12ad0c	Cendrillon
+51	1	37032451-9102-45db-9a83-1a573d63d7fd	Balthazar
+18	0	53cdd69e-5fdc-46ee-8887-82778a3ef4e1	Cendrillon
+19	0	624eadd6-db5c-4bfa-8421-87bef30dd25c	Cendrillon
+36	1	bd6908b1-53bd-4279-8433-5267154e9ffa	Balthazar
+30	0	de13c8e9-584a-49f4-ada6-9d296084cd29	Cendrillon
+40	1	3e24b638-6bac-440f-9e17-1754df801fc2	Balthazar
+32	0	f6259ec2-a2a9-42b8-a03c-3a1bec7972d2	Cendrillon
+34	0	c1561a19-17a4-4ba6-a1b6-307fe60d9c17	Cendrillon
+28	1	4d7600bd-8a91-496b-973a-2f4b972c1b10	Balthazar
+49	1	fc58f523-d330-47aa-95d5-5f2c63e47146	Balthazar
+21	1	36f380b5-c4b0-44fd-9af3-41f6f1bd9b93	Balthazar
+60	0	74236ab6-743e-4441-b0c7-8af296f8b3f6	Cendrillon
+34	0	4c54eb5c-2302-4209-a21c-6fea8f100258	Cendrillon
+31	1	a4b57173-a0e9-493a-9a24-84939941b561	Balthazar
+48	1	f92c4b73-e839-4964-a6e7-7116a26c9555	Balthazar
+43	1	ac4a8d23-4268-41f1-bd25-9de4d77f5b80	Balthazar
+50	1	8e3ea482-cc60-469c-a1a4-6bd61ab8f9dc	Balthazar
+21	0	5b7d257e-ca07-4271-9c81-7e70a7bf07ed	Cendrillon
+35	1	bf4832a7-57f6-45a9-9f33-362a3d46939c	Balthazar
+22	1	a6f6fbb5-8e41-493a-a41e-c56bcbd4df1a	Balthazar
+36	0	290f1e14-5d74-446e-b375-48f577bd8d9f	Cendrillon
+39	1	f7beafbd-a9e2-423a-ad38-a6165905fac0	Balthazar
+43	1	0f4127b2-6cf8-4196-a10e-ee314f336100	Balthazar
+34	1	1103b12c-d00d-4b8c-ae34-74140d893141	Balthazar
+28	1	f2d46133-1789-45c6-9842-f928f7088bde	Balthazar
+36	0	29e9f295-2b47-4d0e-a9cd-b5dab6aadb83	Cendrillon
+51	0	5e218ccc-c48e-45f8-aba6-776b7fede4ad	Cendrillon
+45	0	b020e647-b6b3-4c9a-aed8-ab8554881e44	Cendrillon
+53	1	99508c2a-9eec-4838-82da-944e554db9d6	Balthazar
+22	1	9ea07248-b263-44d9-8f41-d19fb6aa842f	Balthazar
+34	1	6aead63c-7bb9-4ee2-a245-c4c69a029bff	Balthazar
+44	0	b9019ac1-7f42-45b6-92df-a70ecdd9993b	Cendrillon
+55	1	5e5c4bac-d74b-4600-94d4-3e5b58387797	Balthazar
+33	0	a4a985b9-40a0-4b36-89af-5a3c65b1c7ac	Cendrillon
+23	1	3e3f034a-ba38-4e8c-a87e-621726343e7a	Balthazar
+40	0	73d3c796-f4c1-4cb0-ab85-000b0a19dc32	Cendrillon
+38	1	9397b3e8-58fa-4fc0-bca2-db90731c4c1d	Balthazar
+52	0	b6783663-a7ed-42f5-bab7-3da8db1feb43	Cendrillon
+49	0	16010e70-c79c-4ed9-8e42-4ea81f0d9cc6	Cendrillon
+51	1	43850027-5359-4646-aa49-f9876a36898a	Balthazar
+31	1	186b38c7-6dc5-49e0-8edc-a1884b9bb6ab	Balthazar
+35	0	d4a3e0f5-c664-4b80-89cf-1972739a5897	Cendrillon
+56	1	23e17b86-e92a-4911-8a71-1aeba753c0ed	Balthazar
+35	1	2050e597-9387-425b-83e6-8f3ffcd74915	Balthazar
+51	0	8a5b4b21-a19c-4d1b-8d9d-3dab241a8064	Cendrillon
+32	1	7fbf3aa1-3959-420a-8966-7185778ba276	Balthazar
+33	1	a0a71ddf-de84-4baf-bba8-a746719ee8af	Balthazar
+43	0	d8259b1e-57b2-41b0-b626-2ceea9f21a46	Cendrillon
+33	0	db73e76c-ebf8-4928-879a-31351fe48e88	Cendrillon
+28	1	464a3c67-381f-4de3-af8c-6f9c6fe7a5c2	Balthazar
+55	0	2407e184-01a0-4815-8a24-94357618629e	Cendrillon
+39	0	96b0c126-de5b-4473-bccd-0edc114387eb	Cendrillon
+29	1	02c7cdd3-694f-4670-ad68-d5921a08ac7a	Balthazar
+51	0	6790e5cf-35b7-44bd-83bb-95a83e88ff71	Cendrillon
+34	0	c07eaae6-a8fb-4fba-b752-2a3a6c8a4075	Cendrillon
+44	1	4fc802b8-1bb5-42e1-8877-50b86ffa1ae3	Balthazar
+59	1	fd6cb223-85ed-4bd9-b156-26b2ff4fc273	Balthazar
+35	1	8253f021-4c2b-4b1a-8be4-8755e2401a7c	Balthazar
+18	1	6423a722-4769-4a7c-8d1d-266538c2a07a	Balthazar
+45	1	cd50cf03-d91b-4a6b-a7b9-686b4b9032f4	Balthazar
+24	0	a518033f-fdc0-4536-b912-c935fcf73aee	Cendrillon
+39	1	2722b0ef-8626-40b2-b029-c1fd96903643	Balthazar
+42	0	cd9e2183-dab5-4544-9e61-a62b88caa686	Cendrillon
+37	1	c9b5bb06-21cf-4538-bcef-447a874d4b21	Balthazar
+59	0	55caeaba-abe4-45ee-9066-1f77e92b9691	Cendrillon
+57	0	3c910b05-e8ac-4755-bb92-7d2a87ce98e6	Cendrillon
+53	1	a89301c1-f80d-4db2-8a94-9def9c430554	Balthazar
+24	0	cd3c3220-c2eb-47f9-90d6-0ae3be8b8071	Cendrillon
+26	0	b3767aea-94d7-4231-b8ec-d32d21fbffbd	Cendrillon
+39	1	7b62bda3-95d6-40e7-81a9-a0d5ccec14ab	Balthazar
+42	0	2a5fa278-535e-416e-99e4-956f02c0463e	Cendrillon
+57	1	3ee67342-354e-4bac-9c1e-d51942469739	Balthazar
+40	1	281a6537-4152-44a8-8da2-0c75f3ea2fb3	Balthazar
+20	0	fc2ab046-2595-4d1d-9a05-1b9f336f5d22	Cendrillon
+21	1	c0839c92-b6ac-4412-9d7b-39ca4c36f304	Balthazar
+57	1	d2ce4f68-1b84-46c7-855e-d764ed01b914	Balthazar
+19	1	85927900-78ad-45b4-87f6-889df6afe0d8	Balthazar
+60	1	06e78289-6a85-412e-bf0c-a11dfe993372	Balthazar
+45	1	26d158f0-4849-41fa-a657-57753e5ab36b	Balthazar
+29	1	cc7751fc-a74c-4789-ba15-550bf83c367c	Balthazar
+25	1	d9ed01eb-0ba0-44a7-818b-05bdc9f50cf9	Balthazar
+54	0	e8480178-d76e-408a-bef6-cc9ce7c9309a	Cendrillon
+38	0	1b598477-244c-4be0-90c2-5326aa8cf3c9	Cendrillon
+41	0	193aca50-23ff-4266-90d4-ec3510a18606	Cendrillon
+27	1	d76f7bea-06ba-44e7-b8eb-17216ff9d340	Balthazar
+18	1	b077d649-07dc-4faf-913b-8a97af3e638e	Balthazar
+47	0	2de1dc0d-fad1-4d76-ad60-d11c7d401c65	Cendrillon
+30	0	e9cb99dc-8251-4c8b-af32-ac06c74d00aa	Cendrillon
+45	0	9b3e4ccc-0ac2-46fe-989b-e7e7fdf61c70	Cendrillon
+25	1	91974577-78fc-4e91-a0af-b3894a0c9745	Balthazar
+42	0	029abf2d-56a1-48e5-b776-4958d1e6f233	Cendrillon
+45	0	04ec4702-d91c-49a8-846b-1dfae5b0593d	Cendrillon
+44	1	9a6b4a98-2c35-4f76-8768-ac41b555165d	Balthazar
+47	1	a433f603-00be-4c5e-81bc-188725ffe240	Balthazar
+51	1	85b0f584-3e13-44e5-a685-cd2bfb779d9b	Balthazar
+25	0	7fe429d7-4749-42ad-ba85-5c0fc9bda005	Cendrillon
+28	0	39222b6a-ea17-466a-899b-f2f9235bdc56	Cendrillon
+53	1	b62d477b-d8f3-4c7b-b602-e69b08921d3c	Balthazar
+34	0	06c7ac4c-fc3a-4fe7-9645-ead38cfffa8b	Cendrillon
+60	1	7df53d9b-ef69-4b5b-b90e-3d283a8aa7f5	Balthazar
+22	1	242bc1df-fe37-4712-b115-b27f7abad850	Balthazar
+47	0	c2de3047-2787-4dfb-839f-91b6a702d388	Cendrillon
+52	1	5a2a7a4c-5b3c-44d9-816d-859964917b27	Balthazar
+31	0	25c95966-6314-4578-84b4-9fd9ad9c498c	Cendrillon
+53	0	f0c79f97-ac3d-43ea-851b-ce1ade1d9337	Cendrillon
+47	1	6b21770f-53a7-4f4f-87b0-22a49236129f	Balthazar
+45	0	8a5d03cb-0ee8-4c8e-96fb-e02b1111ee75	Cendrillon
+29	1	b712806e-2da6-4e18-925e-3ef1817cec68	Balthazar
+26	1	62ac16b2-5e32-4893-94b2-2bd5995168c9	Balthazar
+51	0	72af3afa-32c8-4e36-ab8b-1e953f11bc09	Cendrillon
+47	0	ab4bf594-6006-43dd-8b93-2fbb4fe0d094	Cendrillon
+43	0	6dd65e3b-8bb4-4b80-a7cc-fd8056902035	Cendrillon
+35	0	30a2514a-b5f5-46fb-8ca2-ac6927a39317	Cendrillon
+47	0	b6321cca-c1c3-4fa3-ae73-b86847e1009d	Cendrillon
+31	0	f8a07bbc-6836-496e-bf25-fed0b761df31	Cendrillon
+38	0	0ab21088-e09d-49eb-b5bb-7a46fdf781d3	Cendrillon
+46	0	e4eef9cd-2146-4ab5-8e92-a444b732467f	Cendrillon
+60	1	4b89b562-2496-46b0-88b4-7e22cd610bd2	Balthazar
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+45	1	ce461b80-8c68-448a-bfa3-a5e36e31d39b	Balthazar
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+39	0	c0e29da4-70b0-4b60-9eec-69e69cb8fa6d	Cendrillon
+39	0	676f682b-d3e1-4734-a660-f863e49991b4	Cendrillon
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+47	1	a8183aaf-bfd8-4a14-9e89-a7d7f15ffc81	Balthazar
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+27	1	6e607a13-0c8d-4889-acea-1f2341d3dee3	Balthazar
+47	1	71675e63-0e46-40c0-a0f9-2331b2d6b326	Balthazar
+47	0	9bad325f-2f41-4095-b35d-9fee89ed68e4	Cendrillon
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+35	0	9d105ce2-871f-4d32-9819-95947681b8b8	Cendrillon
+41	0	d306275b-c114-4ab2-aa81-e4cc74bda23a	Cendrillon
+54	1	7d202fc2-ad68-4e01-9f3d-26970fdb4854	Balthazar
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+38	1	c0de1bec-7369-4351-b1fe-e984a0d7e079	Balthazar
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+37	0	db7dadb2-f261-4368-89a2-6c97f7b956d3	Cendrillon
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+36	0	1fdaeb14-e480-4cef-bcbe-a2d8fb29a1f9	Cendrillon
+38	0	f8c44c2e-3ec1-42d3-92dc-f17eff7d7269	Cendrillon
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+41	1	a76a5f08-c52c-4a85-a673-10e2b773c6c2	Balthazar
+44	1	b37c4638-e484-4603-8323-9567e9e5f8ce	Balthazar
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+30	1	1c6b904c-afec-4bc3-83ad-61bc14c53710	Balthazar
+38	0	18171544-6e17-448f-a025-e4fc1ed381a3	Cendrillon
+18	1	ae902254-276d-4832-a723-e1dc04205538	Balthazar
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+58	1	db43255a-a95b-45c0-a766-5766859448ba	Balthazar
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+42	1	ba3fda0f-662b-44e3-b56c-9785340c709d	Balthazar
+30	0	c5a82435-1d1b-4648-8fe8-b38762c629a0	Cendrillon
+33	1	681b79e8-7132-4832-8035-0a5fa3eb487f	Balthazar
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+38	0	717f1c7c-245a-4399-8f9f-34928b89379b	Cendrillon
+35	1	1a02a156-d05d-4827-ab9b-7f58243a8e62	Balthazar
+23	1	8e36c93e-8b58-4c29-b9bc-67418eebbab0	Balthazar
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+32	1	c1c1c590-fbc6-460c-bedb-ed1c500db47f	Balthazar
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+27	0	ba0ab63a-1b1c-403e-bdf3-52c72e2d71fd	Cendrillon
+49	1	2141ee19-08a5-49cc-90b7-c360971596e9	Balthazar
+28	1	22c3d3ec-5007-4568-b8e9-fb3ab2acdcbb	Balthazar
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+38	1	8dc01923-1ee6-4cc4-a736-f9a65c79e7de	Balthazar
+48	0	97ed65dd-0bc6-49c1-9855-327a4a638b8f	Cendrillon
+57	1	01a754f7-e18a-451f-b781-971d6c56d973	Balthazar
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+48	1	e34723d2-2e3a-416a-9dc2-9eacd3192291	Balthazar
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+45	0	1830e697-f8cb-434d-9e65-accf15973e28	Cendrillon
+42	0	64bdeb8f-e12f-461e-a66a-5d369de8eff7	Cendrillon
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+56	0	cb2650ce-c508-4185-826c-8d39b1db2d14	Cendrillon
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+31	1	5c143ae5-bdef-47a0-b375-2a4f8fb50dfc	Balthazar
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+47	0	7b7a75a3-42e8-44fc-877e-4a0a1876e6c7	Cendrillon
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+54	0	baf3fbe5-2c78-4b8b-8130-19b4a6117bb2	Cendrillon
+60	1	803fa0e9-e163-4ba4-80df-4c30a6926864	Balthazar
+34	1	d5b04e6e-905d-4e46-a28e-8ce459973c32	Balthazar
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+43	0	a7c75f48-e5d5-491f-9c5a-895b554bae74	Cendrillon
+39	0	a34a5a7e-45e0-4641-9f32-796ffe9e1442	Cendrillon
+38	0	5b969420-fd13-4ca0-90df-f608a0966148	Cendrillon
+55	1	3ae96f54-8c8d-46c4-aace-c418af3c83e6	Balthazar
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+47	1	1e864c8e-4339-42e8-94eb-b4f7e8afa8a4	Balthazar
+44	0	1ab5e46e-173e-407e-a245-04dee30bd68c	Cendrillon
+36	1	22977013-0ae6-4eb5-9740-902d67b38088	Balthazar
+39	0	cb8fc3ef-f295-4838-a4d5-4f0fb0dd3aca	Cendrillon
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+21	0	48c3d7c6-75f5-4587-8430-6a4fa129ffc4	Cendrillon
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+49	0	cc1b1dd0-fadc-4aa0-9490-88877aa6d835	Cendrillon
+32	1	d2149c33-d662-4b7b-9d12-ecbf5875f44c	Balthazar
+58	1	92da1ea7-2ecd-4b62-9623-5e7f163537a4	Balthazar
+58	1	5b0c62e5-0644-473f-bf29-94462dd6be0e	Balthazar
+42	0	d2f3b19f-4ab6-4d57-b858-c3a3579f342c	Cendrillon
+24	1	c81f4495-44a8-42da-9aa9-2de69c8593b8	Balthazar
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+54	0	0dae91c6-a422-4620-b2a2-558ab22427ba	Cendrillon
+45	0	788bfe68-a93d-45df-8a3e-d6b575b389dd	Cendrillon
+31	0	a751c9d9-4089-46c8-bbbd-fec14a227e3a	Cendrillon
+48	1	5f4ec2df-1684-4a64-8120-a467311fd771	Balthazar
+47	0	d358c924-40bd-4a5a-8091-b3714eab8c3a	Cendrillon
+20	0	d0392f37-54f5-4455-bb32-9cc892487f82	Cendrillon
+35	1	5bf1880d-a685-4466-9d15-dfb6e5f9cf1c	Balthazar
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+46	0	82108d46-fdb7-4e80-ae92-c079dd56dbee	Cendrillon
+24	0	54712fcc-f3a3-4067-bada-3df590d07109	Cendrillon
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+60	0	02b1ce8e-0baa-4dc1-83d7-34a6aa71b066	Cendrillon
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+54	1	583ed032-540e-4788-b205-09120be8cf7e	Balthazar
+58	1	d8ae26c8-9443-4488-858e-70dee4acee9f	Balthazar
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+46	0	bb2674e8-79c0-449a-bc1d-5452dc007cac	Cendrillon
+18	0	30d78a81-2a1e-4303-a2e0-be32173a5b91	Cendrillon
+47	0	8ae9421a-9fc5-4f43-9894-cbd61d7a39df	Cendrillon
+27	0	a3624145-dd22-400a-b7ad-b248e85b342b	Cendrillon
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+19	0	7b69de26-45bc-43b3-8293-b4d9b1d3c404	Cendrillon
+32	1	d9dd93fe-8eea-4654-8789-88e00205be9d	Balthazar
+37	0	bed794a1-0546-4a42-885b-50ee8a076715	Cendrillon
+41	0	e656771c-d6f4-475a-8224-3bde78b7a3c1	Cendrillon
+37	0	82267193-88e6-40d6-a87e-e6d9a0404af5	Cendrillon
+39	1	de96e9e6-1050-46de-ae6f-bbeaaad77e00	Balthazar
+39	0	7296aa5e-7f01-4187-9650-95db88995f41	Cendrillon
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+31	1	340aade6-77f3-434c-8b80-777724820f98	Balthazar
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+33	0	d4113e27-cceb-434c-a3f2-ac4bdf74a8fe	Cendrillon
+42	0	032db16b-fb1b-46da-bf7c-56c8eb3d6a2a	Cendrillon
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+52	0	1557d391-e2ca-41e0-8a04-ee3bfbc94504	Cendrillon
+26	0	d40caf2c-29b5-4f0e-812e-4b0b8977b89e	Cendrillon
+32	1	f0107e44-ac7e-4cb1-9262-a27ef6f05efe	Balthazar
+35	0	2c584f8e-339e-4086-bc6a-e47be9449260	Cendrillon
+27	1	8d818ff9-1556-4674-bdc9-7917f45e45b0	Balthazar
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+22	1	4cbc3f1c-e396-4ee9-9218-96d662872306	Balthazar
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+60	1	39f8a2d9-e839-46f6-9c0d-8ecce6fdcfdd	Balthazar
+29	0	1efe6567-6009-4dd3-9dfa-da7130c4e359	Cendrillon
+60	0	6e6c89e2-b881-4e80-ab2c-f4293c17fa54	Cendrillon
+41	0	a8d683db-3249-4d66-8d4b-f29d077c6e25	Cendrillon
+28	0	ff914105-cfac-4584-b3b2-6e47b7379e3e	Cendrillon
+24	1	ca23ae3e-6118-4146-9a84-0fcd934fd6f1	Balthazar
+49	0	af1f165b-f08c-42cc-ace5-6806d2fa543f	Cendrillon
+55	0	6c4026f7-710f-4328-99b1-6e30e97cff60	Cendrillon
+23	1	c2bc14da-54cb-4654-bd0c-747dd881d065	Balthazar
+28	1	d84eec53-e027-4bad-996a-c33ad013cef5	Balthazar
+32	1	92a650b1-ffd0-4e01-8f3d-8f6deb42274f	Balthazar
+39	1	d787f649-b901-4d91-9797-d3418163a316	Balthazar
+19	1	a39216c9-d386-45a0-a376-51cbf367847a	Balthazar
+42	1	baf28ac4-b212-493b-9a83-fb4a48ea6430	Balthazar
+32	0	7e4ffadb-91f9-40da-a39c-fa0d613bdaea	Cendrillon
+26	1	efafc48b-785f-410f-8baf-2c9363960bdb	Balthazar
+45	1	2420b7ca-8aa1-4bed-8190-4f46eea9ef91	Balthazar
+46	1	7da80df4-7202-4ab0-932a-81223e9257de	Balthazar
+23	0	1e92b4a5-a8b0-4b8f-b834-5f1f23cb245c	Cendrillon
+29	1	b91cf480-2f26-41a5-baf7-c2beaae6f856	Balthazar
+28	1	b32da025-00e6-4279-b745-2fbf639267e8	Balthazar
+40	1	9fb283a1-bfea-4e30-b5c7-540a764a60dc	Balthazar
+56	0	d5cb022e-6d2c-4299-8aa4-3d4a7150897d	Cendrillon
+45	1	dc8d2064-e7c2-437b-b8f2-e2b34573a406	Balthazar
+46	0	dbce8b12-e3a3-4d06-85f1-e426aa38b700	Cendrillon
+19	0	a1e8e1bd-9d3c-4935-8c06-0e2ec674d3f1	Cendrillon
+48	0	543ad0f6-b3ba-41e6-8195-bc936f3faf42	Cendrillon
+18	1	7722da59-f0ea-4d4c-ba80-c59ec492f3e7	Balthazar
+27	1	18ca64b3-5aeb-49fc-bc0a-76735de18742	Balthazar
+43	0	ce2e68af-23a4-4f90-9a01-261e24111daf	Cendrillon
+49	0	07fb52ed-e2fb-47f5-a12d-cf072279acda	Cendrillon
+19	0	0778ec05-981b-49e9-a73a-74d7755084a5	Cendrillon
+53	1	f910f552-d774-43a7-8a78-32f9be9ffdf8	Balthazar
+26	0	bc7db276-0c19-4a3e-9a25-abc902878960	Cendrillon
+40	0	ecc69aec-b5e9-475a-bdcf-70a083b5c21a	Cendrillon
+21	0	8333d205-4002-4d6f-af69-3ac08d17878b	Cendrillon
+24	1	e4f5c075-009b-4242-96de-feebf785fd1c	Balthazar
+39	0	1b94c78c-a4be-42bb-a2b6-0ddb9dae1421	Cendrillon
+40	1	0781d595-3b9b-41e9-8940-85a2c9988b85	Balthazar
+34	0	d23eaf1f-b202-42e1-861f-d63ed1861105	Cendrillon
+60	0	d572af9a-4b70-4714-a765-bdc70a5ccf85	Cendrillon
+46	1	18626b9d-2cd4-47ba-b985-17c98ebdb51e	Balthazar
+54	1	1240a96e-802d-46c1-b285-d2dfba992328	Balthazar
+40	0	f3d3b570-7587-4398-a3b4-a55a4fc77e0f	Cendrillon
+21	0	09fd7fb5-1b1d-41a0-945e-12154adef1bc	Cendrillon
+22	0	2b892cbd-43a2-4842-ae17-16b622d439a3	Cendrillon
+43	1	c44e6212-4ddb-48f7-89f2-76033a400598	Balthazar
+44	1	5634a6d3-abe8-40b6-bfaa-228acbb5d102	Balthazar
+22	0	09e85867-2998-46b4-a92a-5989ade95f27	Cendrillon
+37	1	ca7e63e4-0bc0-4f7a-9b23-2485dd83c0b1	Balthazar
+51	0	f31e7dbf-48ec-4d0f-8a3b-a3d6ad6198da	Cendrillon
+32	1	99354f7a-27ec-45c8-938e-b9c00330f3f7	Balthazar
+57	1	64418eca-cd7a-403e-83df-0335cbde7039	Balthazar
+40	0	085e2a70-f46c-4fed-a742-f9ccf86c704d	Cendrillon
+56	1	9d9f5a80-40e4-4dfc-a75f-427e983ade52	Balthazar
+39	0	b9503d2c-1dc6-4e01-8e4e-994f41545179	Cendrillon
+59	0	931794c0-46d0-43c4-b311-42b9c8315109	Cendrillon
+59	1	f4542f00-ba1e-45e9-9ecc-55434e9ef4ae	Balthazar
+59	1	4af07f20-04c2-4c5d-8cea-f7e5e8e579ff	Balthazar
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+54	0	e9453533-6235-42f9-9ed6-31258e0822b3	Cendrillon
+52	0	b0851a49-0276-4223-894b-fba6145b8b9c	Cendrillon
+50	0	6106c8cf-5c79-47c8-bc8a-f61a909441cc	Cendrillon
+39	0	041783e7-efb2-49e1-a853-8a09c8a8c1c2	Cendrillon
+49	1	16f9ba9f-766e-4859-9c84-78590b2e72c3	Balthazar
+25	1	57abd160-c70f-4e84-9ee7-5412bfd5f2e2	Balthazar
+23	0	96e1d262-1550-4cb3-9507-fffe9d9934b7	Cendrillon
+31	0	e0d8c260-8eab-459c-bfab-cef0e8fed713	Cendrillon
+48	1	0fc5434a-26c0-4055-84ce-a35afa823073	Balthazar
+31	0	53217aa9-1f20-419b-b49a-2e433caf12b6	Cendrillon
+60	0	1c9e942a-010f-4ac4-8984-7e678ef66a85	Cendrillon
+39	1	171df082-2298-4965-b7fd-182dfed57c00	Balthazar
+31	1	0f5984a9-2b74-4463-bffa-29d110bd1589	Balthazar
+55	0	a41a3c4e-da56-4720-a6ae-78ae436f4b94	Cendrillon
+32	1	2f3af927-50fb-4c6b-8881-fb0f3c10cf72	Balthazar
+53	0	e39f010c-714e-42f4-aa50-fd3ddf884be9	Cendrillon
+39	1	ba1f0912-1c5b-4583-8e48-a99e8462c123	Balthazar
+57	1	b580d765-8919-4287-9f9d-ca4c2d0b1ee8	Balthazar
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+30	1	291cd914-fc1a-4341-b7b1-1d35bfbf257b	Balthazar
+53	0	2e33603f-29f0-4b2e-9e00-cf89d002571a	Cendrillon
+34	0	91f74102-0ef5-4b65-9e0b-6ac77237e286	Cendrillon
+49	1	795960f3-d023-44d4-b246-8eddb4c24a7a	Balthazar
+27	0	35651861-0438-4733-aeb7-1a54f1a5b6df	Cendrillon
+36	0	6a1bac3c-a75d-455c-90ac-5b69e5daaf6e	Cendrillon
+47	1	9cfd5d51-c96d-4798-9ada-7fd030dd5d77	Balthazar
+20	0	ba91a4fa-0923-40cb-8b37-147e0f8567bd	Cendrillon
+53	1	444faab0-3fc3-4e8f-84b8-8efef481dfe0	Balthazar
+48	0	684d7812-f594-4165-bcd2-a73fd633d4d1	Cendrillon
+26	0	ac5caf01-5d9f-4246-b2f0-3e0b16df7d4f	Cendrillon
+49	1	b5c57a0f-a47a-4e0d-bae4-862495d54758	Balthazar
+56	1	3ab09a52-eb6c-4ef7-9c69-172b5c4e973c	Balthazar
+54	1	336adab2-b3f5-4ea0-8f7e-34e7bb0e355f	Balthazar
+48	1	316f0609-2c7e-4452-be60-4bb06ec0c5cf	Balthazar
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+53	0	3b207cf4-f8ce-4847-b440-fdf28c5b200e	Cendrillon
+48	0	b4b81328-bc7b-4ffb-8f37-6f9eaa223105	Cendrillon
+33	1	211c0d61-0486-459f-90e7-1b6ee2aa2efb	Balthazar
+49	0	5cd5ebff-ae63-4be9-be41-f02552a9b858	Cendrillon
+39	1	ff2a8005-3c37-424e-a2fc-135f221156ab	Balthazar
+47	1	8d5016c7-c209-4f4e-a086-e77195d7e2cf	Balthazar
+59	0	feba7cdc-bdcb-4c9e-8af1-453a3afc7cfc	Cendrillon
+18	1	aecd6a28-1ffa-487f-8fab-dd35deeb74bf	Balthazar
+39	1	7fd07061-c1ab-4ba4-83d0-4f7f65631a4a	Balthazar
+38	1	1c4b2be5-eded-4db8-a11a-538bc1d42adc	Balthazar
+39	1	57eb72ac-b632-4c99-9c8f-9a1ea158d998	Balthazar
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+33	0	6cbdc2d8-e242-4ddd-8a94-86f303c5164e	Cendrillon
+60	0	53f227e6-0efd-4a0f-b6a0-7e8401701351	Cendrillon
+51	0	d8105702-28ee-48f4-bf04-1bfe80fe9c9f	Cendrillon
+34	0	7a520b7f-e6d2-4f39-9022-9247f033c20e	Cendrillon
+36	1	c73626b2-d4d0-4333-a7a3-7225c184964f	Balthazar
+27	1	0f89f2c2-672d-494e-935f-1e3a264b05d4	Balthazar
+18	0	f899af2e-c63f-4769-8918-e2cf5b23bd0a	Cendrillon
+30	1	4b106545-38c6-4330-887b-14d7411fb497	Balthazar
+44	0	3d27eb45-b249-4cd3-b226-b8c1ab4f010b	Cendrillon
+60	1	6bc69998-6cfe-4470-b099-3c690b20eaa9	Balthazar
+31	1	44ffb288-4fb7-4765-95ff-8b55f2e7f5a4	Balthazar
+26	0	f75c61ee-7756-4e4f-bc3f-e470fe0c45b0	Cendrillon
+35	0	ddd02d21-a803-4c22-a838-d162f6da8e45	Cendrillon
+22	0	864182e4-0d07-408c-a7a0-57ca813ac7d0	Cendrillon
+60	1	175c12e0-2b19-4716-a23b-3a058bc0da06	Balthazar
+34	0	0ab755bc-c245-45bf-ad4a-5ac66f032fc6	Cendrillon
+43	0	58555f34-4baf-417d-b2f3-acb8952eda82	Cendrillon
+48	0	22265d9b-f1af-4739-a956-cad74ccdfece	Cendrillon
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+42	1	fae3d090-067b-4fa8-8628-9827fc3bcea2	Balthazar
+42	1	7285da98-a768-40c5-a10a-0053e38b2654	Balthazar
+20	0	7a884600-c3f1-4359-be06-a2b606aa8317	Cendrillon
+45	0	97fe3c9f-a7c8-4b77-9864-5274dfb2e2c2	Cendrillon
+26	0	f0583447-acad-40db-a558-c644289f1fe3	Cendrillon
+33	1	d0980dde-66f7-4a15-8fb5-20b46978654d	Balthazar
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+28	0	9697202d-4cff-407d-9c51-4091fb0d3ceb	Cendrillon
+54	0	67af2947-4ec5-48a0-826b-eb1a32d2e83b	Cendrillon
+34	0	f252762c-1ddf-47ef-bb59-6ae85ab693cd	Cendrillon
+52	1	faebfa45-4fae-44ab-b3d0-26558fec37ff	Balthazar
+45	0	ed7e1cc1-8183-42de-aeb4-1c3bc326da68	Cendrillon
+23	0	3a4a18a4-c453-434b-af77-302831a9cc61	Cendrillon
+56	1	58b96e81-9ae9-4f0e-a559-b436ff0f555e	Balthazar
+30	0	bdbf22bd-3917-48e9-9da9-832acd32715d	Cendrillon
+35	0	aa39acd9-ec00-4342-b354-c1e72da01b56	Cendrillon
+30	1	3688d17d-73a8-4791-b021-eb4925887f56	Balthazar
+60	1	9bc35059-3278-4a7f-8d9e-71aa718082a1	Balthazar
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+33	0	8854c7d6-c3b6-4be8-bb86-fd3a5e2d70e5	Cendrillon
+57	0	76bbdb9e-5f01-4ca3-a003-e012d002033a	Cendrillon
+40	0	e4f43f15-0a05-4687-b333-3712d6302d2b	Cendrillon
+44	0	009e689c-51a1-4cef-99ca-a4ba364eba8d	Cendrillon
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+59	1	b19019ea-366b-44b4-bdca-1546af3b987b	Balthazar
+35	0	4ca82eae-1193-43a3-9058-dbfc19f7bfc3	Cendrillon
+47	1	75fcc5b2-7e72-4ed3-98f2-f55946fa8027	Balthazar
+38	1	0d79e3e5-272e-4c2d-8be9-604b9192b26f	Balthazar
+44	1	1dea57b5-c556-4165-81f9-04495db563f5	Balthazar
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+49	0	6117ae90-0986-447a-b07d-655bdc4fc2f7	Cendrillon
+28	1	adf64426-8c61-42b6-b1f3-94b118a5b04f	Balthazar
+50	0	e52b40a3-a424-4777-bada-68c80e4e3c02	Cendrillon
+49	0	480c956e-9757-48cd-be5a-32dd588de565	Cendrillon
+34	1	a6d3e362-10f6-4abb-ad6a-2e728a9ce6aa	Balthazar
+45	0	5a5f3f5a-b6fd-4c93-ab52-e594cadce2ee	Cendrillon
+32	0	9f667859-22f2-4ff2-b1cc-08f6989b9eea	Cendrillon
+48	1	97fda318-0d6d-451a-90d9-ce1aa5463429	Balthazar
+35	1	61ca4cd0-da52-43c7-b24a-f1e9f86e3116	Balthazar
+39	0	c3a2ed64-0e2a-4166-87e3-13b23cd5e1c7	Cendrillon
+24	0	eac147d9-b569-4b33-8097-b90662b66f05	Cendrillon
+42	0	8a3417c3-2681-431e-9978-e01cf0dcf262	Cendrillon
+37	1	adacaefa-3dd1-4e55-a565-8f36de3ab538	Balthazar
+56	1	5eb2cf4d-604a-47a5-a7a0-d16cd63dfb49	Balthazar
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+34	0	964bcc4f-572e-4ac3-b65d-8c5eabccfc15	Cendrillon
+22	1	8be9ed4c-43df-4b87-81a9-707a9832a258	Balthazar
+52	1	3981fdd6-6b7c-4dd5-b3a9-f62bce926085	Balthazar
+59	0	5bc61599-fb41-485c-a053-ee94a0b2b96e	Cendrillon
+34	0	b5ff46ab-f540-4aab-95c4-74a9e2c594d1	Cendrillon
+54	1	46ada0cf-a3d2-43b1-97e1-2e9c71ddd322	Balthazar
+28	1	ca49916e-0cd2-46ac-b706-7ac87335bd30	Balthazar
+59	1	841935f4-d143-45ea-97ea-b4edce122c3e	Balthazar
+39	1	ed4eac60-f9a7-4570-8775-175d928b33f9	Balthazar
+30	0	31d6332a-fc41-49a9-b5e1-edaff61b356d	Cendrillon
+55	0	cd866d56-8d20-4070-b639-3665b3013ee4	Cendrillon
+57	0	dcd9f99c-4a6a-4263-8346-70c5f759df32	Cendrillon
+44	0	083708e7-6334-4dfd-9032-b22f89779fd0	Cendrillon
+50	1	7d30d755-7db2-42f1-b22d-1c6f6ac2f690	Balthazar
+59	1	b6a7a10d-0ee8-49f0-9e4f-13c3109cfeb8	Balthazar
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+21	0	b45d367c-ed17-407b-bf32-3e2eef298e4c	Cendrillon
+24	0	d7412f31-1c1a-4aac-ba26-98421793d3a7	Cendrillon
+41	1	cabb6c70-1134-4cf3-9f79-21045036d810	Balthazar
+36	0	9893119e-fd8a-4ef9-bf06-d87f0b737e0f	Cendrillon
+60	0	f855a24b-f61a-425b-8a74-1d7dfd9f6e7d	Cendrillon
+46	0	cfa1cebb-3bb4-4ed8-b884-6b2f8c939873	Cendrillon
+54	1	45860281-0087-4270-9329-504a680cf330	Balthazar
+55	0	a1ac498f-4f35-43d4-9a11-fb87d76e024a	Cendrillon
+55	1	274e46a7-4e81-4e6f-b3bd-64c26b93942e	Balthazar
+32	1	2cf71954-a9b5-4e27-9e20-420c500a3926	Balthazar
+55	1	e6723e5d-f3cd-41e1-9de0-a4ba460b4109	Balthazar
+27	1	3c46782f-49f7-4ea1-b970-8982a9b9de95	Balthazar
+45	0	7ceb2614-9b15-4076-889c-d60c64280c30	Cendrillon
+40	1	dc41ce33-778a-41bf-b3c9-a20b60bc1e70	Balthazar
+34	0	4cb79265-372c-4d8b-b338-97b2b78d74f5	Cendrillon
+59	0	30e40670-5bb9-41e0-a995-8f6987f1603c	Cendrillon
+33	0	cab293ba-bb8d-4a33-aa97-16f6c7419aeb	Cendrillon
+55	1	da3b255d-5b49-47f6-a42b-e7f958b80ff4	Balthazar
+30	0	a0f1d4d3-627a-4ff3-ac48-ca18466614cb	Cendrillon
+35	1	0e3e4d09-af54-4a3e-be2d-f6012b9620e4	Balthazar
+50	1	6a5a45d3-a797-4b71-bbb2-904aada658e5	Balthazar
+48	1	aa3788fc-5080-448a-b9a9-214585a562d9	Balthazar
+49	0	12501612-346b-47a0-a2bc-05ecca685027	Cendrillon
+47	0	53fa5b90-4721-4de2-8fd7-afa32dc643f3	Cendrillon
+45	0	83a22fde-0233-40fc-9279-c31998f5f610	Cendrillon
+19	0	9800415b-4944-48a8-ac7a-5e81b7cfd983	Cendrillon
+28	1	b6729ce3-42e4-41aa-8e15-2b63b70941e3	Balthazar
+32	1	38c0236c-4388-465a-9941-751027a39313	Balthazar
+59	0	f430e04b-dea0-468b-b847-8e2fc70df54c	Cendrillon
+29	1	ca82c9d2-a3dd-45f6-ad12-7a10b06c471e	Balthazar
+19	0	e17a0f3c-94d1-4db7-8ce2-cfd81bebebae	Cendrillon
+52	0	c5c6c084-5b9a-40cd-a279-d03acba74cff	Cendrillon
+56	1	ffe832d7-76ac-469f-aa34-50c8b97791e1	Balthazar
+52	1	08ac985f-5d22-481e-81bd-60fd2cc6617a	Balthazar
+41	1	0435ec61-acb1-448a-8322-1621107546a6	Balthazar
+55	0	89ab5914-f17a-47ce-a81e-7ad101a9f011	Cendrillon
+31	1	6b31a820-7a4a-415f-a961-645d5b50a903	Balthazar
+33	1	3f93f236-35bf-42aa-b031-e2441816b2fc	Balthazar
+45	0	1b10bf8a-ecab-4678-ad67-6c81ab7adce6	Cendrillon
+25	1	00a213c2-1174-4359-8a67-fe710ec1b439	Balthazar
+53	1	27af7def-6677-4bc6-8d74-f907d0cc410c	Balthazar
+35	0	79ea577c-9b02-4338-884a-626ac37dcad6	Cendrillon
+27	0	1246e024-2f70-4d3b-88a0-6ba535d2a14e	Cendrillon
+54	1	af6ba33f-a4a4-4670-a400-06209fda13a8	Balthazar
+36	0	1c2ef7e2-294d-4474-8789-f19cd97da19d	Cendrillon
+35	1	98e9b072-6cbb-4320-a66b-19611dc63c34	Balthazar
+56	1	195ffb64-31a6-4751-9c88-bc584e83a2b9	Balthazar
+40	1	2c204bd7-28d0-4543-8e8a-037901d539d2	Balthazar
+60	1	4efcd2bc-960d-4fc0-ad0b-0a1d2e22a319	Balthazar
+42	1	34d07100-913a-430e-a2af-41904ff2834c	Balthazar
+33	0	946a7c3c-3868-48af-8d94-5cae3c9e634a	Cendrillon
+50	1	75554b1c-04e8-47ab-af57-f6003b56cf24	Balthazar
+57	1	5a59b493-c47d-4f07-ae9b-0a43afabf6aa	Balthazar
+44	0	f8ae7cd5-43a3-42b8-9fec-3ea9ddebc729	Cendrillon
+45	0	6d049a1f-6327-49bb-b52d-e7892dc534b6	Cendrillon
+24	0	d81ac04f-ea06-49a5-9b9a-44c801b1d86e	Cendrillon
+46	0	9c031826-212b-45a4-9c8c-815e070a7b53	Cendrillon
+54	1	e50b7476-13bd-4293-816f-9667109f521d	Balthazar
+59	0	b0c54809-c742-4a53-87bb-9c96d81791d0	Cendrillon
+58	1	1e5fede4-d8ad-487d-b573-95c82bb11735	Balthazar
+37	0	697deb73-5643-4761-96fd-4c7dfd2d09c2	Cendrillon
+32	0	db678f9b-f887-4327-bc2b-d5ab2fa25a9e	Cendrillon
+56	1	3fd23fdc-737e-41cb-b069-90d4f84aa700	Balthazar
+27	1	c2b95ea4-f46e-42c1-a2cb-7c96a8869eae	Balthazar
+18	1	f143488f-4b94-4e85-b327-b97de7512d2e	Balthazar
+33	0	fc704080-91be-48a8-9068-74dd2516942d	Cendrillon
+51	1	9ee82e17-44ba-49aa-8832-7d1960d8cb08	Balthazar
+23	1	f1709e94-e0fa-43e1-b3a8-d89e01909276	Balthazar
+26	0	c6b9f0e6-94d2-4e9d-a2d4-6497502256a9	Cendrillon
+21	0	036ba415-d7c4-4ee9-94a7-677b10036228	Cendrillon
+21	1	2b57d7f9-6e7c-4a4f-a347-8f42c50b6b68	Balthazar
+20	0	050aff8e-0974-4888-92db-232447f7930c	Cendrillon
+58	0	0c38cda0-5186-4fef-9c14-2056b11208d1	Cendrillon
+56	1	f36ca1b1-7177-4673-876a-40ba2edc869c	Balthazar
+46	1	8d0cf959-4e11-40d7-bf69-d90d16a436ee	Balthazar
+58	1	7894a8f5-2b89-429d-b946-af1ebd64d946	Balthazar
+47	0	464938bd-a846-418f-a367-700c05da9c86	Cendrillon
+44	1	7cb4ca16-4913-4e59-b59a-0836a8fb36fb	Balthazar
+59	1	d23ccb48-4eaa-4b25-93fb-0a1ce1c4f81d	Balthazar
+26	0	0ff3d782-5c7a-4909-8559-7ba0d7baa680	Cendrillon
+49	0	dbb402e5-2f7b-4267-a69b-af95602591be	Cendrillon
+18	0	2955a876-4bef-4ba4-ac95-37ae8578b178	Cendrillon
+26	1	bf7abaae-e30d-4b3e-bf37-e8f939fbdc4a	Balthazar
+45	0	b7e7f4ab-6d74-410a-82a4-2741b93a54ab	Cendrillon
+43	0	3b4b5ca9-8bca-436b-8613-064e607f8041	Cendrillon
+28	0	38481800-558b-480c-a30f-a780abbb13e5	Cendrillon
+30	1	9abfd764-c673-4946-9c3b-d9c415b8eb45	Balthazar
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+36	1	02ceb118-24c9-4c30-9fd6-1272b5278bd3	Balthazar
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+48	1	20b6c06d-ae63-46ce-b10a-edf43799f7b5	Balthazar
+33	0	0da95a60-2a0d-4689-b834-f318eac93e57	Cendrillon
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+49	0	c7fb9c48-1fe7-48a9-b2ae-8b44f2d6ab2f	Cendrillon
+58	1	12298486-c5fe-4479-a954-e339f5110ebc	Balthazar
+18	0	506ec5a1-5c68-4305-a9ea-1427f83cc1f0	Cendrillon
+27	0	58cab8af-190d-40ad-9a82-15da58154570	Cendrillon
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+51	1	e5f78341-8e10-4c36-aca3-6ccc5d7c2d96	Balthazar
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+47	0	f824ceb1-896f-47f0-ab83-39fd3934514b	Cendrillon
+41	0	56ae6d57-22f9-4e3e-879e-1c0b3ec3f7e0	Cendrillon
+30	1	04b19c6e-aa27-4ffa-b3c3-a41f27fea341	Balthazar
+48	0	5d33015b-f64f-4a13-ac5f-b8a87e06caf9	Cendrillon
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+49	0	f6b669c3-4288-4ccb-9949-acdd7c18bdcb	Cendrillon
+20	0	ea7dc924-af78-4287-adad-088a1a54ca95	Cendrillon
+39	0	b2957c79-979b-4dc0-9ea3-4e91cc23ce6f	Cendrillon
+44	1	d00e1602-1b51-4e1a-a9c3-1a057bea6f5b	Balthazar
+26	0	e3d95fce-cbc3-4c6e-af9e-4541f4b3d09c	Cendrillon
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+44	0	7276cfac-e2da-4b05-b66f-b6c5a238badb	Cendrillon
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+31	0	482a81fb-11b3-486f-b842-3b13bd1420c7	Cendrillon
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+48	0	7588a817-9bb5-478d-97ad-7ee46ab6bfcd	Cendrillon
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+39	0	2a90c784-4f7b-4353-b67d-4c9ab0669ec6	Cendrillon
+53	1	dc5a6ce7-2d43-4b30-9dd6-d19d1d0a4ca0	Balthazar
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+57	0	184a4663-825b-4a7e-80a2-11ebd581eed5	Cendrillon
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+46	1	916e6a78-4116-43eb-b874-03b93fa3d761	Balthazar
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+23	0	80f1d6cd-6655-4e60-bdf5-461a749ecc3d	Cendrillon
+56	0	7d560630-d58f-4586-a963-c525190b8ddb	Cendrillon
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+32	1	7e5577a8-81c2-47c7-bf39-ecf7682f84e7	Balthazar
+32	1	86859f36-899b-44f4-a279-2250a6a4503e	Balthazar
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+52	1	db8c4944-daed-4b9e-8e59-b6afbdedb457	Balthazar
+33	0	7c2ee199-387a-48fd-8a86-af9775b5675e	Cendrillon
+20	0	bcf5de07-401b-4e58-ba61-05996fbdfe5d	Cendrillon
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+19	1	21bd9b8c-0834-4429-bdda-69091cbd4e2e	Balthazar
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+57	0	5ee4fd54-8a42-43b4-b25d-a8472f89dfba	Cendrillon
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+58	0	8257e1cf-10cc-4ec3-b728-b1d07535aa4f	Cendrillon
+57	0	a02d12fd-5909-40b4-970f-96be733cc541	Cendrillon
+31	1	a0387efe-2914-48cd-b1af-f209c5b7f77a	Balthazar
+49	0	87d2c2d9-4ec6-4afd-8da2-24073b7177ce	Cendrillon
+30	0	ec702424-b932-4357-a257-39c4ee14e85f	Cendrillon
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+55	0	2112a90a-d63f-4e7b-beb3-ae7d683f3d83	Cendrillon
+60	1	20feb62b-a090-41f9-a7f4-a03d6117f82f	Balthazar
+42	0	4a351527-f994-4c74-a076-113e73fd0954	Cendrillon
+25	0	1eb17b73-e09f-4458-b6d9-d9b75916915e	Cendrillon
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+60	0	5ec1c933-955d-47e8-9bb2-63c5d6f7f257	Cendrillon
+46	1	516e902c-3dac-4091-a38f-ee0d76f32049	Balthazar
+49	1	23608acc-d570-4e1f-ae6a-7b3cdd862bea	Balthazar
+33	0	f1a52bce-6d2e-43ad-b7e5-ee2dac39bcae	Cendrillon
+33	0	4e307430-cf97-4b38-83c7-2b35af33a62e	Cendrillon
+26	0	6bb5af8a-f75d-4de9-8b75-c54c1ed5946f	Cendrillon
+32	0	653d2187-b165-4cfc-a663-7e9c8a1cd0c0	Cendrillon
+51	0	878b5059-93f7-42fc-adda-e02f44343189	Cendrillon
+19	0	c50722ad-543b-4c0a-8b4c-ce40f1b459ff	Cendrillon
+46	0	9736ca20-3682-42b8-8798-89967a43aa8b	Cendrillon
+32	1	1b113448-fb99-41db-8d43-9f8c15c0bbe8	Balthazar
+37	1	d59519f1-1496-47eb-9118-e0fc02706616	Balthazar
+59	0	e54e0e41-accc-409f-af8b-1f250dd09fb1	Cendrillon
+48	0	be1d3337-ebb8-4eaa-972f-951cd550fe43	Cendrillon
+60	1	b9298c6b-c3c4-455a-84ef-171e8d7fd44a	Balthazar
+30	0	bce45ad1-5d7d-4f05-a1e0-8e964180ef8f	Cendrillon
+49	0	1f01543f-2a75-48d0-937a-6776cf750f2b	Cendrillon
+42	1	173dbe54-02ee-4ac6-9cbf-39adf148aae5	Balthazar
+44	0	de22e744-00b0-4a14-90b7-42a367026e60	Cendrillon
+52	1	c52c7ced-1931-4b7c-bb77-388d246195b2	Balthazar
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+40	0	38372b17-7152-472e-80ac-1174cc380e47	Cendrillon
+35	0	0a3da111-ec8e-4831-9312-e86dad26f1a9	Cendrillon
+40	1	9d5fc145-3b76-4602-b44c-b449ee94e541	Balthazar
+18	1	48463563-9f95-4e03-9f95-23b38c53f798	Balthazar
+59	0	ff8409b8-b05f-4e7f-9bdc-f7a60abf8f4e	Cendrillon
+47	0	ec94a11e-42c0-4241-9ca4-666591905e87	Cendrillon
+50	0	a763e9c6-e6cc-4519-8543-99c6f36c2377	Cendrillon
+25	0	4e8dbd79-5a0c-4ecf-83f1-68abc7bbc209	Cendrillon
+60	1	15856ec0-ca86-49fd-a61d-abae473e3beb	Balthazar
+26	0	046a2341-08b8-4504-99d3-8d08523cd933	Cendrillon
+58	0	1efdad8c-7dd5-466b-80eb-96ad46475b4d	Cendrillon
+24	1	ee8a49fb-db60-4697-a816-0127fc24370c	Balthazar
+29	1	79295611-9053-4fba-b5ac-a55d02c8dc71	Balthazar
+32	0	4115e10c-fa80-4467-ad86-8c249bc987c5	Cendrillon
+50	0	bc0acee8-9d81-4d3d-a41a-38d56316c71d	Cendrillon
+35	1	8d3e20c2-1935-4150-b813-a8b56f934827	Balthazar
+36	0	c6ba6023-4b4c-4ac3-a416-bf8f8c8005ef	Cendrillon
+32	0	b2f317b2-cc26-45b9-b5db-aee9922d4f3a	Cendrillon
+54	0	68c6ad61-1948-4e36-8a4c-0e67a5a10484	Cendrillon
+60	0	67245b2a-3953-48d2-b085-4be2454ea402	Cendrillon
+58	1	71e78da4-04bb-4cad-8cda-dddb00a84c9f	Balthazar
+60	1	37781d41-18c4-4968-a445-91b7b1a89ec4	Balthazar
+47	0	c64407ab-7d52-42ce-9c12-65d0911553e1	Cendrillon
+58	1	bfb6d558-c0c3-4cf7-b3d5-3303044c6f06	Balthazar
+59	0	94526af8-0e78-4705-9995-f6b41670f396	Cendrillon
+37	0	398ca7a9-7991-4753-931f-2f8032fb6cb4	Cendrillon
+26	0	9bea399c-f121-451f-ab4d-60893730f5e1	Cendrillon
+36	0	d64c5d64-d694-4f53-afb3-428169076dab	Cendrillon
+49	0	29cca253-9c23-4324-979e-b65ee1199b50	Cendrillon
+34	0	ba965838-aa88-4227-b74e-df9c0e14b0fb	Cendrillon
+55	0	0cd5b95c-8709-4e53-bb06-a4b74d1cc0b4	Cendrillon
+50	1	705af513-a50b-470a-b197-2c58e6f72c48	Balthazar
+41	1	d0a752b3-e610-4fd2-b383-f9a4e712d49e	Balthazar
+40	1	f81bf65d-7527-417f-9de6-996a93a5ed2f	Balthazar
+39	1	4cfc4591-51e3-4634-b6ad-fe2f238abf56	Balthazar
+42	1	833ec0e1-3815-4688-8697-724cf8bad309	Balthazar
+21	0	5140d2d1-6886-44ad-ba39-631c9d5d1626	Cendrillon
+48	1	1148b354-3ed0-4e8f-9eb6-e00f9976faa6	Balthazar
+39	1	479befb2-2bba-41f3-81bf-4c371430ea92	Balthazar
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+45	0	43dbe685-ec38-4745-b77f-cf1f05a52087	Cendrillon
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+52	0	ba57ac08-f05e-4c3a-98ce-22a1abfab295	Cendrillon
diff --git a/_data/2017/rendezvous.txt b/_data/2017/rendezvous.txt
new file mode 100644
index 00000000..9c330d53
--- /dev/null
+++ b/_data/2017/rendezvous.txt
@@ -0,0 +1,2538 @@
+date	idc	idr	price
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diff --git a/_doc/articles/2024/2024-03-01-route2024.rst b/_doc/articles/2024/2024-03-01-route2024.rst
index 74d04c5f..2eb56124 100644
--- a/_doc/articles/2024/2024-03-01-route2024.rst
+++ b/_doc/articles/2024/2024-03-01-route2024.rst
@@ -1,4 +1,4 @@
-_l-feuille-route-2024:
+.. _l-feuille-route-2024:
 
 ==================================
 2024-03-01 : feuille de route 2024
@@ -11,9 +11,11 @@ Séance 1 (26/1)
 * :ref:`l-ml-rappel`
 * programmation (python :epkg:`numpy`, :epkg:`pandas`, :epkg:`matplotlib`, :epkg:`jupyter`)
 * :ref:`Tests unitaires <nbl-practice-py-base-tests_unitaires>`, package python
-* CPU, CUDA
-* données, SQL
+* `SQL <https://en.wikipedia.org/wiki/SQL>`_
+* `CPU <https://en.wikipedia.org/wiki/Central_processing_unit>`_,
+  `CUDA <https://en.wikipedia.org/wiki/CUDA>`_
 * machine learning, :epkg:`scikit-learn`, :epkg:`pytorch`
+* `comparaison torch/scikit-learn <https://sdpython.github.io/doc/experimental-experiment/dev/auto_examples/plot_torch_linreg.html>`_
 * :ref:`l-regclass`
 * évaluation, :ref:`ROC <l-ml-plot-roc>`, :math:`R^2`
 * ranking, clustering
diff --git a/_doc/articles/index.rst b/_doc/articles/index.rst
index 4225aa3d..5f51f0c7 100644
--- a/_doc/articles/index.rst
+++ b/_doc/articles/index.rst
@@ -9,8 +9,8 @@ Ou *blog*.
     :caption: 2024
     :maxdepth: 1
 
-    2024/2024-01-18-wsl
     2024/2024-03-01-route2024
+    2024/2024-01-18-wsl
 
 .. toctree::
     :caption: 2023
@@ -25,6 +25,6 @@ Ou *blog*.
     :caption: 2022
     :maxdepth: 1
 
-    2022/2022-11-31-route2022
     2022/2022-12-07-cartopy
+    2022/2022-11-31-route2022
     2022/2022-01-01-assurance
diff --git a/_doc/c_ml/regclass.rst b/_doc/c_ml/regclass.rst
index f888fe09..4432c493 100644
--- a/_doc/c_ml/regclass.rst
+++ b/_doc/c_ml/regclass.rst
@@ -566,8 +566,8 @@ un modèle.
 Exercices
 +++++++++
 
-* `Tree, overfitting <http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/ml_a_tree_overfitting.html>`_
-* `Comparaison de deux régressions <http://www.xavierdupre.fr/app/actuariat_python/helpsphinx/notebooks/enonce_2017.html#enonce2017rst>`_
+* :ref:`Tree, overfitting <nbl-practice-ml-ml_a_tree_overfitting>`
+* :ref:`Comparaison de deux régressions <nbl-practice-exams-enonce_ml_2017>`
 
 Bibliographie
 +++++++++++++
diff --git a/_doc/notebook_gallery.rst b/_doc/notebook_gallery.rst
index 6b27f9fd..a47dbbee 100644
--- a/_doc/notebook_gallery.rst
+++ b/_doc/notebook_gallery.rst
@@ -140,6 +140,7 @@ Correction d'examens
     practice/exams/td_note_2015
     practice/exams/td_note_2016
     practice/exams/td_note_2017
+    practice/exams/enonce_ml_2017_correction
     practice/exams/td_note_2017_2
     practice/exams/td_note_2018_1
     practice/exams/td_note_2018_2
@@ -176,3 +177,4 @@ Machine Learning
     practice/ml/artificiel_multiclass
     practice/ml/ml_features_model
     practice/ml/timeseries_ssa
+    practice/ml/ml_a_tree_overfitting
diff --git a/_doc/practice/algo-compose/exercice_morse.ipynb b/_doc/practice/algo-compose/exercice_morse.ipynb
index 159e7030..f2ec961d 100644
--- a/_doc/practice/algo-compose/exercice_morse.ipynb
+++ b/_doc/practice/algo-compose/exercice_morse.ipynb
@@ -57,7 +57,7 @@
    "source": [
     "### Exercice 1 : Traduire un texte Morse qui ne contient pas d'espace\n",
     "\n",
-    "Ce sujet est un exercice classique de programmation. Il est déjà résolu et expliqué sur [Codingame](http://www.synbioz.com/blog/exercice_de_programmation_codingame). Mais on pourra par exemple commencer par utiliser une expression régulière. Une autre option consiste à utiliser un *trie*."
+    "Ce sujet est un exercice classique de programmation. On peut trouver des solution sur internet. On pourra par exemple commencer par utiliser une expression régulière. Une autre option consiste à utiliser un *trie*."
    ]
   },
   {
diff --git a/_doc/practice/exams.rst b/_doc/practice/exams.rst
index 0807b65e..0542073c 100644
--- a/_doc/practice/exams.rst
+++ b/_doc/practice/exams.rst
@@ -75,3 +75,12 @@ Exercices courts
     exams/interro_rapide_20_minutes_2014_12
     exams/interro_rapide_20_minutes_2015_09
     exams/interro_rapide_20_minutes_2015_11
+
+Séance notées machaine learning
+===============================
+
+.. toctree::
+    :maxdepth: 1
+
+    exams/enonce_ml_2017
+    exams/enonce_ml_2017_correction
diff --git a/_doc/practice/exams/enonce_ml_2017.ipynb b/_doc/practice/exams/enonce_ml_2017.ipynb
new file mode 100644
index 00000000..9c217e9e
--- /dev/null
+++ b/_doc/practice/exams/enonce_ml_2017.ipynb
@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation Python / Machine Learning année 2017 - énoncé\n",
+    "\n",
+    "Le répertoire [_data/2017](https://github.com/sdpython/teachpyx/tree/main/_data/2017) contient deux fichiers csv simulés aléatoirement dont il faudra se servir pour répondre aux 10 questions qui suivent. Chaque question vaut deux points. Le travail est à rendre pour le lundi 20 février sous la forme d'un notebook envoyé en pièce jointe d'un mail."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## 1\n",
+    "\n",
+    "Deux fichiers sont extraits de la base de données d'un médecin.\n",
+    "Un fichier contient des informations sur des personnes, un autre\n",
+    "sur les rendez-vous pris par ces personnes. Quels sont-ils ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2\n",
+    "\n",
+    "On souhaite étudier la relation entre le prix moyen payé par une personne,\n",
+    "son âge et son genre. Calculer le prix moyen payé par une personne ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3\n",
+    "\n",
+    "Faire la jointure entre les deux tables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4\n",
+    "\n",
+    "Tracer deux nuages de points (age, prix moyen) et (genre, prix moyen) ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5\n",
+    "\n",
+    "Calculer les coefficients de la régression $prix\\_moyen \\sim age + genre$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6\n",
+    "\n",
+    "On souhaite étudier le prix d'une consultation en fonction du jour de la semaine.\n",
+    "Ajouter une colonne dans la table de votre choix avec le jour de la semaine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7\n",
+    "\n",
+    "Créer un graphe moustache qui permet de vérifier cette hypothèse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8\n",
+    "\n",
+    "Ajouter une colonne dans la table de votre choix qui contient 365 si c'est le premier rendez-vous, le nombre de jour écoulés depuis le précédent rendez-vous. On appelle cette colonne $delay$. On ajoute également la colonne $1/delay$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9 \n",
+    "\n",
+    "Calculer les coefficients de la régression $prix \\sim age + genre + delay + 1/delay + jour\\_semaine$.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10\n",
+    "\n",
+    "Comment comparer ce modèle avec le précédent ? Implémentez le calcul qui vous permet de répondre à cette question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_doc/practice/exams/enonce_ml_2017_correction.ipynb b/_doc/practice/exams/enonce_ml_2017_correction.ipynb
new file mode 100644
index 00000000..e38bfd91
--- /dev/null
+++ b/_doc/practice/exams/enonce_ml_2017_correction.ipynb
@@ -0,0 +1,6155 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Evaluation Python / Machine Learning année 2017 - solution\n",
+    "\n",
+    "Le répertoire [_data/2017](https://github.com/sdpython/teachpyx/tree/main/_data/2017) contient deux fichiers csv simulés aléatoirement dont il faudra se servir pour répondre aux 10 questions qui suivent. Chaque question vaut deux points. Le travail est à rendre pour le lundi 20 février sous la forme d'un notebook envoyé en pièce jointe d'un mail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 1\n",
+    "\n",
+    "*Deux fichiers sont extraits de la base de données d'un médecin. Un fichier contient des informations sur des personnes, un autre sur les rendez-vous pris par ces personnes. Quels sont-ils ?*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>Cendrillon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>Cendrillon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>46</td>\n",
+       "      <td>1</td>\n",
+       "      <td>304895f0-f686-4b0e-8854-a705bb5a6982</td>\n",
+       "      <td>Balthazar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>42</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3f0d31d2-0ef4-4e7e-b876-07d10225cc8c</td>\n",
+       "      <td>Balthazar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>f29273f4-a76c-4158-b5f5-b3e5a080a0c7</td>\n",
+       "      <td>Balthazar</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  gender                                   idc        name\n",
+       "0   37       0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  Cendrillon\n",
+       "1   41       0  f44b004b-b01e-4835-b86d-1a42846c6d93  Cendrillon\n",
+       "2   46       1  304895f0-f686-4b0e-8854-a705bb5a6982   Balthazar\n",
+       "3   42       1  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c   Balthazar\n",
+       "4   41       1  f29273f4-a76c-4158-b5f5-b3e5a080a0c7   Balthazar"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "\n",
+    "persons = pandas.read_csv(\n",
+    "    \"https://raw.githubusercontent.com/sdpython/teachpyx/main/_data/2017/persons.txt\",\n",
+    "    sep=\"\\t\",\n",
+    ")\n",
+    "persons.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-12-07 19:47:45.068274</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>644b1236-b9ee-4ef5-8ca7-d1adadb547c8</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-12-17 19:47:45.068274</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>aff6ac9e-5dd0-434e-9888-f724f6d40969</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2017-01-07 19:47:45.068274</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>9ca87507-aa95-49a9-88b3-86ec9fbc44d6</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date                                   idc  \\\n",
+       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "2  2016-12-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "3  2016-12-17 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "4  2017-01-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "\n",
+       "                                    idr  price  \n",
+       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0  \n",
+       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0  \n",
+       "2  644b1236-b9ee-4ef5-8ca7-d1adadb547c8   75.0  \n",
+       "3  aff6ac9e-5dd0-434e-9888-f724f6d40969   80.0  \n",
+       "4  9ca87507-aa95-49a9-88b3-86ec9fbc44d6   80.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rend = pandas.read_csv(\n",
+    "    \"https://raw.githubusercontent.com/sdpython/teachpyx/main/_data/2017/rendezvous.txt\",\n",
+    "    sep=\"\\t\",\n",
+    ")\n",
+    "rend.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((1000, 4), (2537, 4))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "persons.shape, rend.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Le second fichier est plus volumineux et contient une variable *price* qui ne peut pas être reliée aux personnes. Le premier fichier est celui des personnes, le second celui des rendez-vous. La variable *idc* est présente dans les deux tables. C'est elle qui identifie les personnes dans les deux tables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 2\n",
+    "\n",
+    "*On souhaite étudier la relation entre le prix moyen payé par une personne, son âge et son genre. Calculer le prix moyen payé par une personne ?*\n",
+    "\n",
+    "La table des rendez-vous contient toutes l'information nécessaire. La question était un peu ambiguë. On peut déterminer le prix moyen payé par personne, ou le prix moyen des prix moyens... On va répondre à la première option car la seconde n'a pas beaucoup d'intérêt et c'est très proche du prix moyen par rendez-vous, ce que la question aurait sans doute formulé dans ce sens si telle avait été son intention. On groupe par *idc* et on fait la moyenne."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>idc</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>003b0195-2acb-4f46-b7fa-28cf266a8f60</th>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>009e689c-51a1-4cef-99ca-a4ba364eba8d</th>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00a213c2-1174-4359-8a67-fe710ec1b439</th>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00e42818-aade-4758-a5f6-c78a6f235ea5</th>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0153b785-9acd-4d28-aad1-62f8bf2faea3</th>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      price\n",
+       "idc                                        \n",
+       "003b0195-2acb-4f46-b7fa-28cf266a8f60   80.0\n",
+       "009e689c-51a1-4cef-99ca-a4ba364eba8d   80.0\n",
+       "00a213c2-1174-4359-8a67-fe710ec1b439   70.0\n",
+       "00e42818-aade-4758-a5f6-c78a6f235ea5   70.0\n",
+       "0153b785-9acd-4d28-aad1-62f8bf2faea3   75.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gr = rend[[\"idc\", \"price\"]].groupby(\"idc\").mean()\n",
+    "gr.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 3\n",
+    "\n",
+    "*Faire la jointure entre les deux tables.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>name</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>Cendrillon</td>\n",
+       "      <td>70.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>Cendrillon</td>\n",
+       "      <td>78.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>46</td>\n",
+       "      <td>1</td>\n",
+       "      <td>304895f0-f686-4b0e-8854-a705bb5a6982</td>\n",
+       "      <td>Balthazar</td>\n",
+       "      <td>75.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>42</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3f0d31d2-0ef4-4e7e-b876-07d10225cc8c</td>\n",
+       "      <td>Balthazar</td>\n",
+       "      <td>95.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>f29273f4-a76c-4158-b5f5-b3e5a080a0c7</td>\n",
+       "      <td>Balthazar</td>\n",
+       "      <td>90.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  gender                                   idc        name      price\n",
+       "0   37       0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  Cendrillon  70.000000\n",
+       "1   41       0  f44b004b-b01e-4835-b86d-1a42846c6d93  Cendrillon  78.333333\n",
+       "2   46       1  304895f0-f686-4b0e-8854-a705bb5a6982   Balthazar  75.000000\n",
+       "3   42       1  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c   Balthazar  95.000000\n",
+       "4   41       1  f29273f4-a76c-4158-b5f5-b3e5a080a0c7   Balthazar  90.000000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "join = persons.merge(gr.reset_index(), on=\"idc\")\n",
+    "join.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Cette jointure est assez simple puisque la colonne partagée porte le même nom dans les deux tables. On peut néanmoins se poser la question de savoir s'il y a des personnes qui n'ont pas de rendez-vous associé et réciproquement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 5)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "join.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 5)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "join = persons.merge(gr.reset_index(), on=\"idc\", how=\"outer\")\n",
+    "join.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Visiblement, ce n'est pas le cas puisqu'une jointure incluant les éléments sans correspondances dans les deux tables n'ajoute pas plus d'éléments à la jointure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 4\n",
+    "\n",
+    "*Tracer deux nuages de points (age, prix moyen) et (genre, prix moyen) ?*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'age / price'}, xlabel='age', ylabel='price'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "join.plot(x=\"age\", y=\"price\", kind=\"scatter\", title=\"age / price\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On peut aussi utiliser un module comme [seaborn](http://seaborn.pydata.org/) qui propose des dessins intéressants pour un statisticatien."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn\n",
+    "\n",
+    "g = seaborn.jointplot(join, x=\"age\", y=\"price\", kind=\"reg\", scatter_kws={\"s\": 10})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='gender', ylabel='price'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "join.plot(x=\"gender\", y=\"price\", kind=\"scatter\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On ne voit pas grand chose sur ce second graphe. Une option est d'ajouter un bruit aléatoire sur le genre pour éclater le nuage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='gx', ylabel='price'>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "\n",
+    "bruit = join.copy()\n",
+    "bruit[\"gx\"] = bruit.gender + numpy.random.random(bruit.shape[0]) / 3\n",
+    "bruit.plot(x=\"gx\", y=\"price\", kind=\"scatter\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Il n'y a rien de flagrant. On peut faire un graphe moustache."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'price'}, xlabel='[gender]'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "join[[\"price\", \"gender\"]].boxplot(by=\"gender\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "C'est mieux. Un dernier. Le diagramme violon, plus complet que le précédent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='gender', ylabel='price'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seaborn.violinplot(x=\"gender\", y=\"price\", data=join, inner=\"quart\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 5\n",
+    "\n",
+    "*Calculer les coefficients de la régression* $prix\\_moyen \\sim age + genre$.\n",
+    "\n",
+    "Une régression. Le premier réflexe est [scikit-learn](https://scikit-learn.org/stable/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "lr = LinearRegression()\n",
+    "lr.fit(join[[\"age\", \"gender\"]], join[\"price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.52440734, -4.36373925])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.05057671902867"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On utilise maintenant [statsmodels](http://statsmodels.sourceforge.net/) qui est plus complet pour toute ce qui est un modèle linéaire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.579</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.579</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   686.6</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th> <td>3.25e-188</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:12:30</td>     <th>  Log-Likelihood:    </th> <td> -3149.8</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   6306.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   997</td>      <th>  BIC:               </th> <td>   6320.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>   61.0506</td> <td>    0.640</td> <td>   95.432</td> <td> 0.000</td> <td>   59.795</td> <td>   62.306</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>age</th>       <td>    0.5244</td> <td>    0.015</td> <td>   35.302</td> <td> 0.000</td> <td>    0.495</td> <td>    0.554</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gender</th>    <td>   -4.3637</td> <td>    0.358</td> <td>  -12.194</td> <td> 0.000</td> <td>   -5.066</td> <td>   -3.662</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>300.096</td> <th>  Durbin-Watson:     </th> <td>   1.999</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 719.506</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 1.618</td>  <th>  Prob(JB):          </th> <td>5.77e-157</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 5.608</td>  <th>  Cond. No.          </th> <td>    151.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.579   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.579   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     686.6   \\\\\n",
+       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} & 3.25e-188   \\\\\n",
+       "\\textbf{Time:}             &     11:12:30     & \\textbf{  Log-Likelihood:    } &   -3149.8   \\\\\n",
+       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               } &     6306.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         997      & \\textbf{  BIC:               } &     6320.   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept} &      61.0506  &        0.640     &    95.432  &         0.000        &       59.795    &       62.306     \\\\\n",
+       "\\textbf{age}       &       0.5244  &        0.015     &    35.302  &         0.000        &        0.495    &        0.554     \\\\\n",
+       "\\textbf{gender}    &      -4.3637  &        0.358     &   -12.194  &         0.000        &       -5.066    &       -3.662     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 300.096 & \\textbf{  Durbin-Watson:     } &     1.999  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   719.506  \\\\\n",
+       "\\textbf{Skew:}          &   1.618 & \\textbf{  Prob(JB):          } & 5.77e-157  \\\\\n",
+       "\\textbf{Kurtosis:}      &   5.608 & \\textbf{  Cond. No.          } &      151.  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.579\n",
+       "Model:                            OLS   Adj. R-squared:                  0.579\n",
+       "Method:                 Least Squares   F-statistic:                     686.6\n",
+       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):          3.25e-188\n",
+       "Time:                        11:12:30   Log-Likelihood:                -3149.8\n",
+       "No. Observations:                1000   AIC:                             6306.\n",
+       "Df Residuals:                     997   BIC:                             6320.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept     61.0506      0.640     95.432      0.000      59.795      62.306\n",
+       "age            0.5244      0.015     35.302      0.000       0.495       0.554\n",
+       "gender        -4.3637      0.358    -12.194      0.000      -5.066      -3.662\n",
+       "==============================================================================\n",
+       "Omnibus:                      300.096   Durbin-Watson:                   1.999\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              719.506\n",
+       "Skew:                           1.618   Prob(JB):                    5.77e-157\n",
+       "Kurtosis:                       5.608   Cond. No.                         151.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.formula.api import ols\n",
+    "\n",
+    "lr = ols(\"price ~ age + gender\", data=join)\n",
+    "res = lr.fit()\n",
+    "res.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ou encore (après avoir ajouté une constante)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.579</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.579</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   686.6</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th> <td>3.25e-188</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:12:33</td>     <th>  Log-Likelihood:    </th> <td> -3149.8</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   6306.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   997</td>      <th>  BIC:               </th> <td>   6320.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>age</th>    <td>    0.5244</td> <td>    0.015</td> <td>   35.302</td> <td> 0.000</td> <td>    0.495</td> <td>    0.554</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gender</th> <td>   -4.3637</td> <td>    0.358</td> <td>  -12.194</td> <td> 0.000</td> <td>   -5.066</td> <td>   -3.662</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>cst</th>    <td>   61.0506</td> <td>    0.640</td> <td>   95.432</td> <td> 0.000</td> <td>   59.795</td> <td>   62.306</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>300.096</td> <th>  Durbin-Watson:     </th> <td>   1.999</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 719.506</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 1.618</td>  <th>  Prob(JB):          </th> <td>5.77e-157</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 5.608</td>  <th>  Cond. No.          </th> <td>    151.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.579   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.579   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     686.6   \\\\\n",
+       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} & 3.25e-188   \\\\\n",
+       "\\textbf{Time:}             &     11:12:33     & \\textbf{  Log-Likelihood:    } &   -3149.8   \\\\\n",
+       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               } &     6306.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         997      & \\textbf{  BIC:               } &     6320.   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{age}    &       0.5244  &        0.015     &    35.302  &         0.000        &        0.495    &        0.554     \\\\\n",
+       "\\textbf{gender} &      -4.3637  &        0.358     &   -12.194  &         0.000        &       -5.066    &       -3.662     \\\\\n",
+       "\\textbf{cst}    &      61.0506  &        0.640     &    95.432  &         0.000        &       59.795    &       62.306     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 300.096 & \\textbf{  Durbin-Watson:     } &     1.999  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   719.506  \\\\\n",
+       "\\textbf{Skew:}          &   1.618 & \\textbf{  Prob(JB):          } & 5.77e-157  \\\\\n",
+       "\\textbf{Kurtosis:}      &   5.608 & \\textbf{  Cond. No.          } &      151.  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.579\n",
+       "Model:                            OLS   Adj. R-squared:                  0.579\n",
+       "Method:                 Least Squares   F-statistic:                     686.6\n",
+       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):          3.25e-188\n",
+       "Time:                        11:12:33   Log-Likelihood:                -3149.8\n",
+       "No. Observations:                1000   AIC:                             6306.\n",
+       "Df Residuals:                     997   BIC:                             6320.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "age            0.5244      0.015     35.302      0.000       0.495       0.554\n",
+       "gender        -4.3637      0.358    -12.194      0.000      -5.066      -3.662\n",
+       "cst           61.0506      0.640     95.432      0.000      59.795      62.306\n",
+       "==============================================================================\n",
+       "Omnibus:                      300.096   Durbin-Watson:                   1.999\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              719.506\n",
+       "Skew:                           1.618   Prob(JB):                    5.77e-157\n",
+       "Kurtosis:                       5.608   Cond. No.                         151.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.api import OLS\n",
+    "\n",
+    "join2 = join.copy()\n",
+    "join2[\"cst\"] = 1\n",
+    "lr = OLS(join2[\"price\"], join2[[\"age\", \"gender\", \"cst\"]])\n",
+    "res = lr.fit()\n",
+    "res.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On peut aussi définir la régression sous la forme de formule avec le module [patsy](http://patsy.readthedocs.io/en/latest/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression(fit_intercept=False)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression(fit_intercept=False)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from patsy import dmatrices\n",
+    "\n",
+    "y, X = dmatrices(\"price ~ age + gender\", join, return_type=\"matrix\")\n",
+    "y = numpy.ravel(y)\n",
+    "lr = LinearRegression(fit_intercept=False)\n",
+    "lr.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([61.05057672,  0.52440734, -4.36373925]), 0.0)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.coef_, lr.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1., 37.,  0.],\n",
+       "       [ 1., 41.,  0.]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 6\n",
+    "\n",
+    "*On souhaite étudier le prix d'une consultation en fonction du jour de la semaine. Ajouter une colonne dans la table de votre choix avec le jour de la semaine.*\n",
+    "\n",
+    "On convertit d'abord la colonne date (chaîne de caractères au format date) avec la fonction [to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date             object\n",
+       "idc              object\n",
+       "idr              object\n",
+       "price           float64\n",
+       "date2    datetime64[ns]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rend[\"date2\"] = pandas.to_datetime(rend.date)\n",
+    "rend.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Et on récupère le jour de la semaine avec [weekday](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.weekday.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>price</th>\n",
+       "      <th>date2</th>\n",
+       "      <th>weekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-12-07 19:47:45.068274</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>644b1236-b9ee-4ef5-8ca7-d1adadb547c8</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2016-12-07 19:47:45.068274</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-12-17 19:47:45.068274</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>aff6ac9e-5dd0-434e-9888-f724f6d40969</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2016-12-17 19:47:45.068274</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2017-01-07 19:47:45.068274</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>9ca87507-aa95-49a9-88b3-86ec9fbc44d6</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2017-01-07 19:47:45.068274</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date                                   idc  \\\n",
+       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "2  2016-12-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "3  2016-12-17 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "4  2017-01-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "\n",
+       "                                    idr  price                      date2  \\\n",
+       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
+       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
+       "2  644b1236-b9ee-4ef5-8ca7-d1adadb547c8   75.0 2016-12-07 19:47:45.068274   \n",
+       "3  aff6ac9e-5dd0-434e-9888-f724f6d40969   80.0 2016-12-17 19:47:45.068274   \n",
+       "4  9ca87507-aa95-49a9-88b3-86ec9fbc44d6   80.0 2017-01-07 19:47:45.068274   \n",
+       "\n",
+       "   weekday  \n",
+       "0        4  \n",
+       "1        4  \n",
+       "2        2  \n",
+       "3        5  \n",
+       "4        5  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rend[\"weekday\"] = rend.date2.dt.weekday\n",
+    "rend.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 7\n",
+    "\n",
+    "*Créer un graphe moustache qui permet de vérifier cette hypothèse.*\n",
+    "\n",
+    "On réutilise le code d'une question précédente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'price'}, xlabel='[weekday]'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rend[[\"price\", \"weekday\"]].boxplot(by=\"weekday\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "C'est clairement plus cher le dimanche."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 8\n",
+    "\n",
+    "*Ajouter une colonne dans la table de votre choix qui contient 365 si c'est le premier rendez-vous, le nombre de jour écoulés depuis le précédent rendez-vous. On appelle cette colonne $delay$. On ajoute également la colonne* $1/delay$.\n",
+    "\n",
+    "Pour commencer, on convertit la date en nombre de jours depuis la première date."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>price</th>\n",
+       "      <th>date2</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>constant</th>\n",
+       "      <th>jour</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date                                   idc  \\\n",
+       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "\n",
+       "                                    idr  price                      date2  \\\n",
+       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
+       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
+       "\n",
+       "   weekday                   constant    jour  \n",
+       "0        4 2016-11-18 19:47:45.068274 14 days  \n",
+       "1        4 2016-11-18 19:47:45.068274 14 days  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rend[\"constant\"] = rend[\"date2\"].min()\n",
+    "rend[\"jour\"] = rend[\"date2\"] - rend[\"constant\"]\n",
+    "rend.head(n=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On convertit en entier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>price</th>\n",
+       "      <th>date2</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>constant</th>\n",
+       "      <th>jour</th>\n",
+       "      <th>jouri</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date                                   idc  \\\n",
+       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "\n",
+       "                                    idr  price                      date2  \\\n",
+       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
+       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
+       "\n",
+       "   weekday                   constant    jour  jouri  \n",
+       "0        4 2016-11-18 19:47:45.068274 14 days     14  \n",
+       "1        4 2016-11-18 19:47:45.068274 14 days     14  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rend[\"jouri\"] = rend.jour.apply(lambda d: d.days)\n",
+    "rend.head(n=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On trie par patient et jour puis on effectue la différence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>price</th>\n",
+       "      <th>date2</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>constant</th>\n",
+       "      <th>jour</th>\n",
+       "      <th>jouri</th>\n",
+       "      <th>diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "      <td>14</td>\n",
+       "      <td>-26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date                                   idc  \\\n",
+       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "\n",
+       "                                    idr  price                      date2  \\\n",
+       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
+       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
+       "\n",
+       "   weekday                   constant    jour  jouri  diff  \n",
+       "0        4 2016-11-18 19:47:45.068274 14 days     14 -26.0  \n",
+       "1        4 2016-11-18 19:47:45.068274 14 days     14   0.0  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff = rend.sort_values([\"idc\", \"jouri\"])[\"jouri\"].diff()\n",
+    "rend[\"diff\"] = diff\n",
+    "rend.head(n=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Il reste à traiter le premier jour ou plutôt le premier rendez-vous. On le récupère pour chaque patient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idc</th>\n",
+       "      <th>date</th>\n",
+       "      <th>j365</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>003b0195-2acb-4f46-b7fa-28cf266a8f60</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>009e689c-51a1-4cef-99ca-a4ba364eba8d</td>\n",
+       "      <td>2016-11-26 19:47:45.068274</td>\n",
+       "      <td>365</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    idc                        date  j365\n",
+       "0  003b0195-2acb-4f46-b7fa-28cf266a8f60  2016-12-02 19:47:45.068274   365\n",
+       "1  009e689c-51a1-4cef-99ca-a4ba364eba8d  2016-11-26 19:47:45.068274   365"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first = rend[[\"idc\", \"date\"]].groupby(\"idc\", as_index=False).min()\n",
+    "first[\"j365\"] = 365\n",
+    "first.head(n=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Puis on fait une jointure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idc</th>\n",
+       "      <th>jouri</th>\n",
+       "      <th>date</th>\n",
+       "      <th>j365</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2016-12-07 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2016-12-17 19:47:45.068274</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>50</td>\n",
+       "      <td>2017-01-07 19:47:45.068274</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    idc  jouri                        date  \\\n",
+       "0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
+       "1  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
+       "2  f44b004b-b01e-4835-b86d-1a42846c6d93     19  2016-12-07 19:47:45.068274   \n",
+       "3  f44b004b-b01e-4835-b86d-1a42846c6d93     29  2016-12-17 19:47:45.068274   \n",
+       "4  f44b004b-b01e-4835-b86d-1a42846c6d93     50  2017-01-07 19:47:45.068274   \n",
+       "\n",
+       "    j365  \n",
+       "0  365.0  \n",
+       "1  365.0  \n",
+       "2  365.0  \n",
+       "3    NaN  \n",
+       "4    NaN  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tout = rend.merge(first, on=[\"idc\", \"date\"], how=\"outer\")\n",
+    "tout[[\"idc\", \"jouri\", \"date\", \"j365\"]].head(n=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Il ne reste plus qu'à remplacer les NaN par *jouri*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idc</th>\n",
+       "      <th>jouri</th>\n",
+       "      <th>date</th>\n",
+       "      <th>j365</th>\n",
+       "      <th>delay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2016-12-07 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2016-12-17 19:47:45.068274</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>50</td>\n",
+       "      <td>2017-01-07 19:47:45.068274</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>304895f0-f686-4b0e-8854-a705bb5a6982</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2016-11-19 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3f0d31d2-0ef4-4e7e-b876-07d10225cc8c</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2016-12-18 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>f29273f4-a76c-4158-b5f5-b3e5a080a0c7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2016-11-20 19:47:45.068274</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    idc  jouri                        date  \\\n",
+       "0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
+       "1  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
+       "2  f44b004b-b01e-4835-b86d-1a42846c6d93     19  2016-12-07 19:47:45.068274   \n",
+       "3  f44b004b-b01e-4835-b86d-1a42846c6d93     29  2016-12-17 19:47:45.068274   \n",
+       "4  f44b004b-b01e-4835-b86d-1a42846c6d93     50  2017-01-07 19:47:45.068274   \n",
+       "5  304895f0-f686-4b0e-8854-a705bb5a6982      1  2016-11-19 19:47:45.068274   \n",
+       "6  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c     30  2016-12-18 19:47:45.068274   \n",
+       "7  f29273f4-a76c-4158-b5f5-b3e5a080a0c7      2  2016-11-20 19:47:45.068274   \n",
+       "\n",
+       "    j365  delay  \n",
+       "0  365.0  365.0  \n",
+       "1  365.0  365.0  \n",
+       "2  365.0  365.0  \n",
+       "3    NaN   29.0  \n",
+       "4    NaN   50.0  \n",
+       "5  365.0  365.0  \n",
+       "6  365.0  365.0  \n",
+       "7  365.0  365.0  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tout[\"delay\"] = tout.j365.fillna(tout.jouri)\n",
+    "tout[[\"idc\", \"jouri\", \"date\", \"j365\", \"delay\"]].head(n=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Finalement, il faut ajouter une colonne $1/delay$. Comme des patients ont parfois deux rendez-vous le même jour, pour éviter les valeurs nulles, on ajoute la colonne $1/(1+delay)$. On aurait pu également pour éviter les valeurs nulles considérer le temps en secondes et non en jour entre deux rendez-vous. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>delay</th>\n",
+       "      <th>delay1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.033333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.019608</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   delay    delay1\n",
+       "0  365.0  0.002732\n",
+       "1  365.0  0.002732\n",
+       "2  365.0  0.002732\n",
+       "3   29.0  0.033333\n",
+       "4   50.0  0.019608"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tout[\"delay1\"] = 1 / (tout[\"delay\"] + 1)\n",
+    "tout[[\"delay\", \"delay1\"]].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8 - réponse plus courte\n",
+    "\n",
+    "On garde l'idée de la fonction [diff](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.diff.html) et on ajoute la fonction [shift](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shift.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>price</th>\n",
+       "      <th>date2</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>constant</th>\n",
+       "      <th>jour</th>\n",
+       "      <th>jouri</th>\n",
+       "      <th>diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>003b0195-2acb-4f46-b7fa-28cf266a8f60</td>\n",
+       "      <td>6d65b2b5-f34d-4440-8442-679175554db4</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "      <td>14</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-11-26 19:47:45.068274</td>\n",
+       "      <td>009e689c-51a1-4cef-99ca-a4ba364eba8d</td>\n",
+       "      <td>065474ed-6ba3-4ee1-aa83-b89e97d5a2aa</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2016-11-26 19:47:45.068274</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>8 days</td>\n",
+       "      <td>8</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-12-20 19:47:45.068274</td>\n",
+       "      <td>009e689c-51a1-4cef-99ca-a4ba364eba8d</td>\n",
+       "      <td>38a02a6a-8c04-4630-aaa6-2077077a9f87</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2016-12-20 19:47:45.068274</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>32 days</td>\n",
+       "      <td>32</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2017-01-12 19:47:45.068274</td>\n",
+       "      <td>009e689c-51a1-4cef-99ca-a4ba364eba8d</td>\n",
+       "      <td>f4cf0df4-0b6a-4b0f-93c7-d12499026fab</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2017-01-12 19:47:45.068274</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>55 days</td>\n",
+       "      <td>55</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>00a213c2-1174-4359-8a67-fe710ec1b439</td>\n",
+       "      <td>c1b3381b-9663-429d-a8fb-3b24c20a125b</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "      <td>14</td>\n",
+       "      <td>365.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date                                   idc  \\\n",
+       "0  2016-12-02 19:47:45.068274  003b0195-2acb-4f46-b7fa-28cf266a8f60   \n",
+       "1  2016-11-26 19:47:45.068274  009e689c-51a1-4cef-99ca-a4ba364eba8d   \n",
+       "2  2016-12-20 19:47:45.068274  009e689c-51a1-4cef-99ca-a4ba364eba8d   \n",
+       "3  2017-01-12 19:47:45.068274  009e689c-51a1-4cef-99ca-a4ba364eba8d   \n",
+       "4  2016-12-02 19:47:45.068274  00a213c2-1174-4359-8a67-fe710ec1b439   \n",
+       "\n",
+       "                                    idr  price                      date2  \\\n",
+       "0  6d65b2b5-f34d-4440-8442-679175554db4   80.0 2016-12-02 19:47:45.068274   \n",
+       "1  065474ed-6ba3-4ee1-aa83-b89e97d5a2aa   80.0 2016-11-26 19:47:45.068274   \n",
+       "2  38a02a6a-8c04-4630-aaa6-2077077a9f87   80.0 2016-12-20 19:47:45.068274   \n",
+       "3  f4cf0df4-0b6a-4b0f-93c7-d12499026fab   80.0 2017-01-12 19:47:45.068274   \n",
+       "4  c1b3381b-9663-429d-a8fb-3b24c20a125b   70.0 2016-12-02 19:47:45.068274   \n",
+       "\n",
+       "   weekday                   constant    jour  jouri   diff  \n",
+       "0        4 2016-11-18 19:47:45.068274 14 days     14  365.0  \n",
+       "1        5 2016-11-18 19:47:45.068274  8 days      8  365.0  \n",
+       "2        1 2016-11-18 19:47:45.068274 32 days     32   24.0  \n",
+       "3        3 2016-11-18 19:47:45.068274 55 days     55   23.0  \n",
+       "4        4 2016-11-18 19:47:45.068274 14 days     14  365.0  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rend2 = rend.sort_values([\"idc\", \"jouri\"]).reset_index(drop=True).copy()\n",
+    "rend2[\"diff\"] = rend2[\"jouri\"].diff()\n",
+    "rend2.loc[rend2.idc != rend2.idc.shift(1), \"diff\"] = 365\n",
+    "rend2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 9 \n",
+    "\n",
+    "*Calculer les coefficients de la régression* $prix \\sim age + genre + delay + 1/delay + jour\\_semaine$.\n",
+    "\n",
+    "L'âge ne fait pas partie de la table *tout*. Il faut faire une jointure pour le récupérer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "mat = tout.merge(persons, on=\"idc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Ensuite retour à [scikit-learn](https://scikit-learn.org/stable/) et plutôt le second [statsmodels](http://statsmodels.sourceforge.net/) pour effectuer des tests sur les coefficients du modèle. On regarde d'abord les corrélations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>delay</th>\n",
+       "      <th>delay1</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.030852</td>\n",
+       "      <td>-0.010694</td>\n",
+       "      <td>-0.023011</td>\n",
+       "      <td>0.032413</td>\n",
+       "      <td>0.614708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <td>0.030852</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.008700</td>\n",
+       "      <td>-0.026779</td>\n",
+       "      <td>-0.000064</td>\n",
+       "      <td>-0.198835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>delay</th>\n",
+       "      <td>-0.010694</td>\n",
+       "      <td>0.008700</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.616038</td>\n",
+       "      <td>0.039766</td>\n",
+       "      <td>0.049796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>delay1</th>\n",
+       "      <td>-0.023011</td>\n",
+       "      <td>-0.026779</td>\n",
+       "      <td>-0.616038</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.025400</td>\n",
+       "      <td>-0.054954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weekday</th>\n",
+       "      <td>0.032413</td>\n",
+       "      <td>-0.000064</td>\n",
+       "      <td>0.039766</td>\n",
+       "      <td>0.025400</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.448584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>price</th>\n",
+       "      <td>0.614708</td>\n",
+       "      <td>-0.198835</td>\n",
+       "      <td>0.049796</td>\n",
+       "      <td>-0.054954</td>\n",
+       "      <td>0.448584</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              age    gender     delay    delay1   weekday     price\n",
+       "age      1.000000  0.030852 -0.010694 -0.023011  0.032413  0.614708\n",
+       "gender   0.030852  1.000000  0.008700 -0.026779 -0.000064 -0.198835\n",
+       "delay   -0.010694  0.008700  1.000000 -0.616038  0.039766  0.049796\n",
+       "delay1  -0.023011 -0.026779 -0.616038  1.000000  0.025400 -0.054954\n",
+       "weekday  0.032413 -0.000064  0.039766  0.025400  1.000000  0.448584\n",
+       "price    0.614708 -0.198835  0.049796 -0.054954  0.448584  1.000000"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.matrix.ClusterGrid at 0x7fb3214280d0>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seaborn.clustermap(\n",
+    "    mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]].corr(), figsize=(5, 5)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Si le jeu de données n'est pas trop volumineux."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7fb3029bf820>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 42 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seaborn.pairplot(\n",
+    "    mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]], plot_kws={\"s\": 10}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Un dernier pour la route."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7fb2fad7ac20>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1250x250 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]]\n",
+    "g = seaborn.PairGrid(\n",
+    "    feat.sort_values(\"price\", ascending=False),\n",
+    "    x_vars=feat.columns[:-1],\n",
+    "    y_vars=[\"price\"],\n",
+    ")\n",
+    "g.map(seaborn.stripplot, size=3, orient=\"h\", palette=\"Reds_r\", edgecolor=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Régression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5.08109837e-01, -4.41245429e+00,  5.42852787e-04, -1.60797483e+01,\n",
+       "        2.12155016e+00])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = LinearRegression()\n",
+    "lr.fit(mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\"]], mat[\"price\"])\n",
+    "lr.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.612</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.612</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   800.0</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:15:05</td>     <th>  Log-Likelihood:    </th> <td> -8257.2</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.653e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>  2531</td>      <th>  BIC:               </th> <td>1.656e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>   55.4146</td> <td>    0.545</td> <td>  101.618</td> <td> 0.000</td> <td>   54.345</td> <td>   56.484</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>age</th>       <td>    0.5081</td> <td>    0.010</td> <td>   48.918</td> <td> 0.000</td> <td>    0.488</td> <td>    0.528</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gender</th>    <td>   -4.4125</td> <td>    0.250</td> <td>  -17.683</td> <td> 0.000</td> <td>   -4.902</td> <td>   -3.923</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay</th>     <td>    0.0005</td> <td>    0.001</td> <td>    0.562</td> <td> 0.574</td> <td>   -0.001</td> <td>    0.002</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay1</th>    <td>  -16.0797</td> <td>    4.842</td> <td>   -3.321</td> <td> 0.001</td> <td>  -25.575</td> <td>   -6.585</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>weekday</th>   <td>    2.1216</td> <td>    0.061</td> <td>   34.623</td> <td> 0.000</td> <td>    2.001</td> <td>    2.242</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>55.147</td> <th>  Durbin-Watson:     </th> <td>   1.969</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  57.558</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.358</td> <th>  Prob(JB):          </th> <td>3.17e-13</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.823</td> <th>  Cond. No.          </th> <td>9.20e+03</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.2e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.612   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.612   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     800.0   \\\\\n",
+       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
+       "\\textbf{Time:}             &     11:15:05     & \\textbf{  Log-Likelihood:    } &   -8257.2   \\\\\n",
+       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.653e+04   \\\\\n",
+       "\\textbf{Df Residuals:}     &        2531      & \\textbf{  BIC:               } & 1.656e+04   \\\\\n",
+       "\\textbf{Df Model:}         &           5      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept} &      55.4146  &        0.545     &   101.618  &         0.000        &       54.345    &       56.484     \\\\\n",
+       "\\textbf{age}       &       0.5081  &        0.010     &    48.918  &         0.000        &        0.488    &        0.528     \\\\\n",
+       "\\textbf{gender}    &      -4.4125  &        0.250     &   -17.683  &         0.000        &       -4.902    &       -3.923     \\\\\n",
+       "\\textbf{delay}     &       0.0005  &        0.001     &     0.562  &         0.574        &       -0.001    &        0.002     \\\\\n",
+       "\\textbf{delay1}    &     -16.0797  &        4.842     &    -3.321  &         0.001        &      -25.575    &       -6.585     \\\\\n",
+       "\\textbf{weekday}   &       2.1216  &        0.061     &    34.623  &         0.000        &        2.001    &        2.242     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 55.147 & \\textbf{  Durbin-Watson:     } &    1.969  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   57.558  \\\\\n",
+       "\\textbf{Skew:}          &  0.358 & \\textbf{  Prob(JB):          } & 3.17e-13  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.823 & \\textbf{  Cond. No.          } & 9.20e+03  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 9.2e+03. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.612\n",
+       "Model:                            OLS   Adj. R-squared:                  0.612\n",
+       "Method:                 Least Squares   F-statistic:                     800.0\n",
+       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        11:15:05   Log-Likelihood:                -8257.2\n",
+       "No. Observations:                2537   AIC:                         1.653e+04\n",
+       "Df Residuals:                    2531   BIC:                         1.656e+04\n",
+       "Df Model:                           5                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept     55.4146      0.545    101.618      0.000      54.345      56.484\n",
+       "age            0.5081      0.010     48.918      0.000       0.488       0.528\n",
+       "gender        -4.4125      0.250    -17.683      0.000      -4.902      -3.923\n",
+       "delay          0.0005      0.001      0.562      0.574      -0.001       0.002\n",
+       "delay1       -16.0797      4.842     -3.321      0.001     -25.575      -6.585\n",
+       "weekday        2.1216      0.061     34.623      0.000       2.001       2.242\n",
+       "==============================================================================\n",
+       "Omnibus:                       55.147   Durbin-Watson:                   1.969\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               57.558\n",
+       "Skew:                           0.358   Prob(JB):                     3.17e-13\n",
+       "Kurtosis:                       2.823   Cond. No.                     9.20e+03\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.2e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.formula.api import ols\n",
+    "\n",
+    "lr = ols(\"price ~ age + gender + delay + delay1 + weekday\", data=mat)\n",
+    "res = lr.fit()\n",
+    "res.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## 10\n",
+    "\n",
+    "*Comment comparer ce modèle avec le précédent ? Implémentez le calcul qui vous permet de répondre à cette question.*\n",
+    "\n",
+    "Nous pourrions comparer les coefficients $R^2$ (0.57, 0.61) des régressions pour savoir quelle est la meilleur excepté que celle-ci ne sont pas calculées sur les mêmes données. La comparaison n'a pas de sens et il serait dangeraux d'en tirer des conclusions. Les valeurs sont de plus très proches. Il faut comparer les prédictions. Dans le premier cas, on prédit le prix moyen. Dans le second, on prédit le prix d'une consultation. Il est alors possible de calculer une prédiction moyenne par patient et de comparer les erreurs de prédiction du prix moyen. D'un côté, la prédiction du prix moyen, de l'autre la prédiction du prix d'une consultation agrégé par patient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0.52440734, -4.36373925]), 61.05057671902867)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_moy = LinearRegression()\n",
+    "lr_moy.fit(join[[\"age\", \"gender\"]], join[\"price\"])\n",
+    "lr_moy.coef_, lr_moy.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>name</th>\n",
+       "      <th>price</th>\n",
+       "      <th>pred_moy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>Cendrillon</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>80.453648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>Cendrillon</td>\n",
+       "      <td>78.333333</td>\n",
+       "      <td>82.551278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>46</td>\n",
+       "      <td>1</td>\n",
+       "      <td>304895f0-f686-4b0e-8854-a705bb5a6982</td>\n",
+       "      <td>Balthazar</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>80.809575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>42</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3f0d31d2-0ef4-4e7e-b876-07d10225cc8c</td>\n",
+       "      <td>Balthazar</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>78.711946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>f29273f4-a76c-4158-b5f5-b3e5a080a0c7</td>\n",
+       "      <td>Balthazar</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>78.187538</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  gender                                   idc        name      price  \\\n",
+       "0   37       0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  Cendrillon  70.000000   \n",
+       "1   41       0  f44b004b-b01e-4835-b86d-1a42846c6d93  Cendrillon  78.333333   \n",
+       "2   46       1  304895f0-f686-4b0e-8854-a705bb5a6982   Balthazar  75.000000   \n",
+       "3   42       1  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c   Balthazar  95.000000   \n",
+       "4   41       1  f29273f4-a76c-4158-b5f5-b3e5a080a0c7   Balthazar  90.000000   \n",
+       "\n",
+       "    pred_moy  \n",
+       "0  80.453648  \n",
+       "1  82.551278  \n",
+       "2  80.809575  \n",
+       "3  78.711946  \n",
+       "4  78.187538  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_moy = lr_moy.predict(join[[\"age\", \"gender\"]])\n",
+    "join[\"pred_moy\"] = pred_moy\n",
+    "join.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On calcule l'erreur."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31.874448844571756"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "err1 = ((join.pred_moy - join.price) ** 2).sum() / join.shape[0]\n",
+    "err1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='price', ylabel='pred_moy'>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "join.plot(x=\"price\", y=\"pred_moy\", kind=\"scatter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 5.08109837e-01, -4.41245429e+00,  5.42852787e-04, -1.60797483e+01,\n",
+       "         2.12155016e+00]),\n",
+       " 55.41460950333407)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lrc = LinearRegression()\n",
+    "feat = mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\", \"idc\"]].copy()\n",
+    "lrc.fit(feat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\"]], feat[\"price\"])\n",
+    "lrc.coef_, lrc.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>delay</th>\n",
+       "      <th>delay1</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>price</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>predc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>4</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>82.855082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>4</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>82.855082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>2</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>80.644421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>86.334615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.019608</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>86.566717</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  gender  delay    delay1  weekday  price  \\\n",
+       "0   37       0  365.0  0.002732        4   75.0   \n",
+       "1   37       0  365.0  0.002732        4   65.0   \n",
+       "2   41       0  365.0  0.002732        2   75.0   \n",
+       "3   41       0   29.0  0.033333        5   80.0   \n",
+       "4   41       0   50.0  0.019608        5   80.0   \n",
+       "\n",
+       "                                    idc      predc  \n",
+       "0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  82.855082  \n",
+       "1  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  82.855082  \n",
+       "2  f44b004b-b01e-4835-b86d-1a42846c6d93  80.644421  \n",
+       "3  f44b004b-b01e-4835-b86d-1a42846c6d93  86.334615  \n",
+       "4  f44b004b-b01e-4835-b86d-1a42846c6d93  86.566717  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predc = lrc.predict(feat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\"]])\n",
+    "feat[\"predc\"] = predc\n",
+    "feat.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On agrège les secondes prédictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predc</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>idc</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>003b0195-2acb-4f46-b7fa-28cf266a8f60</th>\n",
+       "      <td>85.048055</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>009e689c-51a1-4cef-99ca-a4ba364eba8d</th>\n",
+       "      <td>83.945104</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00a213c2-1174-4359-8a67-fe710ec1b439</th>\n",
+       "      <td>72.345309</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00e42818-aade-4758-a5f6-c78a6f235ea5</th>\n",
+       "      <td>65.277461</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0153b785-9acd-4d28-aad1-62f8bf2faea3</th>\n",
+       "      <td>79.298313</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          predc  price\n",
+       "idc                                                   \n",
+       "003b0195-2acb-4f46-b7fa-28cf266a8f60  85.048055   80.0\n",
+       "009e689c-51a1-4cef-99ca-a4ba364eba8d  83.945104   80.0\n",
+       "00a213c2-1174-4359-8a67-fe710ec1b439  72.345309   70.0\n",
+       "00e42818-aade-4758-a5f6-c78a6f235ea5  65.277461   70.0\n",
+       "0153b785-9acd-4d28-aad1-62f8bf2faea3  79.298313   75.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agg = feat[[\"idc\", \"predc\", \"price\"]].groupby(\"idc\").mean()\n",
+    "agg.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.697836056479865"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "err2 = ((agg.predc - agg.price) ** 2).sum() / agg.shape[0]\n",
+    "err2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Le second modèle est clairement meilleur."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='price', ylabel='predc'>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "agg.plot(x=\"price\", y=\"predc\", kind=\"scatter\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "La seconde régression utilise une information dont on ne dispose pas au niveau agrégé : le jour de la semaine et un précédent graphe a clairement montré que c'était une variable importante. Un dernier graphe pour comparer les deux prédictions en montrant les prédictions triées par prix à prédire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>name</th>\n",
+       "      <th>price</th>\n",
+       "      <th>pred_moy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6423a722-4769-4a7c-8d1d-266538c2a07a</td>\n",
+       "      <td>Balthazar</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>66.12617</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index  age  gender                                   idc       name  price  \\\n",
+       "0      0   18       1  6423a722-4769-4a7c-8d1d-266538c2a07a  Balthazar   60.0   \n",
+       "\n",
+       "   pred_moy  \n",
+       "0  66.12617  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp = join.sort_values(\"price\").reset_index(drop=True).reset_index(drop=False)\n",
+    "temp.head(n=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='index'>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp.plot(x=\"index\", y=[\"price\", \"pred_moy\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>predc</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>64.54544</td>\n",
+       "      <td>60.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index     predc  price\n",
+       "0      0  64.54544   60.0"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp2 = agg.sort_values(\"price\").reset_index(drop=True).reset_index(drop=False)\n",
+    "temp2.head(n=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='index'>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = temp.plot(x=\"index\", y=\"price\", figsize=(14, 4), ylim=[60, 120])\n",
+    "temp.plot(x=\"index\", y=\"pred_moy\", linewidth=1, ax=ax, ylim=[60, 120])\n",
+    "temp2.plot(x=\"index\", y=\"predc\", ax=ax, linewidth=0.6, ylim=[60, 120])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "C'est finalement un peu plus visible sur le graphe précédent (nuage de points) mais aussi un peu trompeur du fait de la superposition des points. Une dernière remarque. En machine learning, nous avons l'habitude d'apprendre un modèle sur une base d'apprentissage et de tester les prédictions sur une autre. Dans notre cas, nous avons appris et prédit sur la même base. Ce type de tester est évidemment plus fiable. Mais nous avons comparé ici deux erreurs d'apprentissage moyennes et c'est exactement ce que l'on fait lorsqu'on compare deux coefficients $R^2$.\n",
+    "\n",
+    "Un dernier graphe pour la route obtenu en triant les erreurs par ordre croissant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fb2fa805210>]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp[\"erreur\"] = temp.pred_moy - temp.price\n",
+    "temp2[\"erreur2\"] = temp2.predc - temp2.price\n",
+    "ax = temp[[\"erreur\"]].sort_values(\"erreur\").reset_index(drop=True).plot()\n",
+    "temp2[[\"erreur2\"]].sort_values(\"erreur2\").reset_index(drop=True).plot(ax=ax)\n",
+    "ax.plot([0, 1000], [0, 0], \"r--\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Le second modèle fait des erreurs moins importantes surtout côté négatif. Il sous-estime moins la bonne valeur."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Traitement spécifique de la variable catégorielle weekday\n",
+    "\n",
+    "Le second modèle ne prend pas en compte le dimanche comme jour de la semaine. *weekday* est une variable catégorielle. Contrairment au genre, elle possède plus de deux modalités. Il serait intéressant de la traiter comme un ensemble de variable binaire et non une colonne de type entier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lu</th>\n",
+       "      <th>ma</th>\n",
+       "      <th>me</th>\n",
+       "      <th>je</th>\n",
+       "      <th>ve</th>\n",
+       "      <th>sa</th>\n",
+       "      <th>di</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      lu     ma     me     je     ve     sa     di\n",
+       "0  False  False  False  False   True  False  False\n",
+       "1  False  False  False  False   True  False  False\n",
+       "2  False  False   True  False  False  False  False\n",
+       "3  False  False  False  False  False   True  False\n",
+       "4  False  False  False  False  False   True  False"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummies = pandas.get_dummies(mat.weekday)\n",
+    "dummies.columns = [\"lu\", \"ma\", \"me\", \"je\", \"ve\", \"sa\", \"di\"]\n",
+    "dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On supprime une modalité pour éviter d'avoir une matrice corrélée avec la constante et on ajoute des variables au modèle de régression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>price</th>\n",
+       "      <th>date2</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>constant</th>\n",
+       "      <th>jour</th>\n",
+       "      <th>jouri</th>\n",
+       "      <th>diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>delay1</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>name</th>\n",
+       "      <th>ma</th>\n",
+       "      <th>me</th>\n",
+       "      <th>je</th>\n",
+       "      <th>ve</th>\n",
+       "      <th>sa</th>\n",
+       "      <th>di</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2016-12-02 19:47:45.068274</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-11-18 19:47:45.068274</td>\n",
+       "      <td>14 days</td>\n",
+       "      <td>14</td>\n",
+       "      <td>-26.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cendrillon</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         date                                   idc  \\\n",
+       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "\n",
+       "                                    idr  price                      date2  \\\n",
+       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
+       "\n",
+       "   weekday                   constant    jour  jouri  diff  ...    delay1  \\\n",
+       "0        4 2016-11-18 19:47:45.068274 14 days     14 -26.0  ...  0.002732   \n",
+       "\n",
+       "   age  gender        name     ma     me     je    ve     sa     di  \n",
+       "0   37       0  Cendrillon  False  False  False  True  False  False  \n",
+       "\n",
+       "[1 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat2 = pandas.concat([mat, dummies.drop(\"lu\", axis=1)], axis=1)\n",
+    "mat2.head(n=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.918</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.917</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2812.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:15:41</td>     <th>  Log-Likelihood:    </th> <td> -6293.7</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.261e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>  2526</td>      <th>  BIC:               </th> <td>1.267e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    10</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>  <td>   59.0371</td> <td>    0.272</td> <td>  217.342</td> <td> 0.000</td> <td>   58.504</td> <td>   59.570</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>ma[T.True]</th> <td>   -0.0840</td> <td>    0.208</td> <td>   -0.405</td> <td> 0.686</td> <td>   -0.491</td> <td>    0.323</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>me[T.True]</th> <td>    0.3003</td> <td>    0.216</td> <td>    1.392</td> <td> 0.164</td> <td>   -0.123</td> <td>    0.723</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>je[T.True]</th> <td>    0.1607</td> <td>    0.219</td> <td>    0.735</td> <td> 0.463</td> <td>   -0.268</td> <td>    0.590</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>ve[T.True]</th> <td>   -0.1748</td> <td>    0.212</td> <td>   -0.825</td> <td> 0.409</td> <td>   -0.590</td> <td>    0.240</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sa[T.True]</th> <td>    0.1849</td> <td>    0.210</td> <td>    0.879</td> <td> 0.379</td> <td>   -0.227</td> <td>    0.597</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>di[T.True]</th> <td>   20.0978</td> <td>    0.211</td> <td>   95.203</td> <td> 0.000</td> <td>   19.684</td> <td>   20.512</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>age</th>        <td>    0.5005</td> <td>    0.005</td> <td>  104.318</td> <td> 0.000</td> <td>    0.491</td> <td>    0.510</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gender</th>     <td>   -4.0722</td> <td>    0.115</td> <td>  -35.321</td> <td> 0.000</td> <td>   -4.298</td> <td>   -3.846</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay</th>      <td>    0.0003</td> <td>    0.000</td> <td>    0.661</td> <td> 0.509</td> <td>   -0.001</td> <td>    0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay1</th>     <td>  -20.7385</td> <td>    2.236</td> <td>   -9.275</td> <td> 0.000</td> <td>  -25.123</td> <td>  -16.354</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>257.849</td> <th>  Durbin-Watson:     </th> <td>   2.034</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 484.437</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.672</td>  <th>  Prob(JB):          </th> <td>6.40e-106</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 4.666</td>  <th>  Cond. No.          </th> <td>9.20e+03</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.2e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.918   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.917   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     2812.   \\\\\n",
+       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
+       "\\textbf{Time:}             &     11:15:41     & \\textbf{  Log-Likelihood:    } &   -6293.7   \\\\\n",
+       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.261e+04   \\\\\n",
+       "\\textbf{Df Residuals:}     &        2526      & \\textbf{  BIC:               } & 1.267e+04   \\\\\n",
+       "\\textbf{Df Model:}         &          10      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                    & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept}  &      59.0371  &        0.272     &   217.342  &         0.000        &       58.504    &       59.570     \\\\\n",
+       "\\textbf{ma[T.True]} &      -0.0840  &        0.208     &    -0.405  &         0.686        &       -0.491    &        0.323     \\\\\n",
+       "\\textbf{me[T.True]} &       0.3003  &        0.216     &     1.392  &         0.164        &       -0.123    &        0.723     \\\\\n",
+       "\\textbf{je[T.True]} &       0.1607  &        0.219     &     0.735  &         0.463        &       -0.268    &        0.590     \\\\\n",
+       "\\textbf{ve[T.True]} &      -0.1748  &        0.212     &    -0.825  &         0.409        &       -0.590    &        0.240     \\\\\n",
+       "\\textbf{sa[T.True]} &       0.1849  &        0.210     &     0.879  &         0.379        &       -0.227    &        0.597     \\\\\n",
+       "\\textbf{di[T.True]} &      20.0978  &        0.211     &    95.203  &         0.000        &       19.684    &       20.512     \\\\\n",
+       "\\textbf{age}        &       0.5005  &        0.005     &   104.318  &         0.000        &        0.491    &        0.510     \\\\\n",
+       "\\textbf{gender}     &      -4.0722  &        0.115     &   -35.321  &         0.000        &       -4.298    &       -3.846     \\\\\n",
+       "\\textbf{delay}      &       0.0003  &        0.000     &     0.661  &         0.509        &       -0.001    &        0.001     \\\\\n",
+       "\\textbf{delay1}     &     -20.7385  &        2.236     &    -9.275  &         0.000        &      -25.123    &      -16.354     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 257.849 & \\textbf{  Durbin-Watson:     } &     2.034  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   484.437  \\\\\n",
+       "\\textbf{Skew:}          &  -0.672 & \\textbf{  Prob(JB):          } & 6.40e-106  \\\\\n",
+       "\\textbf{Kurtosis:}      &   4.666 & \\textbf{  Cond. No.          } &  9.20e+03  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 9.2e+03. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.918\n",
+       "Model:                            OLS   Adj. R-squared:                  0.917\n",
+       "Method:                 Least Squares   F-statistic:                     2812.\n",
+       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        11:15:41   Log-Likelihood:                -6293.7\n",
+       "No. Observations:                2537   AIC:                         1.261e+04\n",
+       "Df Residuals:                    2526   BIC:                         1.267e+04\n",
+       "Df Model:                          10                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept     59.0371      0.272    217.342      0.000      58.504      59.570\n",
+       "ma[T.True]    -0.0840      0.208     -0.405      0.686      -0.491       0.323\n",
+       "me[T.True]     0.3003      0.216      1.392      0.164      -0.123       0.723\n",
+       "je[T.True]     0.1607      0.219      0.735      0.463      -0.268       0.590\n",
+       "ve[T.True]    -0.1748      0.212     -0.825      0.409      -0.590       0.240\n",
+       "sa[T.True]     0.1849      0.210      0.879      0.379      -0.227       0.597\n",
+       "di[T.True]    20.0978      0.211     95.203      0.000      19.684      20.512\n",
+       "age            0.5005      0.005    104.318      0.000       0.491       0.510\n",
+       "gender        -4.0722      0.115    -35.321      0.000      -4.298      -3.846\n",
+       "delay          0.0003      0.000      0.661      0.509      -0.001       0.001\n",
+       "delay1       -20.7385      2.236     -9.275      0.000     -25.123     -16.354\n",
+       "==============================================================================\n",
+       "Omnibus:                      257.849   Durbin-Watson:                   2.034\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              484.437\n",
+       "Skew:                          -0.672   Prob(JB):                    6.40e-106\n",
+       "Kurtosis:                       4.666   Cond. No.                     9.20e+03\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.2e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = ols(\n",
+    "    \"price ~ age + gender + delay + delay1 + ma + me + je + ve + sa + di\", data=mat2\n",
+    ")\n",
+    "res = lr.fit()\n",
+    "res.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On vérifie que le coefficient pour dimanche n'est clairement significatif contrairement aux autres dont la probabilité d'être nul est élevée. Le médecin appliquerait une majoration de 20 euros le dimanche. Le coefficient $R^2$ est aussi nettement plus élevé. On construit les prédictions d'un second modèle en ne tenant compte que de la variable dimanche."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 5.00892234e-01, -4.07229120e+00,  2.97614199e-04, -2.07004497e+01,\n",
+       "         2.00395644e+01]),\n",
+       " 59.07951294272968)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lrc = LinearRegression()\n",
+    "feat2 = mat2[[\"age\", \"gender\", \"delay\", \"delay1\", \"price\", \"idc\", \"di\"]].copy()\n",
+    "lrc.fit(feat2[[\"age\", \"gender\", \"delay\", \"delay1\", \"di\"]], feat2[\"price\"])\n",
+    "lrc.coef_, lrc.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>delay</th>\n",
+       "      <th>delay1</th>\n",
+       "      <th>price</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>di</th>\n",
+       "      <th>predc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>False</td>\n",
+       "      <td>77.664596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>False</td>\n",
+       "      <td>77.664596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>False</td>\n",
+       "      <td>79.668165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>False</td>\n",
+       "      <td>78.934710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.019608</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
+       "      <td>False</td>\n",
+       "      <td>79.225084</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  gender  delay    delay1  price                                   idc  \\\n",
+       "0   37       0  365.0  0.002732   75.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "1   37       0  365.0  0.002732   65.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "2   41       0  365.0  0.002732   75.0  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "3   41       0   29.0  0.033333   80.0  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "4   41       0   50.0  0.019608   80.0  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
+       "\n",
+       "      di      predc  \n",
+       "0  False  77.664596  \n",
+       "1  False  77.664596  \n",
+       "2  False  79.668165  \n",
+       "3  False  78.934710  \n",
+       "4  False  79.225084  "
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predc = lrc.predict(feat2[[\"age\", \"gender\", \"delay\", \"delay1\", \"di\"]])\n",
+    "feat2[\"predc\"] = predc\n",
+    "feat2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predc</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>idc</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>003b0195-2acb-4f46-b7fa-28cf266a8f60</th>\n",
+       "      <td>80.103904</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>009e689c-51a1-4cef-99ca-a4ba364eba8d</th>\n",
+       "      <td>80.812446</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00a213c2-1174-4359-8a67-fe710ec1b439</th>\n",
+       "      <td>67.581598</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>00e42818-aade-4758-a5f6-c78a6f235ea5</th>\n",
+       "      <td>66.890947</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0153b785-9acd-4d28-aad1-62f8bf2faea3</th>\n",
+       "      <td>74.158351</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          predc  price\n",
+       "idc                                                   \n",
+       "003b0195-2acb-4f46-b7fa-28cf266a8f60  80.103904   80.0\n",
+       "009e689c-51a1-4cef-99ca-a4ba364eba8d  80.812446   80.0\n",
+       "00a213c2-1174-4359-8a67-fe710ec1b439  67.581598   70.0\n",
+       "00e42818-aade-4758-a5f6-c78a6f235ea5  66.890947   70.0\n",
+       "0153b785-9acd-4d28-aad1-62f8bf2faea3  74.158351   75.0"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agg2 = feat2[[\"idc\", \"predc\", \"price\"]].groupby(\"idc\").mean()\n",
+    "agg2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.626505427547838"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "err2d = ((agg2.predc - agg2.price) ** 2).sum() / agg2.shape[0]\n",
+    "err2d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nettement, nettement mieux."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='price', ylabel='predc'>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "agg2.plot(x=\"price\", y=\"predc\", kind=\"scatter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='index'>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp2 = agg2.sort_values(\"price\").reset_index(drop=True).reset_index(drop=False)\n",
+    "ax = temp2.plot(x=\"index\", y=\"price\", figsize=(14, 4), ylim=[60, 120])\n",
+    "temp2.plot(x=\"index\", y=\"predc\", linewidth=1, ax=ax, ylim=[60, 120])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Une variable catégorielle en une seule colonne ?\n",
+    "\n",
+    "Un jeu de données peut rapidement croître s'il est étendu pour chaque variable catégorielle. On peut utiliser le module [category_encoders](https://github.com/scikit-learn-contrib/categorical-encoding/tree/master/category_encoders) ou [statsmodels](http://statsmodels.sourceforge.net/devel/contrasts.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xadupre/.local/lib/python3.10/site-packages/category_encoders/base_contrast_encoder.py:126: FutureWarning: Intercept column might not be added anymore in future releases (c.f. issue #370)\n",
+      "  warnings.warn(\"Intercept column might not be added anymore in future releases (c.f. issue #370)\",\n",
+      "/home/xadupre/.local/lib/python3.10/site-packages/category_encoders/base_contrast_encoder.py:126: FutureWarning: Intercept column might not be added anymore in future releases (c.f. issue #370)\n",
+      "  warnings.warn(\"Intercept column might not be added anymore in future releases (c.f. issue #370)\",\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>intercept</th>\n",
+       "      <th>weekday_0</th>\n",
+       "      <th>weekday_1</th>\n",
+       "      <th>weekday_2</th>\n",
+       "      <th>weekday_3</th>\n",
+       "      <th>weekday_4</th>\n",
+       "      <th>weekday_5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.566947</td>\n",
+       "      <td>0.545545</td>\n",
+       "      <td>-0.408248</td>\n",
+       "      <td>0.241747</td>\n",
+       "      <td>-0.109109</td>\n",
+       "      <td>0.032898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.566947</td>\n",
+       "      <td>0.545545</td>\n",
+       "      <td>-0.408248</td>\n",
+       "      <td>0.241747</td>\n",
+       "      <td>-0.109109</td>\n",
+       "      <td>0.032898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.377964</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.408248</td>\n",
+       "      <td>-0.564076</td>\n",
+       "      <td>0.436436</td>\n",
+       "      <td>-0.197386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.188982</td>\n",
+       "      <td>-0.327327</td>\n",
+       "      <td>0.408248</td>\n",
+       "      <td>0.080582</td>\n",
+       "      <td>-0.545545</td>\n",
+       "      <td>0.493464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.188982</td>\n",
+       "      <td>-0.327327</td>\n",
+       "      <td>0.408248</td>\n",
+       "      <td>0.080582</td>\n",
+       "      <td>-0.545545</td>\n",
+       "      <td>0.493464</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   weekday  intercept  weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  \\\n",
+       "0        4          1  -0.566947   0.545545  -0.408248   0.241747  -0.109109   \n",
+       "1        4          1  -0.566947   0.545545  -0.408248   0.241747  -0.109109   \n",
+       "2        2          1  -0.377964   0.000000   0.408248  -0.564076   0.436436   \n",
+       "3        5          1  -0.188982  -0.327327   0.408248   0.080582  -0.545545   \n",
+       "4        5          1  -0.188982  -0.327327   0.408248   0.080582  -0.545545   \n",
+       "\n",
+       "   weekday_5  \n",
+       "0   0.032898  \n",
+       "1   0.032898  \n",
+       "2  -0.197386  \n",
+       "3   0.493464  \n",
+       "4   0.493464  "
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from category_encoders import PolynomialEncoder\n",
+    "\n",
+    "encoder = PolynomialEncoder(cols=[\"weekday\"])\n",
+    "encoder.fit(rend[[\"weekday\"]], rend[\"price\"])\n",
+    "pred = encoder.transform(rend[[\"weekday\"]])\n",
+    "conc = pandas.concat([rend[[\"weekday\"]], pred], axis=1)\n",
+    "conc.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ou associer la valeur de la cible à prédire pour chaque jour de la semaine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>76.112532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>76.195373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>77.047478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>76.677116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>76.049724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>76.630728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>97.078804</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   weekday      price\n",
+       "0        0  76.112532\n",
+       "1        1  76.195373\n",
+       "2        2  77.047478\n",
+       "3        3  76.677116\n",
+       "4        4  76.049724\n",
+       "5        5  76.630728\n",
+       "6        6  97.078804"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "copy = rend[[\"weekday\", \"price\"]].copy()\n",
+    "gr = copy.groupby(\"weekday\", as_index=False).mean()\n",
+    "gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>delay</th>\n",
+       "      <th>delay1</th>\n",
+       "      <th>price</th>\n",
+       "      <th>idc</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>price_label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>4</td>\n",
+       "      <td>76.049724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
+       "      <td>4</td>\n",
+       "      <td>76.049724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>57</td>\n",
+       "      <td>1</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>7ffb8a8b-a829-41f1-9572-2985ee9ba05e</td>\n",
+       "      <td>4</td>\n",
+       "      <td>76.049724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>0.002732</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0153b785-9acd-4d28-aad1-62f8bf2faea3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>76.049724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.034483</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>47e415f0-f4e9-40c5-a59d-adc4c0621c8a</td>\n",
+       "      <td>4</td>\n",
+       "      <td>76.049724</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  gender  delay    delay1  price                                   idc  \\\n",
+       "0   37       0  365.0  0.002732   75.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "1   37       0  365.0  0.002732   65.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
+       "2   57       1  365.0  0.002732   85.0  7ffb8a8b-a829-41f1-9572-2985ee9ba05e   \n",
+       "3   30       0  365.0  0.002732   75.0  0153b785-9acd-4d28-aad1-62f8bf2faea3   \n",
+       "4   28       1   28.0  0.034483   70.0  47e415f0-f4e9-40c5-a59d-adc4c0621c8a   \n",
+       "\n",
+       "   weekday  price_label  \n",
+       "0        4    76.049724  \n",
+       "1        4    76.049724  \n",
+       "2        4    76.049724  \n",
+       "3        4    76.049724  \n",
+       "4        4    76.049724  "
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feat3 = mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"price\", \"idc\", \"weekday\"]]\n",
+    "feat3 = feat3.merge(gr, on=\"weekday\", suffixes=(\"\", \"_label\"))\n",
+    "feat3.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On apprend avec la variable *weekday* :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.612</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.612</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   800.0</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:15:56</td>     <th>  Log-Likelihood:    </th> <td> -8257.2</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.653e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>  2531</td>      <th>  BIC:               </th> <td>1.656e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>   55.4146</td> <td>    0.545</td> <td>  101.618</td> <td> 0.000</td> <td>   54.345</td> <td>   56.484</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>age</th>       <td>    0.5081</td> <td>    0.010</td> <td>   48.918</td> <td> 0.000</td> <td>    0.488</td> <td>    0.528</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gender</th>    <td>   -4.4125</td> <td>    0.250</td> <td>  -17.683</td> <td> 0.000</td> <td>   -4.902</td> <td>   -3.923</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay</th>     <td>    0.0005</td> <td>    0.001</td> <td>    0.562</td> <td> 0.574</td> <td>   -0.001</td> <td>    0.002</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay1</th>    <td>  -16.0797</td> <td>    4.842</td> <td>   -3.321</td> <td> 0.001</td> <td>  -25.575</td> <td>   -6.585</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>weekday</th>   <td>    2.1216</td> <td>    0.061</td> <td>   34.623</td> <td> 0.000</td> <td>    2.001</td> <td>    2.242</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>55.147</td> <th>  Durbin-Watson:     </th> <td>   0.442</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  57.558</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.358</td> <th>  Prob(JB):          </th> <td>3.17e-13</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.823</td> <th>  Cond. No.          </th> <td>9.20e+03</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.2e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.612   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.612   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     800.0   \\\\\n",
+       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
+       "\\textbf{Time:}             &     11:15:56     & \\textbf{  Log-Likelihood:    } &   -8257.2   \\\\\n",
+       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.653e+04   \\\\\n",
+       "\\textbf{Df Residuals:}     &        2531      & \\textbf{  BIC:               } & 1.656e+04   \\\\\n",
+       "\\textbf{Df Model:}         &           5      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept} &      55.4146  &        0.545     &   101.618  &         0.000        &       54.345    &       56.484     \\\\\n",
+       "\\textbf{age}       &       0.5081  &        0.010     &    48.918  &         0.000        &        0.488    &        0.528     \\\\\n",
+       "\\textbf{gender}    &      -4.4125  &        0.250     &   -17.683  &         0.000        &       -4.902    &       -3.923     \\\\\n",
+       "\\textbf{delay}     &       0.0005  &        0.001     &     0.562  &         0.574        &       -0.001    &        0.002     \\\\\n",
+       "\\textbf{delay1}    &     -16.0797  &        4.842     &    -3.321  &         0.001        &      -25.575    &       -6.585     \\\\\n",
+       "\\textbf{weekday}   &       2.1216  &        0.061     &    34.623  &         0.000        &        2.001    &        2.242     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 55.147 & \\textbf{  Durbin-Watson:     } &    0.442  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   57.558  \\\\\n",
+       "\\textbf{Skew:}          &  0.358 & \\textbf{  Prob(JB):          } & 3.17e-13  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.823 & \\textbf{  Cond. No.          } & 9.20e+03  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 9.2e+03. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.612\n",
+       "Model:                            OLS   Adj. R-squared:                  0.612\n",
+       "Method:                 Least Squares   F-statistic:                     800.0\n",
+       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        11:15:56   Log-Likelihood:                -8257.2\n",
+       "No. Observations:                2537   AIC:                         1.653e+04\n",
+       "Df Residuals:                    2531   BIC:                         1.656e+04\n",
+       "Df Model:                           5                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept     55.4146      0.545    101.618      0.000      54.345      56.484\n",
+       "age            0.5081      0.010     48.918      0.000       0.488       0.528\n",
+       "gender        -4.4125      0.250    -17.683      0.000      -4.902      -3.923\n",
+       "delay          0.0005      0.001      0.562      0.574      -0.001       0.002\n",
+       "delay1       -16.0797      4.842     -3.321      0.001     -25.575      -6.585\n",
+       "weekday        2.1216      0.061     34.623      0.000       2.001       2.242\n",
+       "==============================================================================\n",
+       "Omnibus:                       55.147   Durbin-Watson:                   0.442\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               57.558\n",
+       "Skew:                           0.358   Prob(JB):                     3.17e-13\n",
+       "Kurtosis:                       2.823   Cond. No.                     9.20e+03\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.2e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = ols(\"price ~ age + gender + delay + delay1 + weekday\", data=feat3)\n",
+    "res = lr.fit()\n",
+    "res.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Et on compare avec la variable *price_label* :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.917</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.917</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5611.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:15:58</td>     <th>  Log-Likelihood:    </th> <td> -6298.8</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.261e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>  2531</td>      <th>  BIC:               </th> <td>1.264e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>   <td>  -14.9859</td> <td>    0.664</td> <td>  -22.576</td> <td> 0.000</td> <td>  -16.288</td> <td>  -13.684</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>age</th>         <td>    0.5000</td> <td>    0.005</td> <td>  104.173</td> <td> 0.000</td> <td>    0.491</td> <td>    0.509</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gender</th>      <td>   -4.0767</td> <td>    0.115</td> <td>  -35.344</td> <td> 0.000</td> <td>   -4.303</td> <td>   -3.851</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay</th>       <td>    0.0003</td> <td>    0.000</td> <td>    0.601</td> <td> 0.548</td> <td>   -0.001</td> <td>    0.001</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>delay1</th>      <td>  -20.8060</td> <td>    2.237</td> <td>   -9.303</td> <td> 0.000</td> <td>  -25.192</td> <td>  -16.420</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>price_label</th> <td>    0.9696</td> <td>    0.008</td> <td>  122.209</td> <td> 0.000</td> <td>    0.954</td> <td>    0.985</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>249.245</td> <th>  Durbin-Watson:     </th> <td>   2.035</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 459.156</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.660</td>  <th>  Prob(JB):          </th> <td>1.98e-100</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 4.613</td>  <th>  Cond. No.          </th> <td>9.47e+03</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.47e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.917   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.917   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     5611.   \\\\\n",
+       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
+       "\\textbf{Time:}             &     11:15:58     & \\textbf{  Log-Likelihood:    } &   -6298.8   \\\\\n",
+       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.261e+04   \\\\\n",
+       "\\textbf{Df Residuals:}     &        2531      & \\textbf{  BIC:               } & 1.264e+04   \\\\\n",
+       "\\textbf{Df Model:}         &           5      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                      & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept}    &     -14.9859  &        0.664     &   -22.576  &         0.000        &      -16.288    &      -13.684     \\\\\n",
+       "\\textbf{age}          &       0.5000  &        0.005     &   104.173  &         0.000        &        0.491    &        0.509     \\\\\n",
+       "\\textbf{gender}       &      -4.0767  &        0.115     &   -35.344  &         0.000        &       -4.303    &       -3.851     \\\\\n",
+       "\\textbf{delay}        &       0.0003  &        0.000     &     0.601  &         0.548        &       -0.001    &        0.001     \\\\\n",
+       "\\textbf{delay1}       &     -20.8060  &        2.237     &    -9.303  &         0.000        &      -25.192    &      -16.420     \\\\\n",
+       "\\textbf{price\\_label} &       0.9696  &        0.008     &   122.209  &         0.000        &        0.954    &        0.985     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 249.245 & \\textbf{  Durbin-Watson:     } &     2.035  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   459.156  \\\\\n",
+       "\\textbf{Skew:}          &  -0.660 & \\textbf{  Prob(JB):          } & 1.98e-100  \\\\\n",
+       "\\textbf{Kurtosis:}      &   4.613 & \\textbf{  Cond. No.          } &  9.47e+03  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 9.47e+03. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.917\n",
+       "Model:                            OLS   Adj. R-squared:                  0.917\n",
+       "Method:                 Least Squares   F-statistic:                     5611.\n",
+       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        11:15:58   Log-Likelihood:                -6298.8\n",
+       "No. Observations:                2537   AIC:                         1.261e+04\n",
+       "Df Residuals:                    2531   BIC:                         1.264e+04\n",
+       "Df Model:                           5                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "===============================================================================\n",
+       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept     -14.9859      0.664    -22.576      0.000     -16.288     -13.684\n",
+       "age             0.5000      0.005    104.173      0.000       0.491       0.509\n",
+       "gender         -4.0767      0.115    -35.344      0.000      -4.303      -3.851\n",
+       "delay           0.0003      0.000      0.601      0.548      -0.001       0.001\n",
+       "delay1        -20.8060      2.237     -9.303      0.000     -25.192     -16.420\n",
+       "price_label     0.9696      0.008    122.209      0.000       0.954       0.985\n",
+       "==============================================================================\n",
+       "Omnibus:                      249.245   Durbin-Watson:                   2.035\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              459.156\n",
+       "Skew:                          -0.660   Prob(JB):                    1.98e-100\n",
+       "Kurtosis:                       4.613   Cond. No.                     9.47e+03\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.47e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = ols(\"price ~ age + gender + delay + delay1 + price_label\", data=feat3)\n",
+    "res = lr.fit()\n",
+    "res.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aussi bien qu'avec la variable *dimanche*."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_doc/practice/index_ml.rst b/_doc/practice/index_ml.rst
index 5912e244..b06c1b38 100644
--- a/_doc/practice/index_ml.rst
+++ b/_doc/practice/index_ml.rst
@@ -19,6 +19,7 @@ Machine Learning
     ml/winesr_knn_split_strat
     ml/winesr_knn_cross_val
     ml/winesr_knn_hyper
+    ml/ml_a_tree_overfitting
 
 .. toctree::
     :maxdepth: 1
diff --git a/_doc/practice/ml/artificiel_multiclass.ipynb b/_doc/practice/ml/artificiel_multiclass.ipynb
index 48120613..6e45d13e 100644
--- a/_doc/practice/ml/artificiel_multiclass.ipynb
+++ b/_doc/practice/ml/artificiel_multiclass.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Classification multi-classe et jeu mal balancé\n",
     "\n",
-    "Plus il y a de classes, plus la classification est difficile car le nombre d'exemples par classe diminue. Voyons cela plus en détail sur des jeux artificiels produits mar [make_blobs](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html)."
+    "Plus il y a de classes, plus la classification est difficile car le nombre d'exemples par classe diminue. Voyons cela plus en détail sur des jeux artificiels produits mar [make_blobs](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html)."
    ]
   },
   {
diff --git a/_doc/practice/ml/ml_a_tree_overfitting.ipynb b/_doc/practice/ml/ml_a_tree_overfitting.ipynb
new file mode 100644
index 00000000..288089e3
--- /dev/null
+++ b/_doc/practice/ml/ml_a_tree_overfitting.ipynb
@@ -0,0 +1,1716 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tree, hyperparamètres, overfitting\n",
+    "\n",
+    "L'[overfitting](https://en.wikipedia.org/wiki/Overfitting) ou [surapprentissage](https://fr.wikipedia.org/wiki/Surapprentissage) apparaît lorsque les prédictions sur de nouvelles données sont nettement moins bonnes que celles obtenus sur la base d'apprentissage. Les forêts aléatoires sont moins sujettes à l'overfitting que les arbres de décisions qui les composent. Quelques illustrations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Données générées\n",
+    "\n",
+    "On génère un nuage de points $y_i = \\sin(x_i) + \\epsilon_i$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "import pandas\n",
+    "import numpy, numpy.random\n",
+    "\n",
+    "\n",
+    "def generate_data(n):\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    X = numpy.arange(n) / n * 6\n",
+    "    mat = numpy.random.normal(size=(n, 1)) / 2\n",
+    "    si = numpy.sin(X).reshape((n, 1))\n",
+    "    Y = mat + si\n",
+    "    X = X.reshape((n, 1))\n",
+    "    data = numpy.hstack((X, Y))\n",
+    "    return data, X, Y\n",
+    "\n",
+    "\n",
+    "n = 100\n",
+    "data, X, Y = generate_data(n)\n",
+    "df = pandas.DataFrame(data, columns=[\"X\", \"Y\"])\n",
+    "df.plot(x=\"X\", y=\"Y\", kind=\"scatter\", figsize=(10, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Différents arbres de décision\n",
+    "\n",
+    "On regarde l'influence de paramêtres sur la sortie du modèle résultant de son apprentissage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### max_depth\n",
+    "\n",
+    "Un arbre représente une fonction en escalier. La profondeur détermine le nombre de feuilles, c'est-à-dire le nombre de valeurs possibles : $2^{max\\_depth}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"/home/xadupre/install/scikit-learn\")\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "ax = df.plot(\n",
+    "    x=\"X\", y=\"Y\", kind=\"scatter\", figsize=(10, 6), label=\"données\", title=\"DecisionTree\"\n",
+    ")\n",
+    "Xi = (numpy.arange(n * 10) / n * 6 / 10).reshape((n * 10, 1))\n",
+    "for max_depth in [1, 2, 3, 6, 10]:\n",
+    "    clr = DecisionTreeRegressor(max_depth=max_depth)\n",
+    "    clr.fit(X, Y)\n",
+    "    pred = clr.predict(Xi)\n",
+    "    ex = pandas.DataFrame(Xi, columns=[\"Xi\"])\n",
+    "    ex[\"pred\"] = pred\n",
+    "    ex.sort_values(\"Xi\").plot(\n",
+    "        x=\"Xi\", y=\"pred\", kind=\"line\", label=\"max_depth=%d\" % max_depth, ax=ax\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### min_samples_split=10\n",
+    "\n",
+    "Chaque feuille d'un arbre prédit une valeur calculée à partir d'un ensemble d'observations. Ce nombre ne peut pas être inférieur à la valeur de ce paramètre. Ce mécanisme limite la possibilité de faire du surapprentissage en augmentant la représentativité de chaque feuille."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df.plot(\n",
+    "    x=\"X\",\n",
+    "    y=\"Y\",\n",
+    "    kind=\"scatter\",\n",
+    "    figsize=(10, 6),\n",
+    "    label=\"données\",\n",
+    "    title=\"DecisionTree, min_samples_split=10\",\n",
+    ")\n",
+    "Xi = (numpy.arange(n * 10) / n * 6 / 10).reshape((n * 10, 1))\n",
+    "for max_depth in [1, 2, 3, 6, 10]:\n",
+    "    clr = DecisionTreeRegressor(max_depth=max_depth, min_samples_split=10)\n",
+    "    clr.fit(X, Y)\n",
+    "    pred = clr.predict(Xi)\n",
+    "    ex = pandas.DataFrame(Xi, columns=[\"Xi\"])\n",
+    "    ex[\"pred\"] = pred\n",
+    "    ex.sort_values(\"Xi\").plot(\n",
+    "        x=\"Xi\", y=\"pred\", kind=\"line\", label=\"max_depth=%d\" % max_depth, ax=ax\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Forest\n",
+    "\n",
+    "On étudie les deux mêmes paramètres pour une random forest à ceci près que ce modèle est une somme pondérée des résultats produits par un ensemble d'arbres de décision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### max_depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "ax = df.plot(\n",
+    "    x=\"X\", y=\"Y\", kind=\"scatter\", figsize=(10, 6), label=\"données\", title=\"RandomForest\"\n",
+    ")\n",
+    "Xi = (numpy.arange(n * 10) / n * 6 / 10).reshape((n * 10, 1))\n",
+    "for max_depth in [1, 2, 3, 6, 10]:\n",
+    "    clr = RandomForestRegressor(max_depth=max_depth)\n",
+    "    clr.fit(X, Y.ravel())\n",
+    "    pred = clr.predict(Xi)\n",
+    "    ex = pandas.DataFrame(Xi, columns=[\"Xi\"])\n",
+    "    ex[\"pred\"] = pred\n",
+    "    ex.sort_values(\"Xi\").plot(\n",
+    "        x=\"Xi\", y=\"pred\", kind=\"line\", label=\"max_depth=%d\" % max_depth, ax=ax\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### n_estimators\n",
+    "\n",
+    "*n_estimators* est le nombre d'itérations, c'est extactement le nombre d'arbres de décisions qui feront partie de la forêt dans le cas d'une régression ou d'une classification binaire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "f, axarr = plt.subplots(2, sharex=True)\n",
+    "df.plot(\n",
+    "    x=\"X\",\n",
+    "    y=\"Y\",\n",
+    "    kind=\"scatter\",\n",
+    "    figsize=(10, 6),\n",
+    "    label=\"données\",\n",
+    "    title=\"RandomForest md=2\",\n",
+    "    ax=axarr[0],\n",
+    ")\n",
+    "df.plot(\n",
+    "    x=\"X\",\n",
+    "    y=\"Y\",\n",
+    "    kind=\"scatter\",\n",
+    "    figsize=(10, 6),\n",
+    "    label=\"données\",\n",
+    "    title=\"RandomForest md=4\",\n",
+    "    ax=axarr[1],\n",
+    ")\n",
+    "Xi = (numpy.arange(n * 10) / n * 6 / 10).reshape((n * 10, 1))\n",
+    "for i, max_depth in enumerate([2, 4]):\n",
+    "    for n_estimators in [1, 2, 10]:\n",
+    "        clr = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth)\n",
+    "        clr.fit(X, Y.ravel())\n",
+    "        pred = clr.predict(Xi)\n",
+    "        ex = pandas.DataFrame(Xi, columns=[\"Xi\"])\n",
+    "        ex[\"pred\"] = pred\n",
+    "        ex.sort_values(\"Xi\").plot(\n",
+    "            x=\"Xi\",\n",
+    "            y=\"pred\",\n",
+    "            kind=\"line\",\n",
+    "            label=\"n_estimators=%d, max_depth=%d\" % (n_estimators, max_depth),\n",
+    "            ax=axarr[i],\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### min_samples_split=10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df.plot(\n",
+    "    x=\"X\", y=\"Y\", kind=\"scatter\", figsize=(10, 6), label=\"données\", title=\"RandomForest\"\n",
+    ")\n",
+    "Xi = (numpy.arange(n * 10) / n * 6 / 10).reshape((n * 10, 1))\n",
+    "for max_depth in [1, 2, 3, 6, 10]:\n",
+    "    clr = RandomForestRegressor(max_depth=max_depth, min_samples_split=10)\n",
+    "    clr.fit(X, Y.ravel())\n",
+    "    pred = clr.predict(Xi)\n",
+    "    ex = pandas.DataFrame(Xi, columns=[\"Xi\"])\n",
+    "    ex[\"pred\"] = pred\n",
+    "    ex.sort_values(\"Xi\").plot(\n",
+    "        x=\"Xi\", y=\"pred\", kind=\"line\", label=\"max_depth=%d\" % max_depth, ax=ax\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Base d'apprentissage et et base de test\n",
+    "\n",
+    "C'est un des principes de base en machine learning : ne jamais tester un modèle sur les données d'apprentissage. Avec suffisamment de feuilles, un arbre de décision apprendra la valeur à prédire pour chaque observation. Le modèle apprend le bruit. Où s'arrête l'information, où commence le bruit, il n'est pas toujours évident de fixer le curseur."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, X, Y = generate_data(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, Y, test_size=0.33, random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "min_samples_splits = [2, 5, 10]\n",
+    "rows = []\n",
+    "for max_depth in range(1, 15):\n",
+    "    d = dict(max_depth=max_depth)\n",
+    "    for min_samples_split in min_samples_splits:\n",
+    "        clr = DecisionTreeRegressor(\n",
+    "            max_depth=max_depth, min_samples_split=min_samples_split\n",
+    "        )\n",
+    "        clr.fit(X_train, y_train)\n",
+    "        pred = clr.predict(X_test)\n",
+    "        score = r2_score(y_test, pred)\n",
+    "        d[\"min_samples_split=%d\" % min_samples_split] = score\n",
+    "    rows.append(d)\n",
+    "pandas.DataFrame(rows).plot(\n",
+    "    x=\"max_depth\", y=[\"min_samples_split=%d\" % _ for _ in min_samples_splits]\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le pic sur la base de test montre que passé un certain point, la performance décroît. A ce moment précis, le modèle commence à apprendre le bruit de la base d'apprentissage. Il overfitte. On remarque aussi que le modele overfitte moins lorsque *min_samples_split=10*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>max_depth</th>\n",
+       "      <th>n_estimators</th>\n",
+       "      <th>min_samples_split</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.521566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.525835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.534421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.602039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.585866</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   max_depth  n_estimators  min_samples_split     score\n",
+       "0          1             1                  2  0.521566\n",
+       "1          1             1                  5  0.525835\n",
+       "2          1             1                 10  0.534421\n",
+       "3          2             1                  2  0.602039\n",
+       "4          2             1                  5  0.585866"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "rows = []\n",
+    "for n_estimators in range(1, 11):\n",
+    "    for max_depth in range(1, 11):\n",
+    "        for min_samples_split in [2, 5, 10]:\n",
+    "            clr = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth)\n",
+    "            clr.fit(X_train, y_train.ravel())\n",
+    "            pred = clr.predict(X_test)\n",
+    "            score = r2_score(y_test, pred)\n",
+    "            d = dict(max_depth=max_depth)\n",
+    "            d[\"n_estimators\"] = n_estimators\n",
+    "            d[\"min_samples_split\"] = min_samples_split\n",
+    "            d[\"score\"] = score\n",
+    "            rows.append(d)\n",
+    "pl = pandas.DataFrame(rows)\n",
+    "pl.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = pl[(pl.min_samples_split == 10) & (pl.n_estimators == 2)].plot(\n",
+    "    x=\"max_depth\", y=\"score\", label=\"n=2\"\n",
+    ")\n",
+    "for i in (4, 6, 8, 10):\n",
+    "    pl[(pl.min_samples_split == 10) & (pl.n_estimators == i)].plot(\n",
+    "        x=\"max_depth\", y=\"score\", label=\"n=%d\" % i, ax=ax\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "fig = plt.figure(figsize=(8, 8))\n",
+    "ax = fig.add_subplot(111, projection=\"3d\")\n",
+    "for v, c in [(2, \"b\"), (10, \"r\")]:\n",
+    "    piv = pl[pl.min_samples_split == v].pivot(\n",
+    "        index=\"n_estimators\", columns=\"max_depth\", values=\"score\"\n",
+    "    )\n",
+    "    pivX = piv.copy()\n",
+    "    pivY = piv.copy()\n",
+    "    for v in piv.columns:\n",
+    "        pivX.loc[:, v] = piv.index\n",
+    "    for v in piv.index:\n",
+    "        pivY.loc[v, :] = piv.columns\n",
+    "    ax.plot_wireframe(pivX.values, pivY.values, piv.values, color=c)\n",
+    "ax.set_xlabel(\"n_estimators\")\n",
+    "ax.set_ylabel(\"max_depth\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Réseaux de neurones\n",
+    "\n",
+    "Sur ce problème précis, les méthodes à base de gradient sont moins performantes. Elles paraissent également moins stables : la fonction d'erreur est plus agitée que celle obtenue pour les random forest. Ce type d'optimisation est plus sensible aux [extrema locaux](https://fr.wikiversity.org/wiki/Fonction_d%C3%A9riv%C3%A9e/Extremum_local)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/7 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 29%|██▊       | 2/7 [00:03<00:09,  1.83s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 43%|████▎     | 3/7 [00:07<00:10,  2.73s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 57%|█████▋    | 4/7 [00:17<00:17,  5.89s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 71%|███████▏  | 5/7 [00:24<00:12,  6.18s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 86%|████████▌ | 6/7 [00:28<00:05,  5.32s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "100%|██████████| 7/7 [00:35<00:00,  5.03s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.neural_network import MLPRegressor\n",
+    "from sklearn.metrics import r2_score\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "min_samples_splits = [2, 5, 10]\n",
+    "rows = []\n",
+    "for nb in tqdm(range(20, 300, 40)):\n",
+    "    clr = MLPRegressor(hidden_layer_sizes=(nb,), activation=\"relu\", max_iter=200)\n",
+    "    clr.fit(X_train, y_train.ravel())\n",
+    "    pred = clr.predict(X_test)\n",
+    "    score = r2_score(y_test, pred)\n",
+    "    if score > 0:\n",
+    "        d = dict(nb=nb, score=score)\n",
+    "        rows.append(d)\n",
+    "pandas.DataFrame(rows).plot(x=\"nb\", y=[\"score\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Réseaux de neurones, alpha=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/7 [00:00<?, ?it/s]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 14%|█▍        | 1/7 [00:00<00:02,  2.18it/s]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 29%|██▊       | 2/7 [00:02<00:06,  1.34s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 43%|████▎     | 3/7 [00:06<00:11,  2.80s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 57%|█████▋    | 4/7 [00:13<00:13,  4.41s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 71%|███████▏  | 5/7 [00:17<00:08,  4.24s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      " 86%|████████▌ | 6/7 [00:21<00:03,  3.95s/it]/home/xadupre/install/scikit-learn/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "100%|██████████| 7/7 [00:24<00:00,  3.51s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JUKlUOHDgABITE1FVVYXQ0FCUlZUZxkycOBGZmZmIj4/HiRMnMG7cODz33HM4duzYfWw2ERFRyxGzKxt5xZbR36k+EkEQBGNmCAwMREBAAJYvXw4A0Ov18PT0xPTp0zFnzpw7xq9cuRKLFy9GRkYGrK0bdttYQUEBXFxckJycjCFDhgAA2rRpgxUrVuDll182jGvXrh3+9a9/4bXXXrvnMouLi+Hg4ACNRgN7e/sG1UFERGTuLlwvw4hP90Cr02PVy/4I7e0mdklGMeb726gjNVqtFqmpqQgJCfljAVIpQkJCkJKSUuc88fHxCAoKgkqlgqurK3x9fbFw4ULodLq7rkej0QAAnJz+uIhp0KBBWL9+PQoLC6HX6/HDDz+goqICw4YNq3MZlZWVKC4urvUiIiJqaf7c32lEL1exy2lSRoWaa9euQafTwdW19k5xdXWFWq2uc56cnBzExcVBp9MhISEBc+fOxZIlS7BgwYI6x+v1esyYMQPBwcHw9fU1TP/xxx9RVVWFdu3aQaFQ4M0338TGjRvRrVu3OpezaNEiODg4GF6enpZ1Lz4REdG97MkqQOLpPMikEsx70jL6O9Wnye9+0uv1cHFxwapVq+Dv748JEybg3XffxcqVK+scr1KpcPLkSfzwww+1ps+dOxdFRUX49ddfceTIEURFReG5557DiRMn6lxOdHQ0NBqN4XXx4sVG3zYiIiJTVaXT44OtpwEAE4M6oburZfR3qo+VMYOdnZ0hk8mQl5dXa3peXh7c3Oo+R6dUKmFtbQ2ZTGaY5uPjA7VaDa1WC7lcbpgeERGBrVu3Ys+ePejQoYNh+tmzZ7F8+XKcPHkSvXv3BgD4+fnht99+Q0xMTJ0BSaFQQKFQGLN5REREFuOblAvIzi+FU2s5ZoR4i11OszDqSI1cLoe/vz+SkpIM0/R6PZKSkhAUFFTnPMHBwcjOzoZerzdMy8rKglKpNAQaQRAQERGBjRs3YufOnejcufaV2Tdv3qwpVlq7XJlMVmu5REREVNPfaemvWQAsr79TfYw+/RQVFYXY2FisWbMG6enpCA8PR1lZGaZMmQKg5tbr6Ohow/jw8HAUFhYiMjISWVlZ2LZtGxYuXAiVSmUYo1KpsHbtWqxbtw52dnZQq9VQq9UoLy8HAPTs2RPdunXDm2++iUOHDuHs2bNYsmQJEhMTMXbs2AfcBURERJblkx2ZKKmoRm93ezxnYf2d6mPU6ScAmDBhAgoKCjBv3jyo1Wr069cP27dvN1w8nJubW+uIiqenJ3bs2IGZM2eib9++8PDwQGRkJGbPnm0Ys2LFCgC4406m1atXY/LkybC2tkZCQgLmzJmD0aNHo7S0FN26dcOaNWvw+OOP3892ExERWaQTlzRYf+RWf6enLK+/U32Mfk6NueJzaoiIyNIJgoBnV6bgyIUbGNPPHZ8//5DYJT2wJntODREREZmu+N+v4MiFG7C1lmHOqJ5il9PsGGqIiIgsQFllNRYm3O7v1BVKB1uRK2p+DDVEREQW4IvdNf2dPJ1s8dojXcQuRxQMNURERGYu9/pNxP52DgDw7uO9YGMtu8cclomhhoiIyMwt2HYa2mo9BndzRlhvy+7vVB+GGiIiIjP225kC/HKrv9P80Zbf36k+DDVERERmqkqnx/tbavo7vTywZfR3qg9DDRERkZn69lZ/p7atrDGzhfR3qg9DDRERkRm6XlqJzwz9nXrCoVXL6O9UH4YaIiIiM/TJL1mG/k4TAlpOf6f6MNQQERGZmZOXNfjhcC4AYP7oltXfqT4MNURERGZEEAS8F38KggCM9nPHgM5OYpdkMhhqiIiIzMif+ztFt8D+TvVhqCEiIjITN7XVWJSQAQCYNqwr3B1bXn+n+jDUEBERmYkvdp2FurgCHdra4vUhLbO/U30YaoiIiMxA7vWbWPVbDgDg/57wabH9nerDUENERGQGPkyo6e8U3K0dwnq7iV2OSWKoISIiMnF7z1zDjlO3+zv1btH9nerDUENERGTCavo7nQJQ09/Ju4X3d6oPQw0REZEJW3vgAs6wv1ODMNQQERGZqOullfgssaa/09thPdjf6R4YaoiIiEzUksQsFFdUo5fSHs8HdBS7HJPHUENERGSCTl7W4PtDNf2d3nuK/Z0agqGGiIjIxAiCgPe31PR3erKvkv2dGoihhoiIyMRsOX4Vh8/fgI21FO887iN2OWaDoYaIiMiE1PR3SgcATBvWjf2djMBQQ0REZEJW7D6Lq5qa/k5vsL+TURhqiIiITMTFwpv4ck9Nf6d3H2d/J2Mx1BAREZmID7elQ1utx6Cu7TDSl/2djMVQQ0REZAL2ZV/D9lNq9nd6AAw1REREIqv+U3+nvwV2RA839ne6H/cVamJiYuDl5QUbGxsEBgbi0KFD9Y4vKiqCSqWCUqmEQqGAt7c3EhISDO8vWrQIAQEBsLOzg4uLC8aOHYvMzMw7lpOSkoLHHnsMrVu3hr29PYYMGYLy8vL72QQiIiKTsfbABWTl3ervNIL9ne6X0aFm/fr1iIqKwvz583H06FH4+fkhLCwM+fn5dY7XarUYMWIEzp8/j7i4OGRmZiI2NhYeHh6GMcnJyVCpVDhw4AASExNRVVWF0NBQlJWVGcakpKRg5MiRCA0NxaFDh3D48GFERERAKuXBJiIiMl+FZVp8equ/01uhPeDYSi5yReZLIgiCYMwMgYGBCAgIwPLlywEAer0enp6emD59OubMmXPH+JUrV2Lx4sXIyMiAtXXDGnEVFBTAxcUFycnJGDJkCABg4MCBGDFiBP75z38aU65BcXExHBwcoNFoYG9vf1/LICIiamzvbjyB7w7mwkdpj63TB7Mdwl8Y8/1t1GEOrVaL1NRUhISE/LEAqRQhISFISUmpc574+HgEBQVBpVLB1dUVvr6+WLhwIXQ63V3Xo9FoAABOTjWPhc7Pz8fBgwfh4uKCQYMGwdXVFUOHDsXevXvvuozKykoUFxfXehEREZmSU1c0WHe7v9PoXgw0D8ioUHPt2jXodDq4urrWmu7q6gq1Wl3nPDk5OYiLi4NOp0NCQgLmzp2LJUuWYMGCBXWO1+v1mDFjBoKDg+Hr62tYBgC89957eP3117F9+3Y8/PDDGD58OM6cOVPnchYtWgQHBwfDy9PT05hNJSIialKCIOD9+NMQBOCJvkoEdmkndklmr8kvSNHr9XBxccGqVavg7++PCRMm4N1338XKlSvrHK9SqXDy5En88MMPtZYBAG+++SamTJmChx56CJ999hl69OiB//73v3UuJzo6GhqNxvC6ePFi428cERHRfdp6/CoOnS9kf6dGZGXMYGdnZ8hkMuTl5dWanpeXBze3uh8SpFQqYW1tDZnsj6ci+vj4QK1WQ6vVQi7/44KoiIgIbN26FXv27EGHDh1qLQMAevXqVWvZPj4+yM3NrXO9CoUCCoXCmM0jIiJqFuVanaG/U/jQbvBgf6dGYdSRGrlcDn9/fyQlJRmm6fV6JCUlISgoqM55goODkZ2dbTjaAgBZWVlQKpWGQCMIAiIiIrBx40bs3LkTnTt3rrUMLy8vuLu733Gbd1ZWFjp16mTMJhAREYluRfJZXNFUwMPRFm8OZX+nxmL06aeoqCjExsZizZo1SE9PR3h4OMrKyjBlyhQAwMSJExEdHW0YHx4ejsLCQkRGRiIrKwvbtm3DwoULoVKpDGNUKhXWrl2LdevWwc7ODmq1Gmq12vAMGolEglmzZuHf//434uLikJ2djblz5yIjIwOvvvrqg+4DIiKiZnOx8Ca+TD4LAHj3CfZ3akxGnX4CgAkTJqCgoADz5s2DWq1Gv379sH37dsPFw7m5ubWeHePp6YkdO3Zg5syZ6Nu3Lzw8PBAZGYnZs2cbxqxYsQIAMGzYsFrrWr16NSZPngwAmDFjBioqKjBz5kwUFhbCz88PiYmJ6Nq1q7GbQEREJJqFCemorNZjYBcnjGJ/p0Zl9HNqzBWfU0NERGLbf/YaXow9CKkESIh8BD3d+H10L032nBoiIiK6P9U6Pd6PPw0A+NvATgw0TYChhoiIqBl8dzAXmXklcGxljSj2d2oSDDVERERN7Maf+zuN8GZ/pybCUENERNTEliRmQlNehZ5udnhhQEexy7FYDDVERERN6PSVYqw7eKu/01O9YSXjV29T4Z4lIiJqIoIg4L0tp6AXgCf6KDGQ/Z2aFEMNERFRE9l24ioOnSuEwkqK6Md7il2OxWOoISIiagLlWh0WbrvV32lYV3Ro20rkiiwfQw0REVETWPnn/k5D+PT75sBQQ0RE1Mgu3biJlbf6O73zuA9s5ezv1BwYaoiIiBrZ7f5OgZ2d8Hgf9ndqLgw1REREjWj/2WtIOKGGVFJzC7dEIhG7pBaDoYaIiKiRVOv0+GBLTX+nlwI7wUfJ/k7NiaGGiIiokaw7lIsMdQkcbNnfSQwMNURERI3gRpkWS3651d8p1BttW7O/U3NjqCEiImoEnyZmGfo7vcj+TqJgqCEiInpA6VeL8d3BCwCA+aPZ30ks3OtEREQPQBAEvBdf09/p8T5uCOrK/k5iYaghIiJ6AAkn1Dh4q7/TO4/7iF1Oi8ZQQ0REdJ/KtTosTKjp7zR1KPs7iY2hhoiI6D59uecsLheVw93BBlOHsr+T2BhqiIiI7sOlGzexYvet/k5PsL+TKWCoISIiug+LEjIM/Z2e6KMUuxwCQ02j2H5SjZhd2WKXQUREzSTl7HVsO3EVUknNLdzs72QarMQuwNyduKTB1LWpkEqAwd2c4efpKHZJRETUhKp1ery/5RQA4MXAjujlzv5OpoJHah5Qnw4OeMrPHXoBmPPTCVTp9GKXRERETej7P/V3emtED7HLoT9hqGkE80b3gmMra6RfLcZ/fjsndjlERNREim5qsSSxpr9T1Aj2dzI1DDWNwLmNAv/3RC8AwNJfs3D+WpnIFRERUVP4NDELRTer0MPVDi8Fsr+TqWGoaSTjH/bA4G7OqKzW452NJyAIgtglERFRI8pQF2PtgVv9nZ7qxf5OJoj/Io1EIpHgw6d9YWMtxf6z17Eh9ZLYJRERUSMRBAHvx5+GXgBG+bphUFdnsUuiOjDUNKJO7VpjRog3AODDbekoKKkUuSIiImoMP59UIyXnOvs7mbj7CjUxMTHw8vKCjY0NAgMDcejQoXrHFxUVQaVSQalUQqFQwNvbGwkJCYb3Fy1ahICAANjZ2cHFxQVjx45FZmZmncsSBAGjRo2CRCLBpk2b7qf8JvXa4M7opbSHprwKH2w9LXY5RET0gCqqdPhwW01/pzeHdIGnE/s7mSqjQ8369esRFRWF+fPn4+jRo/Dz80NYWBjy8/PrHK/VajFixAicP38ecXFxyMzMRGxsLDw8PAxjkpOToVKpcODAASQmJqKqqgqhoaEoK7vzgtulS5ea9EOOrGRS/Gt8X0glwJbfr2BXRt37hYiIzMOXyTmG/k7hw7qJXQ7VQyIYeUVrYGAgAgICsHz5cgCAXq+Hp6cnpk+fjjlz5twxfuXKlVi8eDEyMjJgbW3doHUUFBTAxcUFycnJGDJkiGF6WloannzySRw5cgRKpRIbN27E2LFj61xGZWUlKiv/OP1TXFwMT09PaDQa2Ns3/YOSFmw9jf/sPQcPR1v8MnMIWiv4nEMiInNzuagcw5fsRkWVHsteeAij/dzFLqnFKS4uhoODQ4O+v406UqPVapGamoqQkJA/FiCVIiQkBCkpKXXOEx8fj6CgIKhUKri6usLX1xcLFy6ETqe763o0Gg0AwMnJyTDt5s2bePHFFxETEwM3N7d71rpo0SI4ODgYXp6eng3dzEYRFeqNDm1tcbmoHJ/8UvepNCIiMm0LE9JRUaXHgM5OeLIv+zuZOqNCzbVr16DT6eDq6lpruqurK9RqdZ3z5OTkIC4uDjqdDgkJCZg7dy6WLFmCBQsW1Dler9djxowZCA4Ohq+vr2H6zJkzMWjQIIwZM6ZBtUZHR0Oj0RheFy9ebOBWNo5Wcit8+HQfAMDX+88j7WJRs66fiIjunyAIiNmVjW3Hb/d36mXSlz5QjSY/J6LX6+Hi4oJVq1ZBJpPB398fly9fxuLFizF//vw7xqtUKpw8eRJ79+41TIuPj8fOnTtx7NixBq9XoVBAoVA0yjbcr6He7TG2nzs2pV3BnP8dx5bpg2HN5xoQEZm0iiodZv/vODanXQEAqB7tht7uDiJXRQ1h1Dess7MzZDIZ8vLyak3Py8u76ykhpVIJb29vyGQywzQfHx+o1WpotdpaYyMiIrB161bs2rULHTp0MEzfuXMnzp49C0dHR1hZWcHKqiaLjR8/HsOGDTNmE5rd3Cd7oW0ra2SoS7BqT47Y5RARUT3yiyswYdUBbE67AiupBAvG+uKtUPZ3MhdGhRq5XA5/f38kJSUZpun1eiQlJSEoKKjOeYKDg5GdnQ29/o9Gj1lZWVAqlZDLa3pmCIKAiIgIbNy4ETt37kTnzp1rLWPOnDk4fvw40tLSDC8A+Oyzz7B69WpjNqHZtWujwNwna1oofJ50BufYQoGIyCQdv1SEp5bvw+8Xi+DYyhrfvDoAfxvYSeyyyAhGnwuJiopCbGws1qxZg/T0dISHh6OsrAxTpkwBAEycOBHR0dGG8eHh4SgsLERkZCSysrKwbds2LFy4ECqVyjBGpVJh7dq1WLduHezs7KBWq6FWq1FeXg4AcHNzg6+vb60XAHTs2PGOAGSKnn7IA490d4a2Wo93fmILBSIiU7Pl9yt4dmUK1MUV6ObSBptVwXxqsBky+pqaCRMmoKCgAPPmzYNarUa/fv2wfft2w8XDubm5kEr/yEqenp7YsWMHZs6cib59+8LDwwORkZGYPXu2YcyKFSsA4I5TSatXr8bkyZPvY7NMi0QiwYdj+yB0aTJScq5jw5FLeC6gee/GIiKiO+n1Apb+moV/78wGADzaoz3+/cJDsLNp2CNIyLQY/Zwac2XMfe5NZdWes1iYkAEHW2skRg2Bi52NKHUQERFQVlmNqB/TsONUzXWibwzpgtkje0Im5V1OpqTJnlNDD+aV4M7w9ahpofD+FrZQICISy6UbN/HMyhTsOJUHuUyKT571wzuP+zDQmDmGmmZkJZPio3F9IZNKsO34VSSl5917JiIialRHzhdibMw+pF8thnMbOb5/IxDP+He494xk8hhqmpmvhwNeHVxzcfP/bTqJ0spqkSsiImo5Nhy5iBdiD+BaqRa9lPbYHDEY/p2c7j0jmQWGGhHMDPGGp5Mtrmoq8MkOtlAgImpqOr2AD7edxqy446jSCRjl64a48CB4ONqKXRo1IoYaEdjKZVh4q4XCmpTzOJp7Q+SKiIgsV3FFFV5dcxixv50DAPx9eHfEvPgwWsnZaNjSMNSI5JHu7THuIQ8IAhD9vxPQVuvvPRMRERnl/LUyjPtiP3ZnFsDGWorlLz6EqBHekPKCYIvEUCOi/3uyF5xay5GZV4JVe86KXQ4RkUXZn30NY2L2ITu/FG72Ntjw5iA82ddd7LKoCTHUiMiptRxzn/QBAPx7ZzZyCkpFroiIyDJ8m3IeL//3EDTlVejn6Yj4iGD06cCmlJaOoUZkY/t5YIh3e2ir9Yj+6QT0+hbxLEQioiZRpdPj/zadwNzNp6DTC3j6IQ/88MZAuNjzYactAUONyGpaKPjC1lqGg+cK8eORi2KXRERklm6UaTHxq0NYeyAXEgkwe2RPfPqcH2ysZWKXRs2EocYEeDq1wluh3gCAhQnpyC+pELkiIiLzkp1fgrFf7ENKznW0lssQ+3J/hA/rComEFwS3JAw1JmLyIC/08XBAcUU13o9nCwUioobalZGPp2P248L1m+jQ1hY/TQtGSC9XscsiETDUmAgrmRQfje9T00LhxFUknmYLBSKi+giCgNg9OXhlzWGUVFZjQGcnxEcMRg83O7FLI5Ew1JiQ3u4OeO2RmhYKczedRElFlcgVERGZpspqHWbFHceHCekQBOCFAZ5Y+2ognFrLxS6NRMRQY2JmDPdGR6dWUBdXYDFbKBAR3aGgpBIvxh5EXOolSCXAe6N7YeHTfSC34ldaS8ffABPz5xYK3x64gNQLbKFARHTbqSsajFm+F6kXbsDexgprXhmAycGdeUEwAWCoMUmDuztj/MMdaloo/HScLRSIiABsP3kVz6xIwRVNBbo4t8YmVTAe6d5e7LLIhDDUmKj/e8IH7VrLkZVXipXJbKFARC2XIAj4d9IZTF17FOVVOjzS3RkbpwWjS/s2YpdGJoahxkS1bS3HvNG9AADLd2YjO58tFIio5SnX6jD9+2P4NDELADAl2AurJwfAoZW1yJWRKWKoMWFP+bljqHd7aHV6vMMWCkTUwqg1FXjuyxRsPX4V1jIJPhrXB/NH94aVjF9dVDf+ZpgwiUSCBbdaKBw6X4gfDrOFAhG1DMdyb2D08r04cVkDp9ZyrH01EM8P6Ch2WWTiGGpMnKdTK7wd1gMAsOjndOQXs4UCEVm2TccuY8KqAygoqUQPVztsVgUjsEs7scsiM8BQYwYmD/KCXwcHlFRUY378KbHLISJqEnq9gH9tz8CM9WnQVusR4uOK/00bBE+nVmKXRmaCocYMyKQSLBrXFzKpBD+fVGPHKbXYJRERNarSymq88W0qVuyuudtz2rCuWPWyP9oorESujMwJQ42Z6OVujzeGdAEAzNt8EsVsoUBEFuJi4U2M/2I/fk3Pg9xKiqUT+uEfI3tCKuUD9cg4DDVmJHJ4d3i1a4W84kp8vD1D7HKIiB7YwZzrGBOzD5l5JWhvp8CPbwZh7EMeYpdFZoqhxozYWP/RQmHtgVwcOV8ockVERPfvh0O5+NtXB1FYpkUfDwfERwSjn6ej2GWRGWOoMTODujnjWf8OAIA5P51AZbVO5IqIiIxTrdPj/S2nMOenE6jSCXiyrxI/vhkEpYOt2KWRmWOoMUPvPuED5zZyZOeXGi6qIyIyB5ryKkz5+jBW7zsPAHhrhDeWvfAQbOUycQsji8BQY4YcW8kxb3RvAMAXu84iO79E5IqIiO4tp6AUT8fsw29nrsHWWoaVf3sY04d3Z4dtajQMNWZqdF8lHu1R00Jhzv/YQoGITNtvZwowNmYfcq6Vwd3BBnHhQRjpqxS7LLIw9xVqYmJi4OXlBRsbGwQGBuLQoUP1ji8qKoJKpYJSqYRCoYC3tzcSEhIM7y9atAgBAQGws7ODi4sLxo4di8zMTMP7hYWFmD59Onr06AFbW1t07NgRf//736HRaO6nfIsgkUiw4Ok+aCWX4ciFG1h3KFfskoiI7iAIAlbvO4fJqw+juKIa/p3aYnPEYPR2dxC7NLJARoea9evXIyoqCvPnz8fRo0fh5+eHsLAw5Ofn1zleq9VixIgROH/+POLi4pCZmYnY2Fh4ePxxy15ycjJUKhUOHDiAxMREVFVVITQ0FGVlZQCAK1eu4MqVK/jkk09w8uRJfP3119i+fTteffXV+9xsy+DhaIu3Q2taKPzr5wyoNWyhQESmQ1utxzsbT+D9Laeh0wt4xr8D1r0eiPZ2CrFLIwslEQTBqPMWgYGBCAgIwPLlywEAer0enp6emD59OubMmXPH+JUrV2Lx4sXIyMiAtXXDWsUXFBTAxcUFycnJGDJkSJ1jNmzYgL/97W8oKyuDldWdT5ysrKxEZWWl4efi4mJ4enpCo9HA3t6+QXWYA51ewLgV+/H7xSKE9XbFly/3F7skIiIUlmkxdW0qDp0rhFQCRI/ywWuPdOb1M2S04uJiODg4NOj726gjNVqtFqmpqQgJCfljAVIpQkJCkJKSUuc88fHxCAoKgkqlgqurK3x9fbFw4ULodHe/Ffn2aSUnJ6d6x9jb29cZaICaU1oODg6Gl6enZ0M20ezIpBL8a3wfWEkl2HEqD9tPsoUCEYkrU12Cp5bvxaFzhbBTWOGrSQF4fUgXBhpqckaFmmvXrkGn08HV1bXWdFdXV6jVdX+Z5uTkIC4uDjqdDgkJCZg7dy6WLFmCBQsW1Dler9djxowZCA4Ohq+v713r+Oc//4k33njjrrVGR0dDo9EYXhcvXmzgVpqfnm72eHPoHy0UNOVsoUBE4kg8nYdxX+zDpRvl6NSuFX6aNgiP9nQRuyxqIZq8U5her4eLiwtWrVoFmUwGf39/XL58GYsXL8b8+fPvGK9SqXDy5Ens3bu3zuUVFxfjiSeeQK9evfDee+/ddb0KhQIKRcs5bzv9se5IOKHGuWtl+Nf2DMOTh4mImoMgCFiZnIOPd2RAEIBBXdsh5sWH0ba1XOzSqAUx6kiNs7MzZDIZ8vLyak3Py8uDm5tbnfMolUp4e3tDJvvjwUo+Pj5Qq9XQarW1xkZERGDr1q3YtWsXOnTocMeySkpKMHLkSNjZ2WHjxo0NvkanJfhzC4V1B3Nx6BxbKBBR86io0iHqx9/xr+01geblgZ2w5pUBDDTU7IwKNXK5HP7+/khKSjJM0+v1SEpKQlBQUJ3zBAcHIzs7G3q93jAtKysLSqUScnnNL7wgCIiIiMDGjRuxc+dOdO7c+Y7lFBcXIzQ0FHK5HPHx8bCxsTGm9BYhqGs7TOhfc+1Q9E/H2UKBiJpcfnEFnl91ABuPXYZMKsE/x/TGP8f6wlrGx6BR8zP6ty4qKgqxsbFYs2YN0tPTER4ejrKyMkyZMgUAMHHiRERHRxvGh4eHo7CwEJGRkcjKysK2bduwcOFCqFQqwxiVSoW1a9di3bp1sLOzg1qthlqtRnl5OYA/Ak1ZWRm++uorFBcXG8bUd8FxS/TO4z5wbqPA2YIyxOxiCwUiajonLmnw1PJ9SLtYBAdba3z7ygC8HOQldlnUghl9Tc2ECRNQUFCAefPmQa1Wo1+/fti+fbvh4uHc3FxIpX9kJU9PT+zYsQMzZ85E37594eHhgcjISMyePdswZsWKFQCAYcOG1VrX6tWrMXnyZBw9ehQHDx4EAHTr1q3WmHPnzsHLy8vYzbBYDq2s8d5TvRCx7hhW7M7Gk32V8Ha1E7ssIrIw245fxVsb0lBRpUc3lzb4z8T+8HJuLXZZ1MIZ/Zwac2XMfe7mThAEvLbmCJIy8uHfqS02vBkEqZS3UhLRg9PrBSxNOoN/J50BADzaoz0+f+Eh2NvwGkdqGk32nBoyDxKJBP8c64vWchlSL9zAdwcviF0SEVmAm9pqqNYdNQSaN4Z0wX8mBTDQkMlgqLFQ7o62mBV2q4XC9kxc1ZSLXBERmbPLReV4ZkUKfj6phlwmxeJn+uKdx30g41FgMiEMNRbs5SAv9PN0RGllNeZtPoUWcqaRiBpZ6oVCjFm+F6evFsO5jRzfvxGIZ/tb5lPaybwx1FiwmhYKfWEllSDxNFsoEJHx4lIv4YVVB3GtVAsfpT02RwyGf6e7t7AhEhNDjYXr4WaH8GFdAQDz4k+xhQIRNYhOL2BhQjre3vA7tDo9RvZ2Q9zUIHg42opdGtFdMdS0AKpHu6FL+9YoKKnERz9niF0OEZm4kooqvLbmMFbtyQEA/P2xbvjipYfRWtHknXWIHghDTQtgYy3DolstFL4/lIsDOddFroiITNWF62UY98V+7MosgMJKimUvPISo0B58LASZBYaaFiKwSzu8MKDmwr53fjqBiio+iZmIatt/9hrGxOzDmfxSuNorsGFqEEb7uYtdFlGDMdS0IHNG+aC9nQI518oQsytb7HKIyISsPXABE786hKKbVfDzdER8xGD07eAodllERmGoaUEcbK3x/lO9AQArdp9FprpE5IqISGxVOj3mbT6J/9t0EtV6AWP7uWP9GwPhas+mwWR+GGpamFG+bgjxcUW1XsCcn45Dp+eza4haqqKbWkxefQjfpFyARAL8Y2QPfDahH2ysZWKXRnRfGGpamJoWCr3RRmGFY7lFWHuALRSIWqLs/BKMjdmHfdnX0Vouw6qX+2PasG6QSHhBMJkvhpoWSOlgi3+MrGmh8PH2DFwpYgsFopZkV2Y+no7Zj/PXb6JDW1v8b9ogjOjlKnZZRA+MoaaF+ltgJzzc0RFlWh3mbT7JFgpELcSerAK8+vVhlFRWY4CXEzargtHTrf7Ox0TmgqGmhZJKJfhofF9YyyT4NT0fCSfYQoHI0ulvPSVYLwBP+blj7WuBaNdGIXZZRI2GoaYF83a1Q/iwbgCA+fGnoLnJFgpElmzbiavIUJfATmGFD8b0htyKXwFkWfgb3cKpHu2Kru1b41ppJRb9nC52OUTURKp1enyWmAUAeH1IFzi2kotcEVHjY6hp4RRWMnw0vi8A4IfDF5Fyli0UiCzRT8cuI+daGdq2ssYrgzuLXQ5Rk2CoIQR4OeHFwI4AgHc2soUCkaWprNbh81/PAADCh3VFGzamJAvFUEMAgDmjesLFToFz18qwbOcZscshokb04+GLuFxUDhc7BSYGeYldDlGTYaghAIC9jTU+GFPTQuHL5BykXy0WuSIiagzlWh2W7azp9Tb9sW58WjBZNIYaMhjpq0Ror9stFE6whQKRBfj2wHnkl1TCw9EWEwI6il0OUZNiqKFaPhjjCzuFFX6/WIRvUs6LXQ4RPYDSymqs2H0WABAZ0p23cJPF42841eLmYIN/jOoJAFi8IxOX2UKByGz9d+853LhZhS7tW2PcQx5il0PU5Bhq6A4vDeiI/p3a4qZWh7mb2EKByBwV3dQidk8OAGBmiDesZPy4J8vH33K6g1QqwaJxfWAtk2BnRj62Hr8qdklEZKQv9+SgpLIaPd3s8EQfpdjlEDULhhqqU3dXO0y71ULh/S2nUHRTK3JFRNRQBSWV+HrfeQDAW6E9IJVKxC2IqJkw1NBdTXu0K7q5tMG1Ui0WJrCFApG5+GJ3NsqrdPDzdESIj4vY5RA1G4YauiuFlQwfjesDAPjxyCXsP3tN5IqI6F6uFJXjuwO5AIBZoT0gkfAoDbUcDDVUr/5eTvjbwFstFH5iCwUiU7ds5xlodXoEdnZCcLd2YpdD1KwYauie/jGyJ1ztFTh//Sb+ncQWCkSm6vy1Mvx45BIAYFYYj9JQy3NfoSYmJgZeXl6wsbFBYGAgDh06VO/4oqIiqFQqKJVKKBQKeHt7IyEhwfD+okWLEBAQADs7O7i4uGDs2LHIzMystYyKigqoVCq0a9cObdq0wfjx45GXl3c/5ZORaloo+AKouaPi9BW2UCAyRZ8nnYFOL2BYj/bo7+UkdjlEzc7oULN+/XpERUVh/vz5OHr0KPz8/BAWFob8/Pw6x2u1WowYMQLnz59HXFwcMjMzERsbCw+PPx4ElZycDJVKhQMHDiAxMRFVVVUIDQ1FWVmZYczMmTOxZcsWbNiwAcnJybhy5QrGjRt3H5tM9yOstxtG9naDTi8g+qfjbKFAZGKy8kqwKe0yAOCtET1EroZIHBLByCerBQYGIiAgAMuXLwcA6PV6eHp6Yvr06ZgzZ84d41euXInFixcjIyMD1tbWDVpHQUEBXFxckJycjCFDhkCj0aB9+/ZYt24dnnnmGQBARkYGfHx8kJKSgoEDB96xjMrKSlRWVhp+Li4uhqenJzQaDezt7Y3ZZLolr7gCIZ8mo6SiGnOf7IVXB3cWuyQiumXqt6nYfkqNkb3dsPJlf7HLIWo0xcXFcHBwaND3t1FHarRaLVJTUxESEvLHAqRShISEICUlpc554uPjERQUBJVKBVdXV/j6+mLhwoXQ6e5+walGowEAODnVHD5NTU1FVVVVrfX27NkTHTt2vOt6Fy1aBAcHB8PL09PTmE2lOrja22DOrRYKS37JxKUbN0WuiIgA4MQlDbafUkMiAaJCvcUuh0g0RoWaa9euQafTwdXVtdZ0V1dXqNXqOufJyclBXFwcdDodEhISMHfuXCxZsgQLFiyoc7xer8eMGTMQHBwMX9+a6zjUajXkcjkcHR0bvN7o6GhoNBrD6+LFi8ZsKt3FCwEdMcDLCTe1OvwfWygQmYQliTXXII7t5wFvVzuRqyEST5Pf/aTX6+Hi4oJVq1bB398fEyZMwLvvvouVK1fWOV6lUuHkyZP44YcfHmi9CoUC9vb2tV704KRSCRaO6wO5TIrdmQWI//2K2CURtWhHzhdid2YBZFIJIod3F7scIlEZFWqcnZ0hk8nuuOsoLy8Pbm5udc6jVCrh7e0NmUxmmObj4wO1Wg2ttvaj9yMiIrB161bs2rULHTp0MEx3c3ODVqtFUVFRg9dLTaebSxuoHq1pofDBltO4UcYWCkRiEAQBi3fUHKV5rn8HeDm3FrkiInEZFWrkcjn8/f2RlJRkmKbX65GUlISgoKA65wkODkZ2djb0er1hWlZWFpRKJeRyOYCaP8yIiAhs3LgRO3fuROfOtS9A9ff3h7W1da31ZmZmIjc3967rpaYVPqwruru0wfUyLT5kCwUiUezLvo6D5wohl0kx/TEepSEy+vRTVFQUYmNjsWbNGqSnpyM8PBxlZWWYMmUKAGDixImIjo42jA8PD0dhYSEiIyORlZWFbdu2YeHChVCpVIYxKpUKa9euxbp162BnZwe1Wg21Wo3y8nIAgIODA1599VVERUVh165dSE1NxZQpUxAUFFTnnU/U9ORWUnw0vi8kEiAu9RL2ZbOFAlFzEgQBi3+pOUrz0sCOcHe0FbkiIvFZGTvDhAkTUFBQgHnz5kGtVqNfv37Yvn274eLh3NxcSKV/ZCVPT0/s2LEDM2fORN++feHh4YHIyEjMnj3bMGbFihUAgGHDhtVa1+rVqzF58mQAwGeffQapVIrx48ejsrISYWFh+OKLL4wtnxqRf6e2eHlgJ3yTcgHvbDyBHTOGwMZadu8ZieiB/Zqej98vFsHWWoZpw7qJXQ6RSTD6OTXmypj73KnhSiqqMOLTPVAXV2Dq0K6GW76JqOno9QIe//dvyFCXIHxYV8weyb87slxN9pwaor+ys7HGP8fW3Hof+1sOTl3RiFwRkeXbduIqMtQlsFNY4c0hXcQuh8hkMNTQAxvRyxWP96lpoTDnfydQrdPfeyYiui/VOj0+S8wCALw+pAscW8lFrojIdDDUUKN4b3Rv2NlY4cRlDb7ef17scogs1k/HLiPnWhnatrLGlGAvscshMikMNdQoXOxt8M7jPgCAJb9k4WIhWygQNTZttR6f/3oGQM1jFexsGtZPj6ilYKihRjOhvycGdHZCeZUO77KFAlGjW384F5eLyuFip8DLA73ELofI5DDUUKORSiVYNK4P5FZS7MkqwOY0tlAgaizlWh2W7cwGAEQ81g22cj4+geivGGqoUXVt3wbTb7dQ2HoahWyhQNQovj1wHvkllfBwtMXzAR3FLofIJDHUUKN7c2hX9HC1Q2GZFgu2nRa7HCKzV1pZjRW7zwIAIkO6Q27Fj26iuvAvgxqd3EqKReP7QCIBfjp6Gb+dKRC7JCKz9t+953DjZhW6OLfGuIc8xC6HyGQx1FCTeLhjW0wK8gIAvLvxJMq1OnELIjJTRTe1iN2TAwCYMcIbVjJ+bBPdDf86qMm8HdYD7g42yC28iaW/ZoldDpFZ+nJPDkoqq9HTzQ5P9lGKXQ6RSWOooSbTRmFlaKHwn73ncPIyWygQGaOgpBJf7zsPAHgrtAekUom4BRGZOIYaalLDfVzxRF9lTQuFn46zhQKREb7YnY3yKh38PB0R4uMidjlEJo+hhprc/NG9YG9jhZOXi/HffefELofILFwpKsd3B3IBAG+HekMi4VEaonthqKEm52Jng3efqGmh8GliFnKvs4UC0b0s25kNrU6PwM5OGNzNWexyiMwCQw01i+f6e2JgFydUVOnx7qYT0OvZQoHobi5cL8OGIxcB1Fxwz6M0RA3DUEPNQiKRYNG4vpBbSfHbmWt4c20qSiurxS6LyCQt/fUMqvUChnq3R4CXk9jlEJkNhhpqNp2dW+OTZ/0gt5Ii8XQexn2xDxeul4ldFpFJycorwaa0ywCAt0N7iFwNkXlhqKFm9ZSfO9a/MRAudgpk5ZXiqeX7sPfMNbHLIjIZnyVmQRCAkb3d0KeDg9jlEJkVhhpqdg91bIst0wfDz9MRmvIqTFp9CP/dew6CwOtsqGU7eVmDn0+qIZEAUaHeYpdDZHYYakgUrvY2WP/GQIx72AM6vYAPtp7GP+KOo7Ka7RSo5frkl0wAwBg/d3i72olcDZH5Yagh0dhYy7DkWT/83xM+kEqADamX8PyqA8gvrhC7NKJmd+R8IXZnFkAmlWBGCI/SEN0PhhoSlUQiwWuPdMHXUwbA3sYKx3KL8NTyffj9YpHYpRE1G0EQsHhHzVGa5/p3gJdza5ErIjJPDDVkEoZ4t8fmiMHo5tIG6uIKPPtlCjYeuyR2WUTNYl/2dRw8Vwi5TIrpj3UXuxwis8VQQyajs3NrbJw2CCE+LtBW6zFz/e9YmJAOHR/URxZMEAQsvnUtzYuBHeHuaCtyRUTmi6GGTIqdjTVWvdwfqke7AgBW7cnBK18fhqa8SuTKiJrGr+n5+P1iEWytZVA92k3scojMGkMNmRypVIJZYT2x7IWHYGMtRXJWAZ6O2Yfs/FKxSyNqVHq9gCW3jtJMDvZCezuFyBURmTeGGjJZo/3cETd1ENwdbJBzrQxPx+zDrox8scsiajTbTlxFhroEdgorvDmki9jlEJk9hhoyab4eDoifPhgBXm1RUlmNV9YcxordZ/mgPjJ71To9PkvMAgC89kgXOLaSi1wRkfljqCGT59xGge9eG4gXBnSEIAD/2p6ByB/SUK7lg/rIfP107DJyrpWhbStrvDLYS+xyiCwCQw2ZBbmVFIvG9cE/x/rCSipB/O9X8OyX+3GlqFzs0oiMpq3W4/NfzwAAwod1hZ2NtcgVEVmG+wo1MTEx8PLygo2NDQIDA3Ho0KF6xxcVFUGlUkGpVEKhUMDb2xsJCQmG9/fs2YPRo0fD3d0dEokEmzZtumMZpaWliIiIQIcOHWBra4tevXph5cqV91M+mbGXB3bC2tcC4dRajpOXi/HU8n04cr5Q7LKIjLL+cC4uF5WjvZ0CLw/0ErscIothdKhZv349oqKiMH/+fBw9ehR+fn4ICwtDfn7dF3BqtVqMGDEC58+fR1xcHDIzMxEbGwsPDw/DmLKyMvj5+SEmJuau642KisL27duxdu1apKenY8aMGYiIiEB8fLyxm0BmbmCXdtisCkZPNztcK63EC7EH8MOhXLHLImqQcq0Oy3ZmAwCmP9YNtnKZyBURWQ6JYOQVl4GBgQgICMDy5csBAHq9Hp6enpg+fTrmzJlzx/iVK1di8eLFyMjIgLX1vQ+xSiQSbNy4EWPHjq013dfXFxMmTMDcuXMN0/z9/TFq1CgsWLDgjuVUVlaisrLS8HNxcTE8PT2h0Whgb2/f0M0lE3ZTW423N/yOhBNqAMCkoE74vyd7wVrGs6pkumL35ODDhHR4ONpi19vDILfi7ytRfYqLi+Hg4NCg72+j/pq0Wi1SU1MREhLyxwKkUoSEhCAlJaXOeeLj4xEUFASVSgVXV1f4+vpi4cKF0OmMu8hz0KBBiI+Px+XLlyEIAnbt2oWsrCyEhobWOX7RokVwcHAwvDw9PY1aH5m+VnIrxLz4MKJG1DT/W5NyARO/OoQbZVqRKyOqW2llNVYknwUARIZ0Z6AhamRG/UVdu3YNOp0Orq6utaa7urpCrVbXOU9OTg7i4uKg0+mQkJCAuXPnYsmSJXUeXanPsmXL0KtXL3To0AFyuRwjR45ETEwMhgwZUuf46OhoaDQaw+vixYtGrY/Mg0Qiwd+Hd8eql/3RWi5DSs51PBWzFxnqYrFLI7rDf/eeQ2GZFl2cW2PcQx73noGIjNLk/5ug1+vh4uKCVatWwd/fHxMmTMC7775r9EW+y5Ytw4EDBxAfH4/U1FQsWbIEKpUKv/76a53jFQoF7O3ta73IcoX2dsNP04LR0akVLhaWY9wX+7H9ZN1Bm0gMRTe1iN2TAwCYMcIbVjxNStTojPqrcnZ2hkwmQ15eXq3peXl5cHNzq3MepVIJb29vyGR/XAzn4+MDtVoNrbZhpwnKy8vxzjvv4NNPP8Xo0aPRt29fREREYMKECfjkk0+M2QSyYD3c7LBZFYzgbu1wU6vD1LWpWPprFvRsiEkmYNWeHJRUVqOnmx2e7KMUuxwii2RUqJHL5fD390dSUpJhml6vR1JSEoKCguqcJzg4GNnZ2dDr9YZpWVlZUCqVkMsb9gTNqqoqVFVVQSqtXa5MJqu1XKK2reVYM2UApgR7AQCW/noG0747irLKanELoxatoKQSq/edBwC8FdoDUqlE3IKILJTRxz+joqIQGxuLNWvWID09HeHh4SgrK8OUKVMAABMnTkR0dLRhfHh4OAoLCxEZGYmsrCxs27YNCxcuhEqlMowpLS1FWloa0tLSAADnzp1DWloacnNrbtO1t7fH0KFDMWvWLOzevRvnzp3D119/jW+++QZPP/30g2w/WSArmRTzR/fGx8/0hVwmxfZTaoxfsR8XC2+KXRq1UF/szkZ5lQ5+no4I8XERuxwiyyXch2XLlgkdO3YU5HK5MGDAAOHAgQOG94YOHSpMmjSp1vj9+/cLgYGBgkKhELp06SJ8+OGHQnV1teH9Xbt2CQDueP15OVevXhUmT54suLu7CzY2NkKPHj2EJUuWCHq9vkE1azQaAYCg0WjuZ5PJTB05Xyj4/zNR6DR7q9Dv/R3CvuwCsUuiFubyjZtC93cShE6ztwp7svLFLofI7Bjz/W30c2rMlTH3uZNluaopx5vfpuL4JQ1kUgnmPdkLE4M6QSLhKQBqetE/ncD3h3IR2NkJP7wxkL93REZqsufUEJkjpYMtfnwzCGP7uUOnFzA//hSifzoBbTWvx6KmdeF6GTYcqXmcxNthPRhoiJoYQw21CDbWMnw2oR/eebwnpBLgh8MX8WLsARSUVN57ZqL7tPTXM6jWCxjq3R4BXk5il0Nk8RhqqMWQSCR4Y0hX/HdyAOxsrHDkwg08tXwvTlzSiF0aWaCsvBJsSrsMAHg7tIfI1RC1DAw11OIM6+GCTapgdGnfGlc1FXhm5X5svvXlQ9RYPkvMgiAAI3u7oU8HB7HLIWoRGGqoReravg02qYLxaI/2qKzWI/KHNPxrewZ0fFAfNYKTlzX4+aQaEgkQFeotdjlELQZDDbVY9jbW+M+kAEwd2hUAsGL3Wbz+zREUV1SJXBmZu09+yQQAjPFzh7erncjVELUcDDXUosmkEswZ1ROfP98PCispdmbk4+mYfcgpKBW7NDJTR84XYndmAWRSCWaE8CgNUXNiqCECMKafB+KmDoLSwQZnC8owJmYfdmfmi10WmRlBELB4R81Rmuf6d4CXc2uRKyJqWRhqiG7p08EBmyOC4d+pLUoqqvHK14exas9ZtJDnU1Ij2Jd9HQfPFUIukyLise5il0PU4jDUEP2Ji50N1r0eiAn9PaEXgIUJGYj68XdUVOnELo1MnCAIWHzrWpoXAzvCw9FW5IqIWh6GGqK/UFjJ8NH4Pnj/qd6QSSXYeOwyJnyZArWmQuzSyIQlpefj94tFsLWWYdqjXcUuh6hFYqghqoNEIsGkQV749pUBcGxljd8vaTB6+V4czb0hdmlkgvR6wXDH06RBXnCxsxG5IqKWiaGGqB6DujkjXjUYPVztUFBSiee/PGDo5UN027YTV5GhLoGdwgpTh3YRuxyiFouhhugeOrZrhZ+mDUJYb1dodXrMijuO97ecQrWODTEJqNbp8VliFgDgtUe6wLGVXOSKiFouhhqiBmitsMKKl/wRObzmjpbV+85j8urDKLqpFbkyEtvGY5eRc60MbVtZ45XBXmKXQ9SiMdQQNZBUKsHMEd5Y8dLDaCWXYW/2NYyJ2YesvBKxSyORaKv1+DzpDABg6tCusLOxFrkiopaNoYbISKP6KPG/8EHo0NYWF67fxNMx+/DLKbXYZZEI1h/OxaUb5Whvp8DEIC+xyyFq8RhqiO6Dj9Ie8RGDMbCLE8q0OrzxbSqWJZ3hg/pakHKtDst2ZgMApj/WDbZymcgVERFDDdF9cmotx7evBmJSUCcAwJLELESsO4ab2mqRK6PmsPbABeSXVMLD0RYTAjzFLoeIwFBD9ECsZVK8P8YXi8b1gbVMgm0nrmL8ihRcunFT7NKoCZVWVmNF8lkAQOTw7lBY8SgNkSlgqCFqBC8M6Ih1rw+Ecxs50q8W46nl+3Aw57rYZVET+e/ecygs06KLc2uMe9hD7HKI6BaGGqJGEuDlhM0Rg+HrYY/CMi1e+s9BrD1wQeyyqJEV3dQidk8OAGDGCG9YyfgxSmQq+NdI1Ig8HG2x4c1BGO3njmq9gP/bdBLvbDwBbTUf1GcpVu3JQUllNXq62eHJPkqxyyGiP2GoIWpktnIZ/v18P/xjZA9IJMC6g7n4238O4lpppdil0QMqKKnE6n3nAQBRI7whlUrELYiIamGoIWoCEokE04Z1w1eT+sNOYYVD5wsxZvk+nLqiEbs0egBf7M5GeZUOfp6OGNHLVexyiOgvGGqImtBjPV2xUTUInZ1b43JROcav2I+tx6+IXRbdhytF5fjuQC4A4O1Qb0gkPEpDZGoYaoiaWDcXO2yaFowh3u1RUaVHxLpj+GRHJvR6PqjPnCzbmQ2tTo8BnZ0wuJuz2OUQUR0YaoiagUMra6yeHIA3hnQBACzflY03vj2CkooqkSujhrhwvQwbjlwEAMwK68GjNEQmiqGGqJnIpBK887gPPn3OD3IrKX5Nz8e4L/bj/LUysUuje1j66xlU6wUM9W6PAC8nscshortgqCFqZuMe7oAf3wyCq70CZ/JLMSZmH347UyB2WXQXZ/JKsCntMgDg7dAeIldDRPW5r1ATExMDLy8v2NjYIDAwEIcOHap3fFFREVQqFZRKJRQKBby9vZGQkGB4f8+ePRg9ejTc3d0hkUiwadOmOpeTnp6Op556Cg4ODmjdujUCAgKQm5t7P5tAJKp+no7YEjEYD3V0hKa8CpP+ewj/+S2HDTFN0KeJWRAEIKy3K/p0cBC7HCKqh9GhZv369YiKisL8+fNx9OhR+Pn5ISwsDPn5+XWO12q1GDFiBM6fP4+4uDhkZmYiNjYWHh5/PFq8rKwMfn5+iImJuet6z549i8GDB6Nnz57YvXs3jh8/jrlz58LGxsbYTSAyCS72Nvj+9YF4xr8D9AKwYFs63t5wHBVVOrFLo1tOXtbg55NqSCTAWzxKQ2TyJIKR/2sYGBiIgIAALF++HACg1+vh6emJ6dOnY86cOXeMX7lyJRYvXoyMjAxYW1vfuyCJBBs3bsTYsWNrTX/++edhbW2Nb7/91phyDYqLi+Hg4ACNRgN7e/v7WgZRUxAEAf/ddx4fbjsNvVBzFGfVy/5wsWdgF9vk1YewO7MAY/u5Y+nzD4ldDlGLZMz3t1FHarRaLVJTUxESEvLHAqRShISEICUlpc554uPjERQUBJVKBVdXV/j6+mLhwoXQ6Rr+f6N6vR7btm2Dt7c3wsLC4OLigsDAwLuepgKAyspKFBcX13oRmSKJRIJXB3fGN68EwsHWGmkXizB6+V6kXSwSu7QW7cj5QuzOLIBMKsGMEG+xyyGiBjAq1Fy7dg06nQ6urrWfpOnq6gq1Wl3nPDk5OYiLi4NOp0NCQgLmzp2LJUuWYMGCBQ1eb35+PkpLS/HRRx9h5MiR+OWXX/D0009j3LhxSE5OrnOeRYsWwcHBwfDy9PRs+IYSiWBwd2dsVgWju0sb5BVX4rkvU/DT0Util9UiCYKAT37JBAA8698BXs6tRa6IiBqiye9+0uv1cHFxwapVq+Dv748JEybg3XffxcqVK41aBgCMGTMGM2fORL9+/TBnzhw8+eSTd11OdHQ0NBqN4XXx4sVG2R6ipuTl3Bo/TRuEEB9XaKv1iPrxd7z81UH8zqM2zWpf9nUcyCmEXCbF9OHdxS6HiBrIqFDj7OwMmUyGvLy8WtPz8vLg5uZW5zxKpRLe3t6QyWSGaT4+PlCr1dBqtQ1er5WVFXr16lVruo+Pz13vflIoFLC3t6/1IjIHdjbWWPWyPyKHd4eVVILfzlzDmJh9eP2bI8hQ8zRqUxMEAYtvHaV5MbAjPBxtRa6IiBrKqFAjl8vh7++PpKQkwzS9Xo+kpCQEBQXVOU9wcDCys7MNR1sAICsrC0qlEnK5vMHrDQgIQGZmZq3pWVlZ6NSpkzGbQGQWpFIJZo7wxq63h2H8wx0glQCJp/Mw6vPfEPnDMT6wrwklpefj94tFsLGWYtqjXcUuh4iMYPTpp6ioKMTGxmLNmjVIT09HeHg4ysrKMGXKFADAxIkTER0dbRgfHh6OwsJCREZGIisrC9u2bcPChQuhUqkMY0pLS5GWloa0tDQAwLlz55CWllbrKMysWbOwfv16xMbGIjs7G8uXL8eWLVswbdq0+912IpPn6dQKS57zwy8zh+DxPm4QBGBz2hUM/zQZc/53HJeLysUu0aLo9X9cSzN5UGe42PEONCKzItyHZcuWCR07dhTkcrkwYMAA4cCBA4b3hg4dKkyaNKnW+P379wuBgYGCQqEQunTpInz44YdCdXW14f1du3YJAO54/XU5X331ldCtWzfBxsZG8PPzEzZt2tTgmjUajQBA0Gg097PJRCbhxKUiYfJ/DwqdZm8VOs3eKnR/J0GYv/mkkF9cIXZpFiE+7bLQafZWwXfeduFGWaXY5RCRYNz3t9HPqTFXfE4NWZIj5wvxyS+ZOJBTCACwtZZhcrAX3hzSBY6tGnZal2qr1ukR+tke5Fwrw8wQb0SG8AJhIlPQZM+pISLT0N/LCd+/PhBrXw2En6cjyqt0WLH7LB75eBeWJZ1BaWW12CWanY3HLiPnWhnatrLGK4O9xC6HiO4DQw2RmZJIJBjc3Rmbpg1C7MT+6Olmh5KKaixJzMKQj3fhP7/lsOVCA2mr9fg86QwAYOrQrrCzuffTz4nI9DDUEJk5iUSCEb1ckfD3R/D58/3Q2bk1Csu0WLAtHUMX78LaAxegrdbfe0Et2PrDubh0oxzt7RSYGOQldjlEdJ8YaogshFQqwZh+HkicOQT/Gt8H7g42yCuuxP9tOonhn+7G/1IvQadvEZfQGaWiSodlO7MBANMf6wZbuewecxCRqWKoIbIwVjIpJgR0xK5Zw/De6F5wbqPAxcJyvLXhd4xcugc/n7iKFnJ/QIN8m3IB+SWV8HC0xYQAtlMhMmcMNUQWSmElw+Tgztjzj2H4x8gecLC1xpn8UoR/dxSjl+/Frsz8Fh9uSiursSL5LAAgcnh3KKx4lIbInDHUEFm4VnIrTBvWDXv+8Sj+/lg3tJbLcPJyMaasPoxnV6bgQM51sUsUzX/3nkNhmRadnVtj3MMeYpdDRA+IoYaohXCwtUZUaA/s+cejeP2RzpBbSXHkwg08v+pAi2yaWXRTi9g9OQCAGSHdYSXjxyGRueNfMVEL066NAu8+0Qt7Zj2KlwI71mqa+cY3R5CpLhG7xGaxak8OSiqr0dPNDqP7uotdDhE1AoYaohbKzcEGHz7dBzvfGoZxD3tAKgF+OZ2HkZ/vsfimmQUllVi97zwAIGqEN6RSibgFEVGjYKghauE6tmuFT5/rd9emmVcssGnmF7uzUV6lg18HB4zo5Sp2OUTUSBhqiAgA0M3FDl+85I+t0wfj0R7todML+OHwRQxbvBvvxZ9CQUml2CU2iitF5fjuQC4A4K3QHpBIeJSGyFIw1BBRLb4eDlg9ZQDipgYhsLMTtDo9vt5/HkM+3oWPt2dAc7NK7BIfyLKd2dDq9BjQ2QmPdHcWuxwiakQMNURUp/5eTvjhjYH49tUB8OvggPIqHb7YfRaDP95ptk0zL1wvw4YjFwEAs8J4lIbI0jDUENFdSSQSPNK9PTapgrHqZX/0cDXvpplLfz2Dar2Aod7tEeDlJHY5RNTIGGqI6J4kEglCe7vh58iapple7VoZmmYOW7wb3x28gCqdaTfNPJNXgk1plwEAb4V6i1wNETUFhhoiajBD08yoofhoXE3TTHVxBd7deBLDlyTjp6Om2zTz08QsCAIQ1tsVfTs4il0OETUBhhoiMpq1TIrnB3TEzreHYf7oXnBuI0du4U1E/WiaTTNPXtbg55NqSCRA1IgeYpdDRE2EoYaI7puNtQxTgjtjzz8eNemmmUt+yQQAPOXnjh5udiJXQ0RNhaGGiB7Yn5tmTv9L08znvkzBQRGbZqZeKMSuzALIpBLMDOG1NESWjKGGiBqNg6013rrVNPO1wTVNMw+fv4EJIjXNFAQBi3fUHKV51r8DvJxbN+v6iah5MdQQUaNr10aB/3tS/KaZ+7Kv40BOIeQyKaYP794s6yQi8TDUEFGTEbNppiAI+OTWtTQvBnaEh6Ntk62LiEwDQw0RNbnbTTN3zBiCUb61m2ZG/9Q0TTOT0vORdrEINtZSTHu0a6Mvn4hMD0MNETWb7q52WPG3mqaZw241zfz+UE3TzPe3NF7TTL3+j6M0kwd1houdTaMsl4hMG0MNETU7Xw8HfD1lADZMDcKAW00zV+9rvKaZ205cRYa6BHYKK0wd2qWRqiYiU8dQQ0SiCfBywvo3BuKbVxqvaWa1To/Pfs0CALz6SGc4tpI3dtlEZKIYaohIVBKJBEO8a5pmfvmXpplD76Np5sZjl5FTUIa2razx6uDOTVg5EZkahhoiMgkSiQRhvd2Q8KemmdeNbJqprdbj86QzAICpQ7vCzsa6OUonIhPBUENEJkX2l6aZyr80zdx47O5NM9cfzsWlG+Vob6fAxCCv5i2ciETHUENEJul208xdbw/DvCf/aJo5c31N08ztJ2s3zayo0mHZzmwAQMSj3WArl4lVOhGJ5L5CTUxMDLy8vGBjY4PAwEAcOnSo3vFFRUVQqVRQKpVQKBTw9vZGQkKC4f09e/Zg9OjRcHd3h0QiwaZNm+pd3tSpUyGRSLB06dL7KZ+IzIiNtQyvDK5pmjkrrAfsbaxwJr8UU9cexVPL92H3raaZ36ZcQH5JJTwcbfH8AE+xyyYiEVgZO8P69esRFRWFlStXIjAwEEuXLkVYWBgyMzPh4uJyx3itVosRI0bAxcUFcXFx8PDwwIULF+Do6GgYU1ZWBj8/P7zyyisYN25cvevfuHEjDhw4AHd3d2NLJyIz1kpuBdWj3fC3gZ3wn99y8NXeczhxWYPJqw8jwKstzhbUPJ04cnh3KKx4lIaoJZIIfz5+2wCBgYEICAjA8uXLAQB6vR6enp6YPn065syZc8f4lStXYvHixcjIyIC19b0v2pNIJNi4cSPGjh17x3uXL19GYGAgduzYgSeeeAIzZszAjBkzGlR3cXExHBwcoNFoYG9v36B5iMh0XS+txIrdZ/HNgQvQVtdcQNzZuTUSZw6BlYxn1okshTHf30b95Wu1WqSmpiIkJOSPBUilCAkJQUpKSp3zxMfHIygoCCqVCq6urvD19cXChQuh0zX8Fk2gJjy9/PLLmDVrFnr37n3P8ZWVlSguLq71IiLLcbtpZvKsYXgxsCM6tLXFB2N6M9AQtWBGnX66du0adDodXF1da013dXVFRkZGnfPk5ORg586deOmll5CQkIDs7GxMmzYNVVVVmD9/foPX/a9//QtWVlb4+9//3qDxixYtwvvvv9/g5ROReVI62GLh033ELoOITIDR19QYS6/Xw8XFBatWrYJMJoO/vz8uX76MxYsXNzjUpKam4vPPP8fRo0chkUgaNE90dDSioqIMPxcXF8PTkxcPEhERWSqjjtM6OztDJpMhLy+v1vS8vDy4ubnVOY9SqYS3tzdksj8u3PPx8YFarYZWq23Qen/77Tfk5+ejY8eOsLKygpWVFS5cuIC33noLXl5edc6jUChgb29f60VERESWy6hQI5fL4e/vj6SkJMM0vV6PpKQkBAUF1TlPcHAwsrOzodf/8STQrKwsKJVKyOUN68ny8ssv4/jx40hLSzO83N3dMWvWLOzYscOYTSAiIiILZfTpp6ioKEyaNAn9+/fHgAEDsHTpUpSVlWHKlCkAgIkTJ8LDwwOLFi0CAISHh2P58uWIjIzE9OnTcebMGSxcuLDWtTGlpaXIzs42/Hzu3DmkpaXByckJHTt2RLt27dCuXbtadVhbW8PNzQ09evS4rw0nIiIiy2J0qJkwYQIKCgowb948qNVq9OvXD9u3bzdcPJybmwup9I8DQJ6entixYwdmzpyJvn37wsPDA5GRkZg9e7ZhzJEjR/Doo48afr59LcykSZPw9ddf3++2ERERUQti9HNqzBWfU0NERGR+muw5NURERESmiqGGiIiILAJDDREREVkEhhoiIiKyCAw1REREZBEYaoiIiMgiMNQQERGRRWCoISIiIovQ5F26TcXtZwwWFxeLXAkRERE11O3v7YY8K7jFhJqSkhIANW0biIiIyLyUlJTAwcGh3jEtpk2CXq/HlStXYGdnB4lEInY5ZqW4uBienp64ePEiW0w0I+53cXC/i4P7XRzmsN8FQUBJSQnc3d1r9ZasS4s5UiOVStGhQwexyzBr9vb2JvtLb8m438XB/S4O7ndxmPp+v9cRmtt4oTARERFZBIYaIiIisggMNXRPCoUC8+fPh0KhELuUFoX7XRzc7+LgfheHpe33FnOhMBEREVk2HqkhIiIii8BQQ0RERBaBoYaIiIgsAkMNERERWQSGGjJ47733IJFIar169uxpeL+iogIqlQrt2rVDmzZtMH78eOTl5YlYsXnas2cPRo8eDXd3d0gkEmzatKnW+4IgYN68eVAqlbC1tUVISAjOnDlTa0xhYSFeeukl2Nvbw9HREa+++ipKS0ubcSvMz732++TJk+/4/R85cmStMdzvxlm0aBECAgJgZ2cHFxcXjB07FpmZmbXGNORzJTc3F0888QRatWoFFxcXzJo1C9XV1c25KWalIft92LBhd/y+T506tdYYc9zvDDVUS+/evXH16lXDa+/evYb3Zs6ciS1btmDDhg1ITk7GlStXMG7cOBGrNU9lZWXw8/NDTExMne9//PHH+Pe//42VK1fi4MGDaN26NcLCwlBRUWEY89JLL+HUqVNITEzE1q1bsWfPHrzxxhvNtQlm6V77HQBGjhxZ6/f/+++/r/U+97txkpOToVKpcODAASQmJqKqqgqhoaEoKyszjLnX54pOp8MTTzwBrVaL/fv3Y82aNfj6668xb948MTbJLDRkvwPA66+/Xuv3/eOPPza8Z7b7XSC6Zf78+YKfn1+d7xUVFQnW1tbChg0bDNPS09MFAEJKSkozVWh5AAgbN240/KzX6wU3Nzdh8eLFhmlFRUWCQqEQvv/+e0EQBOH06dMCAOHw4cOGMT///LMgkUiEy5cvN1vt5uyv+10QBGHSpEnCmDFj7joP9/uDy8/PFwAIycnJgiA07HMlISFBkEqlglqtNoxZsWKFYG9vL1RWVjbvBpipv+53QRCEoUOHCpGRkXedx1z3O4/UUC1nzpyBu7s7unTpgpdeegm5ubkAgNTUVFRVVSEkJMQwtmfPnujYsSNSUlLEKtfinDt3Dmq1utZ+dnBwQGBgoGE/p6SkwNHREf379zeMCQkJgVQqxcGDB5u9Zkuye/duuLi4oEePHggPD8f169cN73G/PziNRgMAcHJyAtCwz5WUlBT06dMHrq6uhjFhYWEoLi7GqVOnmrF68/XX/X7bd999B2dnZ/j6+iI6Oho3b940vGeu+73FNLSkewsMDMTXX3+NHj164OrVq3j//ffxyCOP4OTJk1Cr1ZDL5XB0dKw1j6urK9RqtTgFW6Db+/LPHyS3f779nlqthouLS633rays4OTkxH+LBzBy5EiMGzcOnTt3xtmzZ/HOO+9g1KhRSElJgUwm435/QHq9HjNmzEBwcDB8fX0BoEGfK2q1us6/h9vvUf3q2u8A8OKLL6JTp05wd3fH8ePHMXv2bGRmZuKnn34CYL77naGGDEaNGmX47759+yIwMBCdOnXCjz/+CFtbWxErI2p6zz//vOG/+/Tpg759+6Jr167YvXs3hg8fLmJllkGlUuHkyZO1rtOjpne3/f7na8H69OkDpVKJ4cOH4+zZs+jatWtzl9loePqJ7srR0RHe3t7Izs6Gm5sbtFotioqKao3Jy8uDm5ubOAVaoNv78q93f/x5P7u5uSE/P7/W+9XV1SgsLOS/RSPq0qULnJ2dkZ2dDYD7/UFERERg69at2LVrFzp06GCY3pDPFTc3tzr/Hm6/R3d3t/1el8DAQACo9ftujvudoYbuqrS0FGfPnoVSqYS/vz+sra2RlJRkeD8zMxO5ubkICgoSsUrL0rlzZ7i5udXaz8XFxTh48KBhPwcFBaGoqAipqamGMTt37oRerzd8MNGDu3TpEq5fvw6lUgmA+/1+CIKAiIgIbNy4ETt37kTnzp1rvd+Qz5WgoCCcOHGiVqBMTEyEvb09evXq1TwbYmbutd/rkpaWBgC1ft/Ncr+LfaUymY633npL2L17t3Du3Dlh3759QkhIiODs7Czk5+cLgiAIU6dOFTp27Cjs3LlTOHLkiBAUFCQEBQWJXLX5KSkpEY4dOyYcO3ZMACB8+umnwrFjx4QLFy4IgiAIH330keDo6Chs3rxZOH78uDBmzBihc+fOQnl5uWEZI0eOFB566CHh4MGDwt69e4Xu3bsLL7zwglibZBbq2+8lJSXC22+/LaSkpAjnzp0Tfv31V+Hhhx8WunfvLlRUVBiWwf1unPDwcMHBwUHYvXu3cPXqVcPr5s2bhjH3+lyprq4WfH19hdDQUCEtLU3Yvn270L59eyE6OlqMTTIL99rv2dnZwgcffCAcOXJEOHfunLB582ahS5cuwpAhQwzLMNf9zlBDBhMmTBCUSqUgl8sFDw8PYcKECUJ2drbh/fLycmHatGlC27ZthVatWglPP/20cPXqVRErNk+7du0SANzxmjRpkiAINbd1z507V3B1dRUUCoUwfPhwITMzs9Yyrl+/LrzwwgtCmzZtBHt7e2HKlClCSUmJCFtjPurb7zdv3hRCQ0OF9u3bC9bW1kKnTp2E119/vdbtrILA/W6suvY3AGH16tWGMQ35XDl//rwwatQowdbWVnB2dhbeeustoaqqqpm3xnzca7/n5uYKQ4YMEZycnASFQiF069ZNmDVrlqDRaGotxxz3u0QQBKH5jgsRERERNQ1eU0NEREQWgaGGiIiILAJDDREREVkEhhoiIiKyCAw1REREZBEYaoiIiMgiMNQQERGRRWCoISIiIovAUENEFkMikWDTpk1il0FEImGoISIiIovAUENEREQWgaGGiMzGsGHD8Pe//x3/+Mc/4OTkBDc3N7z33nu1xly9ehWjRo2Cra0tunTpgri4OHGKJaJmx1BDRGZlzZo1aN26NQ4ePIiPP/4YH3zwARITEw3vz507F+PHj8fvv/+Ol156Cc8//zzS09NFrJiImgu7dBOR2Rg2bBh0Oh1+++03w7QBAwbgsccew0cffQSJRIKpU6dixYoVhvcHDhyIhx9+GF988YUYJRNRM+KRGiIyK3379q31s1KpRH5+vuHnoKCgWu8HBQXxSA1RC8FQQ0RmxdrautbPEokEer1epGqIyJQw1BCRRTlw4MAdP/v4+IhUDRE1JyuxCyAiakwbNmxA//79MXjwYHz33Xc4dOgQvvrqK7HLIqJmwFBDRBbl/fffxw8//IBp06ZBqVTi+++/R69evcQui4iaAe9+IiIiIovAa2qIiIjIIjDUEBERkUVgqCEiIiKLwFBDREREFoGhhoiIiCwCQw0RERFZBIYaIiIisggMNURERGQRGGqIiIjIIjDUEBERkUVgqCEiIiKL8P+PoCaeiUiNmwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.neural_network import MLPRegressor\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "min_samples_splits = [2, 5, 10]\n",
+    "rows = []\n",
+    "for nb in tqdm(range(20, 300, 40)):\n",
+    "    clr = MLPRegressor(\n",
+    "        hidden_layer_sizes=(nb,), activation=\"relu\", alpha=0, tol=1e-6, max_iter=200\n",
+    "    )\n",
+    "    clr.fit(X_train, y_train.ravel())\n",
+    "    pred = clr.predict(X_test)\n",
+    "    score = r2_score(y_test, pred)\n",
+    "    if score > 0:\n",
+    "        d = dict(nb=nb, score=score)\n",
+    "        rows.append(d)\n",
+    "pandas.DataFrame(rows).plot(x=\"nb\", y=[\"score\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercice 1 : déterminer les paramètres optimaux pour cet exemple\n",
+    "\n",
+    "A vérifier avec [grid_search](https://scikit-learn.org/stable/modules/grid_search.html), [hyperopt](https://github.com/hyperopt/hyperopt-sklearn), [model_selection](https://scikit-learn.org/stable/modules/model_selection.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercice 2 : ajouter quelques points aberrants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intervalles de confiance\n",
+    "\n",
+    "On pourrait utiliser le module [forest-confidence-interval](https://github.com/scikit-learn-contrib/forest-confidence-interval) mais il ne semble plus vraiment maintenu. Le module s'appuie sur le [Jackknife](https://en.wikipedia.org/wiki/Jackknife_resampling) pour estimer des intervalles de confiance. Il calcule un estimateur qui calcule une sortie en supprimant plusieurs fois un arbre lors de l'évaluation de la sortie de la forêt aléatoire. La théorie s'appuie sur un *resampling* de la base d'apprentissage que l'article considère comme équivalent à ceux effectués par scikit-learn pour générer chaque arbre. L'idée s'appuie sur l'article [Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife](http://jmlr.csail.mit.edu/papers/volume15/wager14a/wager14a.pdf)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=2, n_estimators=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=2, n_estimators=10)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(max_depth=2, n_estimators=10)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clr = RandomForestRegressor(n_estimators=10, max_depth=2)\n",
+    "clr.fit(X_train, y_train.ravel())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=2, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=10, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=2, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=10, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=2, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=10, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from xgboost import XGBRegressor\n",
+    "\n",
+    "clrx = XGBRegressor(n_estimators=10, max_depth=2)\n",
+    "clrx.fit(X_train, y_train.ravel())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Xs = numpy.arange(start=0, stop=6, step=0.001)\n",
+    "Xs = Xs.reshape((len(Xs), 1))\n",
+    "ps = clr.predict(Xs)\n",
+    "ps = ps.reshape((len(ps), 1))\n",
+    "psx = clrx.predict(Xs)\n",
+    "psx = psx.reshape((len(psx), 1))\n",
+    "\n",
+    "df = pandas.DataFrame(numpy.hstack((Xs, ps, psx)), columns=[\"X\", \"Y sk\", \"Y xg\"])\n",
+    "ax = df.plot(x=\"X\", y=[\"Y sk\", \"Y xg\"], kind=\"line\", lw=2)\n",
+    "ax.plot(X, Y, \"g.\", ms=1)\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercice 3 : optimiser les hyperparamètres pour XGBoost et scikit-learn et comparer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_doc/practice/ml/ml_features_model.ipynb b/_doc/practice/ml/ml_features_model.ipynb
index 63edca7d..9a97b4c2 100644
--- a/_doc/practice/ml/ml_features_model.ipynb
+++ b/_doc/practice/ml/ml_features_model.ipynb
@@ -1064,7 +1064,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "C'est nettement mieux mais le modèle n'est plus aussi interprétable que le précédent, il est nettement plus long à calculer. Plus la frontière entre les classes est grande, plus il faut d'exemples dans la base d'apprentissage. Les autres modèles (arbre de décision, réseaux de neurones) proposent des séparations plus ou moins proches de la solution optimale. Le modèle [SVC](http://scikit-learn.org/stable/modules/svm.html#svc) fonctionne bien sur ce problème."
+    "C'est nettement mieux mais le modèle n'est plus aussi interprétable que le précédent, il est nettement plus long à calculer. Plus la frontière entre les classes est grande, plus il faut d'exemples dans la base d'apprentissage. Les autres modèles (arbre de décision, réseaux de neurones) proposent des séparations plus ou moins proches de la solution optimale. Le modèle [SVC](https://scikit-learn.org/stable/modules/svm.html#svc) fonctionne bien sur ce problème."
    ]
   },
   {
@@ -1521,7 +1521,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Cette approche est quelque peu séduisante. Elle donne l'impression qu'il suffit de parcourir la liste des modèles disponibles pour trouver celui qui convient le mieux. Sur un problème aussi simple et petit, cela ne pose pas de problème. Un très grand nombre d'observations réduit considérablement de choix. Les [plus proches voisins](https://fr.wikipedia.org/wiki/M%C3%A9thode_des_k_plus_proches_voisins) ou les [SVM](http://scikit-learn.org/stable/modules/svm.html#support-vector-machines) sont peu recommandés dans ce cas. Le nombre de variables ou features peut devenir un obstacle : en grande dimension, les algorithmes d'optimisation convergent moins bien."
+    "Cette approche est quelque peu séduisante. Elle donne l'impression qu'il suffit de parcourir la liste des modèles disponibles pour trouver celui qui convient le mieux. Sur un problème aussi simple et petit, cela ne pose pas de problème. Un très grand nombre d'observations réduit considérablement de choix. Les [plus proches voisins](https://fr.wikipedia.org/wiki/M%C3%A9thode_des_k_plus_proches_voisins) ou les [SVM](https://scikit-learn.org/stable/modules/svm.html#support-vector-machines) sont peu recommandés dans ce cas. Le nombre de variables ou features peut devenir un obstacle : en grande dimension, les algorithmes d'optimisation convergent moins bien."
    ]
   },
   {
diff --git a/_doc/practice/ml/wines_acp.ipynb b/_doc/practice/ml/wines_acp.ipynb
index 24ccc0ea..023bba18 100644
--- a/_doc/practice/ml/wines_acp.ipynb
+++ b/_doc/practice/ml/wines_acp.ipynb
@@ -301,7 +301,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "La valeur moyenne de chaque variable ressemble étrangement aux coordonnées du premier axe de l'ACP. Il faut normaliser les données avec [normalize](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html)."
+    "La valeur moyenne de chaque variable ressemble étrangement aux coordonnées du premier axe de l'ACP. Il faut normaliser les données avec [normalize](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html)."
    ]
   },
   {
diff --git a/_doc/practice/ml/winesc_color_linear.ipynb b/_doc/practice/ml/winesc_color_linear.ipynb
index e67805f6..0e392c77 100644
--- a/_doc/practice/ml/winesc_color_linear.ipynb
+++ b/_doc/practice/ml/winesc_color_linear.ipynb
@@ -1088,7 +1088,7 @@
    "source": [
     "## Automatisation\n",
     "\n",
-    "Au début, j'avais pensé écrire une classe inspirée de l'API de [scikit-learn](http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/02_basic_of_machine_learning_with_scikit-learn.html#a-recap-on-scikit-learn-s-estimator-interface) qui apprend plusieurs modèles selon une catégorie indiquée lors de l'apprentissage et de la prédiction."
+    "Au début, j'avais pensé écrire une classe inspirée de l'API de [scikit-learn](https://scipy-lectures.org/packages/scikit-learn/index.html#basic-principles-of-machine-learning-with-scikit-learn) qui apprend plusieurs modèles selon une catégorie indiquée lors de l'apprentissage et de la prédiction."
    ]
   },
   {
@@ -2473,18 +2473,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/ml/winesc_multi.ipynb b/_doc/practice/ml/winesc_multi.ipynb
index d328b1a1..7975365d 100644
--- a/_doc/practice/ml/winesc_multi.ipynb
+++ b/_doc/practice/ml/winesc_multi.ipynb
@@ -788,7 +788,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pas extraordinaire. On applique la stratégie [OneVsRestClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html)."
+    "Pas extraordinaire. On applique la stratégie [OneVsRestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html)."
    ]
   },
   {
@@ -2300,7 +2300,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Et avec [OneVsRestClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html) :"
+    "Et avec [OneVsRestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html) :"
    ]
   },
   {
@@ -2755,7 +2755,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Et avec [OneVsOneClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html)"
+    "Et avec [OneVsOneClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html)"
    ]
   },
   {
diff --git a/_doc/practice/ml/winesc_multi_stacking.ipynb b/_doc/practice/ml/winesc_multi_stacking.ipynb
index 9c716272..7b94f5a3 100644
--- a/_doc/practice/ml/winesc_multi_stacking.ipynb
+++ b/_doc/practice/ml/winesc_multi_stacking.ipynb
@@ -1248,7 +1248,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "La courbe ROC ne montre rien de probant. Il faudrait vérifier avec une cross-validation qu'il serait pratique de faire avec un [pipeline](http://scikit-learn.org/stable/modules/pipeline.html) mais ceux-ci n'acceptent qu'un seul prédicteur final."
+    "La courbe ROC ne montre rien de probant. Il faudrait vérifier avec une cross-validation qu'il serait pratique de faire avec un [pipeline](https://scikit-learn.org/stable/modules/pipeline.html) mais ceux-ci n'acceptent qu'un seul prédicteur final."
    ]
   },
   {
diff --git a/_doc/practice/ml/winesr_knn_cross_val.ipynb b/_doc/practice/ml/winesr_knn_cross_val.ipynb
index 3620f5bf..e58abcc2 100644
--- a/_doc/practice/ml/winesr_knn_cross_val.ipynb
+++ b/_doc/practice/ml/winesr_knn_cross_val.ipynb
@@ -237,7 +237,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Nous allons utiliser la fonction [cross_val_score](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html)."
+    "Nous allons utiliser la fonction [cross_val_score](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html)."
    ]
   },
   {
@@ -363,7 +363,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On obtient bien les mêmes résultats mais ils sont bien différents de ceux obtenus avec [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) et reproduits ci-dessous."
+    "On obtient bien les mêmes résultats mais ils sont bien différents de ceux obtenus avec [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) et reproduits ci-dessous."
    ]
   },
   {
@@ -531,7 +531,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On retrouve les mêmes scores que pour [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). Comment l'interpréter ? La raison la plus probable est que la validation croisée implémenté par *scikit-learn* n'est par défaut pas aléatoire. Cela explique qu'on retrouve les mêmes résultats sur deux exécutions. Il reste à expliquer le fait que les chiffres sont nettement mauvais pour le premier code et meilleurs pour ce second code. \n",
+    "On retrouve les mêmes scores que pour [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). Comment l'interpréter ? La raison la plus probable est que la validation croisée implémenté par *scikit-learn* n'est par défaut pas aléatoire. Cela explique qu'on retrouve les mêmes résultats sur deux exécutions. Il reste à expliquer le fait que les chiffres sont nettement mauvais pour le premier code et meilleurs pour ce second code. \n",
     "\n",
     "**Et si les vins n'étaient pas mélangés dans la base avec des vins rouges au début et blancs vers la fin ?**"
    ]
@@ -775,7 +775,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Les éléments sont clairements triés par couleur et la validation croisée par défaut découpe selon cet ordre. Cela signifie presque que le modèle essaye de prédire la note d'un vin rouge en s'appuyant sur des vins blancs et cela ne marche visiblement pas. La validation croisée ne retourne pas de modèle mais cela peut être contourné avec [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)."
+    "Les éléments sont clairements triés par couleur et la validation croisée par défaut découpe selon cet ordre. Cela signifie presque que le modèle essaye de prédire la note d'un vin rouge en s'appuyant sur des vins blancs et cela ne marche visiblement pas. La validation croisée ne retourne pas de modèle mais cela peut être contourné avec [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)."
    ]
   },
   {
diff --git a/_doc/practice/ml/winesr_knn_hyper.ipynb b/_doc/practice/ml/winesr_knn_hyper.ipynb
index 6169653e..c1ef0c4c 100644
--- a/_doc/practice/ml/winesr_knn_hyper.ipynb
+++ b/_doc/practice/ml/winesr_knn_hyper.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Sélection des hyper-paramètres\n",
     "\n",
-    "Le modèle des plus proches voisins [KNeighborsRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) est paramétrable. Le nombre de voisins est variables, la prédiction peut dépendre du plus proche voisins ou des $k$ plus proches proches. Comment choisir $k$ ? "
+    "Le modèle des plus proches voisins [KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) est paramétrable. Le nombre de voisins est variables, la prédiction peut dépendre du plus proche voisins ou des $k$ plus proches proches. Comment choisir $k$ ? "
    ]
   },
   {
@@ -110,7 +110,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "La fonction [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) automatise la recherche d'un optimum parmi les hyperparamètre, elle utilise notamment la validation croisée. On teste toutes les valeurs de $k$ de 1 à 20."
+    "La fonction [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) automatise la recherche d'un optimum parmi les hyperparamètre, elle utilise notamment la validation croisée. On teste toutes les valeurs de $k$ de 1 à 20."
    ]
   },
   {
diff --git a/_doc/practice/ml/winesr_knn_split.ipynb b/_doc/practice/ml/winesr_knn_split.ipynb
index c7bdff6c..aed40457 100644
--- a/_doc/practice/ml/winesr_knn_split.ipynb
+++ b/_doc/practice/ml/winesr_knn_split.ipynb
@@ -35,7 +35,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On divise en base d'apprentissage et de test avec la fonction [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)."
+    "On divise en base d'apprentissage et de test avec la fonction [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)."
    ]
   },
   {
@@ -719,7 +719,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Le modèle se trompe en moyenne d'un demi point. Le module *scikit-learn* propose de nombreuses [métriques](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics) pour évaluer les résultats. On s'intéresse plus particulièrement à celle de la [régression](http://scikit-learn.org/stable/modules/classes.html#regression-metrics). Celle qu'on a utilisée s'appelle [mean_absolute_error](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error)."
+    "Le modèle se trompe en moyenne d'un demi point. Le module *scikit-learn* propose de nombreuses [métriques](https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics) pour évaluer les résultats. On s'intéresse plus particulièrement à celle de la [régression](https://scikit-learn.org/stable/modules/classes.html#regression-metrics). Celle qu'on a utilisée s'appelle [mean_absolute_error](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error)."
    ]
   },
   {
@@ -748,7 +748,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Un autre indicateur très utilisé : [R2](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score)."
+    "Un autre indicateur très utilisé : [R2](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score)."
    ]
   },
   {
diff --git a/_doc/practice/ml/winesr_knn_split_strat.ipynb b/_doc/practice/ml/winesr_knn_split_strat.ipynb
index 9da66b4f..e8dabbf5 100644
--- a/_doc/practice/ml/winesr_knn_split_strat.ipynb
+++ b/_doc/practice/ml/winesr_knn_split_strat.ipynb
@@ -35,7 +35,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On divise en base d'apprentissage et de test avec la fonction [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)."
+    "On divise en base d'apprentissage et de test avec la fonction [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)."
    ]
   },
   {
@@ -172,7 +172,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On voit le ratio entre les deux classes est à peu près égal à 1/3 sauf pour les notes sous-représentées. On utilise une répartition stratifiée : la distribution d'une variable, les labels, sera la même dans les bases d'apprentissages et de de tests. On s'inspire de l'exemple [StratifiedShuffleSplit](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html)."
+    "On voit le ratio entre les deux classes est à peu près égal à 1/3 sauf pour les notes sous-représentées. On utilise une répartition stratifiée : la distribution d'une variable, les labels, sera la même dans les bases d'apprentissages et de de tests. On s'inspire de l'exemple [StratifiedShuffleSplit](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html)."
    ]
   },
   {
diff --git a/_doc/practice/ml/winesr_reg.ipynb b/_doc/practice/ml/winesr_reg.ipynb
index fd445a50..fdc2c2ef 100644
--- a/_doc/practice/ml/winesr_reg.ipynb
+++ b/_doc/practice/ml/winesr_reg.ipynb
@@ -465,7 +465,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "L'estimateur [RANSACRegressor](http://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py) produit un $R^2$ très négatif. Regardons plus en détail."
+    "L'estimateur [RANSACRegressor](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py) produit un $R^2$ très négatif. Regardons plus en détail."
    ]
   },
   {
@@ -962,7 +962,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Le modèle [RANSAC](http://scikit-learn.org/stable/modules/linear_model.html#ransac-random-sample-consensus) est conçu pour apprendre un modèle linéaire et réduire l'ensemble d'apprentissage aux points non aberrants. Dans notre cas, vu le peu d'exemples pour les notes élevées, il est très probable que celles-ci disparaissent des observations choisies pour estimer le modèle : le modèle choisit d'évincer les exemples pour lesquels l'erreur est la plus grande en considérant que cela est une indication du fait qu'ils sont aberrants. Malgré cela, sa faible capacité à prévoir vient du fait que la majorité des vins ont une note entre 4 et 6 et que finalement, il y a peu de différences : ils sont tous moyens et la différence s'explique par d'autres facteurs comme le juge ayant donné la note."
+    "Le modèle [RANSAC](https://scikit-learn.org/stable/modules/linear_model.html#ransac-random-sample-consensus) est conçu pour apprendre un modèle linéaire et réduire l'ensemble d'apprentissage aux points non aberrants. Dans notre cas, vu le peu d'exemples pour les notes élevées, il est très probable que celles-ci disparaissent des observations choisies pour estimer le modèle : le modèle choisit d'évincer les exemples pour lesquels l'erreur est la plus grande en considérant que cela est une indication du fait qu'ils sont aberrants. Malgré cela, sa faible capacité à prévoir vient du fait que la majorité des vins ont une note entre 4 et 6 et que finalement, il y a peu de différences : ils sont tous moyens et la différence s'explique par d'autres facteurs comme le juge ayant donné la note."
    ]
   }
  ],
diff --git a/_doc/practice/ml/winesr_reg_poly.ipynb b/_doc/practice/ml/winesr_reg_poly.ipynb
index c083397b..470d714b 100644
--- a/_doc/practice/ml/winesr_reg_poly.ipynb
+++ b/_doc/practice/ml/winesr_reg_poly.ipynb
@@ -66,7 +66,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "La transformation [PolynomialFeatures](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) créée de nouvelles features en multipliant les variables les unes avec les autres. Pour le degré deux et trois features $a, b, c$, on obtient les nouvelles features : $1, a, b, c, a^2, ab, ac, b^2, bc, c^2$."
+    "La transformation [PolynomialFeatures](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) créée de nouvelles features en multipliant les variables les unes avec les autres. Pour le degré deux et trois features $a, b, c$, on obtient les nouvelles features : $1, a, b, c, a^2, ab, ac, b^2, bc, c^2$."
    ]
   },
   {
@@ -1560,7 +1560,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On ne garde que celles dont la [p-value](http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/c_metric/pvalues.html) est inférieur à 0.05."
+    "On ne garde que celles dont la [p-value](https://sdpython.github.io/doc/mlstatpy/dev/c_metric/pvalues.html) est inférieur à 0.05."
    ]
   },
   {
@@ -1662,18 +1662,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index f92782c4..82efdcbc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,7 +25,7 @@ license = {file = "LICENSE.txt"}
 name = "teachpyx"
 readme = "README.rst"
 requires-python = ">=3.9"
-version = "0.4.0"
+version = "0.5.0"
 
 [project.urls]
 Homepage = "https://sdpython.github.io/doc/teachpyx/dev/"
diff --git a/requirements-dev.txt b/requirements-dev.txt
index d6664ae9..7d6329b4 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -22,6 +22,7 @@ nbsphinx
 networkx
 openpyxl
 pandas
+patsy
 pillow
 protobuf<4
 pyinstrument
@@ -39,4 +40,5 @@ git+https://github.com/sdpython/sphinx-runpython.git
 statsmodels
 tqdm
 ujson
+xgboost
 wheel
diff --git a/teachpyx/__init__.py b/teachpyx/__init__.py
index 4f2cc121..ad683b14 100644
--- a/teachpyx/__init__.py
+++ b/teachpyx/__init__.py
@@ -1,6 +1,6 @@
 # coding: utf-8
 
-__version__ = "0.4.0"
+__version__ = "0.5.0"
 __author__ = "Xavier Dupré"
 __github__ = "https://github.com/sdpython/teachpyx"
 __url__ = "https://sdpython.github.io/doc/teachpyx/dev/"