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Antoine JOUBREL 2024-02-13 22:42:13 +00:00
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2_Regression_logistique.ipynb
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@ -27,32 +27,6 @@
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "59ce5096-4e2c-45c1-be78-43e14db4142c",
"metadata": {},
"outputs": [],
"source": [
"# # modification des variables categorielles\n",
" \n",
"# ### variable gender\n",
"# df1_customer_product[\"gender_label\"] = df1_customer_product[\"gender\"].map({\n",
"# 0: 'female',\n",
"# 1: 'male',\n",
"# 2: 'other'\n",
"# })\n",
" \n",
"# ### variable country -> on indique si le pays est france\n",
"# df1_customer_product[\"country_fr\"] = df1_customer_product[\"country\"].apply(lambda x : int(x==\"fr\") if pd.notna(x) else np.nan)\n",
"\n",
"# # Création des indicatrices de gender\n",
"# gender_dummies = pd.get_dummies(df1_customer_product[\"gender_label\"], prefix='gender').astype(int)\n",
" \n",
"# # Concaténation des indicatrices avec le dataframe d'origine\n",
"# df1_customer_product = pd.concat([df1_customer_product, gender_dummies], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 3,