From 15125ef6dbdf52e15520f1c8a98b9b5dfe9adbd0 Mon Sep 17 00:00:00 2001
From: ajoubrel-ensae <antoine.joubrel@ensae.fr>
Date: Tue, 13 Feb 2024 22:42:13 +0000
Subject: [PATCH] Nettoyage

---
 2_Regression_logistique.ipynb | 26 --------------------------
 1 file changed, 26 deletions(-)

diff --git a/2_Regression_logistique.ipynb b/2_Regression_logistique.ipynb
index 75dca1a..501ee86 100644
--- a/2_Regression_logistique.ipynb
+++ b/2_Regression_logistique.ipynb
@@ -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,