diff --git a/Notebook_AR.ipynb b/Notebook_AR.ipynb
index 0ad1826..fb178c9 100644
--- a/Notebook_AR.ipynb
+++ b/Notebook_AR.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": 15,
    "id": "20eeb149-6618-4ef2-9cfd-ff062950f36c",
    "metadata": {},
    "outputs": [],
@@ -8311,49 +8311,466 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
-   "id": "daef46cd-f6a5-4282-ac0a-83fde277edec",
+   "execution_count": 30,
+   "id": "25346383-25e3-43e3-8ea4-fe05bd68900d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_purchase = df_purchase.fillna(0)"
+    "import pandas as pd\n",
+    "import os\n",
+    "import s3fs\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
-   "id": "e34437e6-a57d-4d10-ac62-5c43cdda6892",
+   "execution_count": 36,
+   "id": "453c317a-b979-4fac-a9eb-60d72bf9caa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create filesystem object\n",
+    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
+    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "07bb1dbc-1543-49b0-a41c-6d5f569698d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(156289, 41)\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>customer_id</th>\n",
+       "      <th>birthdate</th>\n",
+       "      <th>street_id</th>\n",
+       "      <th>is_partner</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>is_email_true</th>\n",
+       "      <th>opt_in</th>\n",
+       "      <th>structure_id</th>\n",
+       "      <th>profession</th>\n",
+       "      <th>language</th>\n",
+       "      <th>...</th>\n",
+       "      <th>purchase_date_min</th>\n",
+       "      <th>purchase_date_max</th>\n",
+       "      <th>time_between_purchase</th>\n",
+       "      <th>nb_tickets_internet</th>\n",
+       "      <th>name_event_types</th>\n",
+       "      <th>avg_amount</th>\n",
+       "      <th>nb_categories</th>\n",
+       "      <th>nb_campaigns</th>\n",
+       "      <th>nb_campaigns_opened</th>\n",
+       "      <th>time_to_open</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1705.261192</td>\n",
+       "      <td>1456.333715</td>\n",
+       "      <td>248.927477</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>formule adhésion</td>\n",
+       "      <td>6.439463</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2041.274549</td>\n",
+       "      <td>1340.308160</td>\n",
+       "      <td>700.966389</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>offre muséale individuel</td>\n",
+       "      <td>6.150659</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1511.177396</td>\n",
+       "      <td>42.428692</td>\n",
+       "      <td>1468.748704</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>spectacle vivant</td>\n",
+       "      <td>7.762474</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>1 days 00:28:30.169354838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1511.157396</td>\n",
+       "      <td>1511.157396</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>formule adhésion</td>\n",
+       "      <td>6.439463</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1 days 04:31:01.428571428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>797.033437</td>\n",
+       "      <td>797.033437</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>spectacle vivant</td>\n",
+       "      <td>7.762474</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1 days 04:31:01.428571428</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id birthdate  street_id  is_partner  gender  is_email_true  \\\n",
+       "4            2       NaN          2       False       1           True   \n",
+       "5            2       NaN          2       False       1           True   \n",
+       "6            3       NaN          3       False       1           True   \n",
+       "7            4       NaN          4       False       0           True   \n",
+       "8            4       NaN          4       False       0           True   \n",
+       "\n",
+       "   opt_in  structure_id profession language  ...  purchase_date_min  \\\n",
+       "4    True           NaN        NaN      NaN  ...        1705.261192   \n",
+       "5    True           NaN        NaN      NaN  ...        2041.274549   \n",
+       "6    True           NaN        NaN      NaN  ...        1511.177396   \n",
+       "7    True           NaN        NaN      NaN  ...        1511.157396   \n",
+       "8    True           NaN        NaN      NaN  ...         797.033437   \n",
+       "\n",
+       "  purchase_date_max  time_between_purchase  nb_tickets_internet  \\\n",
+       "4       1456.333715             248.927477                  0.0   \n",
+       "5       1340.308160             700.966389                  0.0   \n",
+       "6         42.428692            1468.748704                  6.0   \n",
+       "7       1511.157396               0.000000                  2.0   \n",
+       "8        797.033437               0.000000                  2.0   \n",
+       "\n",
+       "           name_event_types  avg_amount  nb_categories  nb_campaigns  \\\n",
+       "4          formule adhésion    6.439463            1.0           4.0   \n",
+       "5  offre muséale individuel    6.150659            1.0           4.0   \n",
+       "6          spectacle vivant    7.762474            1.0         222.0   \n",
+       "7          formule adhésion    6.439463            1.0           7.0   \n",
+       "8          spectacle vivant    7.762474            1.0           7.0   \n",
+       "\n",
+       "   nb_campaigns_opened               time_to_open  \n",
+       "4                  NaN                        NaN  \n",
+       "5                  NaN                        NaN  \n",
+       "6                124.0  1 days 00:28:30.169354838  \n",
+       "7                  7.0  1 days 04:31:01.428571428  \n",
+       "8                  7.0  1 days 04:31:01.428571428  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Chargement des données temporaires\n",
+    "BUCKET = \"projet-bdc2324-team1\"\n",
+    "FILE_KEY_S3 = \"0_Temp/Company 1 - customer_event.csv\"\n",
+    "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n",
+    "\n",
+    "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
+    "    customer = pd.read_csv(file_in, sep=\",\")\n",
+    "\n",
+    "print(customer.shape)\n",
+    "# Remove customer 1 as outlier\n",
+    "\n",
+    "customer = customer[customer['customer_id']!=1]\n",
+    "customer.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "d1f2608a-911c-440e-b7cd-5cd6684b0de3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer_id', 'birthdate', 'street_id', 'is_partner', 'gender',\n",
+       "       'is_email_true', 'opt_in', 'structure_id', 'profession', 'language',\n",
+       "       'mcp_contact_id', 'last_buying_date', 'max_price', 'ticket_sum',\n",
+       "       'average_price', 'fidelity', 'average_purchase_delay',\n",
+       "       'average_price_basket', 'average_ticket_basket', 'total_price',\n",
+       "       'purchase_count', 'first_buying_date', 'country', 'age', 'tenant_id',\n",
+       "       'event_type_id', 'nb_tickets', 'nb_purchases', 'total_amount',\n",
+       "       'nb_suppliers', 'vente_internet_max', 'purchase_date_min',\n",
+       "       'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet',\n",
+       "       'name_event_types', 'avg_amount', 'nb_categories', 'nb_campaigns',\n",
+       "       'nb_campaigns_opened', 'time_to_open'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customer.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "fe5557f9-b629-46bf-a606-a8db2158933c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6.,  2.,  4.,  5., nan])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customer['event_type_id'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "b1681828-165b-43be-8e42-04af052c132a",
+   "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>event_type_id</th>\n",
+       "      <th>name_event_types</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>formule adhésion</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>offre muséale individuel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>spectacle vivant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>offre muséale groupe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    event_type_id          name_event_types\n",
+       "4             6.0          formule adhésion\n",
+       "5             2.0  offre muséale individuel\n",
+       "6             4.0          spectacle vivant\n",
+       "16            5.0      offre muséale groupe\n",
+       "40            NaN                       NaN"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customer[['event_type_id', 'name_event_types']].drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "daef46cd-f6a5-4282-ac0a-83fde277edec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape :  (21388, 41)\n"
+     ]
+    }
+   ],
+   "source": [
+    "customer_event1 = customer[customer['event_type_id']==2]\n",
+    "print(\"shape : \", customer_event1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "cd1103b4-27b5-4be1-8451-43172df2d33e",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n",
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+      "/tmp/ipykernel_551/3565272300.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  customer_event1[\"percent_campaign_opened\"] = 100* (customer_event1[\"nb_campaigns_opened\"] /\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "customer_event1[\"percent_campaign_opened\"] = 100* (customer_event1[\"nb_campaigns_opened\"] /\n",
+    "                                              customer_event1[\"nb_campaigns\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "ec19582b-7d84-422c-8f53-9b0eb1bea21e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customer_event1 = customer_event1.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "e34437e6-a57d-4d10-ac62-5c43cdda6892",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -8366,10 +8783,12 @@
     "from sklearn.cluster import KMeans\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "columns_for_clustering = ['gender', 'is_partner', 'is_email_true', 'nb_campaigns', 'nb_campaigns_opened', 'fidelity', 'nb_tickets', 'ticket_sum', 'average_price', 'amount']\n",
+    "columns_for_clustering = [\"percent_campaign_opened\", 'nb_tickets', 'average_price', 'nb_purchases',\n",
+    "                          'average_purchase_delay', 'average_price_basket', 'average_ticket_basket',\n",
+    "                         'nb_categories', 'nb_suppliers']\n",
     "\n",
     "scaler = StandardScaler()\n",
-    "X = scaler.fit_transform(df_purchase[columns_for_clustering])\n",
+    "X = scaler.fit_transform(customer_event1[columns_for_clustering])\n",
     "\n",
     "inertia = []\n",
     "for i in range(1, 11):\n",
@@ -8389,63 +8808,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 52,
    "id": "4da7d97e-9128-4e4a-a454-1451d2dfee40",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/mamba/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
-      "  super()._check_params_vs_input(X, default_n_init=10)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Cluster 1:\n",
-      "gender                    0.893962\n",
-      "is_partner                0.000000\n",
-      "is_email_true             1.000000\n",
-      "nb_campaigns            231.270802\n",
-      "nb_campaigns_opened      99.261042\n",
-      "fidelity                 30.193383\n",
-      "nb_tickets               10.965757\n",
-      "ticket_sum             2604.072622\n",
-      "average_price             9.781489\n",
-      "amount                   16.114144\n",
+      "percent_campaign_opened     23.552766\n",
+      "nb_tickets                   4.831229\n",
+      "average_price                9.073833\n",
+      "nb_purchases                 1.178776\n",
+      "average_purchase_delay    -243.688956\n",
+      "average_price_basket        19.548741\n",
+      "average_ticket_basket        3.755214\n",
+      "nb_categories                1.123118\n",
+      "nb_suppliers                 1.000000\n",
       "Name: 0, dtype: float64\n",
-      "Size: 6045\n",
+      "Size: 20590\n",
       "\n",
       "Cluster 2:\n",
-      "gender                 1.999420e+00\n",
-      "is_partner             0.000000e+00\n",
-      "is_email_true          9.998067e-01\n",
-      "nb_campaigns           1.048816e-02\n",
-      "nb_campaigns_opened    1.159981e-03\n",
-      "fidelity               3.305112e+05\n",
-      "nb_tickets             6.141087e+01\n",
-      "ticket_sum             1.253568e+06\n",
-      "average_price          7.031328e+00\n",
-      "amount                 6.880643e+00\n",
+      "percent_campaign_opened    35.050398\n",
+      "nb_tickets                 36.677136\n",
+      "average_price              13.146019\n",
+      "nb_purchases                4.533920\n",
+      "average_purchase_delay     40.550181\n",
+      "average_price_basket       54.766752\n",
+      "average_ticket_basket       9.094874\n",
+      "nb_categories               1.918342\n",
+      "nb_suppliers                2.036432\n",
       "Name: 1, dtype: float64\n",
-      "Size: 20690\n",
+      "Size: 796\n",
       "\n",
       "Cluster 3:\n",
-      "gender                   1.311996\n",
-      "is_partner               0.000000\n",
-      "is_email_true            0.982297\n",
-      "nb_campaigns            11.520089\n",
-      "nb_campaigns_opened      2.922872\n",
-      "fidelity                 4.664367\n",
-      "nb_tickets               4.819549\n",
-      "ticket_sum             184.855712\n",
-      "average_price            9.696602\n",
-      "amount                  11.980846\n",
+      "percent_campaign_opened      49.430524\n",
+      "nb_tickets                 9085.000000\n",
+      "average_price                 1.149821\n",
+      "nb_purchases                485.000000\n",
+      "average_purchase_delay       -3.941335\n",
+      "average_price_basket         12.981105\n",
+      "average_ticket_basket        38.049343\n",
+      "nb_categories                13.500000\n",
+      "nb_suppliers                  4.500000\n",
       "Name: 2, dtype: float64\n",
-      "Size: 101623\n",
+      "Size: 2\n",
       "\n"
      ]
     }
@@ -8454,10 +8862,10 @@
     "k = 3  \n",
     "\n",
     "kmeans = KMeans(n_clusters=k, random_state=42)\n",
-    "df_purchase['cluster'] = kmeans.fit_predict(X)\n",
+    "customer_event1['cluster'] = kmeans.fit_predict(X)\n",
     "\n",
-    "cluster_means = df_purchase.groupby('cluster')[columns_for_clustering].mean()\n",
-    "cluster_sizes = df_purchase['cluster'].value_counts()\n",
+    "cluster_means = customer_event1.groupby('cluster')[columns_for_clustering].mean()\n",
+    "cluster_sizes = customer_event1['cluster'].value_counts()\n",
     "\n",
     "for cluster in range(k):\n",
     "    print(f\"Cluster {cluster + 1}:\")\n",
@@ -8482,7 +8890,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,