{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3415114e-9577-4487-89eb-4931620ad9f0",
   "metadata": {},
   "source": [
    "# Predict Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f271eb45-1470-4764-8c2e-31374efa1fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import s3fs\n",
    "import re\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n",
    "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
    "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n",
    "\n",
    "import pickle\n",
    "import warnings\n",
    "#import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3fecb606-22e5-4dee-8efa-f8dff0832299",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae591854-3003-4c75-a0c7-5abf04246e81",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "59dd4694-a812-4923-b995-a2ee86c74f85",
   "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": 25,
   "id": "017f7e9a-3ba0-40fa-bdc8-51b98cc1fdb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_test():\n",
    "    BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n",
    "    File_path_train = BUCKET + \"/Train_set/\" + \"dataset_train5.csv\"\n",
    "    File_path_test = BUCKET + \"/Test_set/\" + \"dataset_test5.csv\"\n",
    "    \n",
    "    with fs.open( File_path_train, mode=\"rb\") as file_in:\n",
    "        dataset_train = pd.read_csv(file_in, sep=\",\")\n",
    "        dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n",
    "\n",
    "    with fs.open(File_path_test, mode=\"rb\") as file_in:\n",
    "        dataset_test = pd.read_csv(file_in, sep=\",\")\n",
    "        dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n",
    "    \n",
    "    return dataset_train, dataset_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "825d14a3-6967-4733-bfd4-64bf61c2bd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_target_split(dataset_train, dataset_test):\n",
    "    features_l = ['nb_tickets', 'nb_purchases', 'total_amount',\n",
    "       'nb_suppliers', 'nb_tickets_internet',\n",
    "       'opt_in',\n",
    "        'nb_campaigns', 'nb_campaigns_opened']\n",
    "    X_train = dataset_train[features_l]\n",
    "    y_train = dataset_train[['y_has_purchased']]\n",
    "\n",
    "    X_test = dataset_test[features_l]\n",
    "    y_test = dataset_test[['y_has_purchased']]\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c479b230-b4bd-4cfb-b76b-d9faf6d95772",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_train, dataset_test = load_train_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "69eaec12-b30f-4d30-a461-ea520d5cbf77",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d039f31d-0093-46c6-9743-ddec1381f758",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape train :  (330117, 8)\n",
      "Shape test :  (141480, 8)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape train : \", X_train.shape)\n",
    "print(\"Shape test : \", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d6de94-4e11-481a-a0ce-412bf29f692c",
   "metadata": {},
   "source": [
    "### Prepare preprocessing and Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b808da43-c444-4e94-995a-7ec6ccd01e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: 0.5381774965030861, 1.0: 7.048360235716116}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute Weights\n",
    "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n",
    "                                            y = y_train['y_has_purchased'])\n",
    "\n",
    "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n",
    "weight_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b32a79ea-907f-4dfc-9832-6c74bef3200c",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n",
    "                    'nb_tickets_internet', 'nb_campaigns', 'nb_campaigns_opened']\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    #(\"imputer\", SimpleImputer(strategy=\"mean\")),  \n",
    "    (\"scaler\", StandardScaler()) \n",
    "])\n",
    "\n",
    "categorical_features = ['opt_in']  \n",
    "\n",
    "# Transformer for the categorical features\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")),  # Impute missing values with the most frequent\n",
    "    (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
    "])\n",
    "\n",
    "preproc = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numeric_transformer, numeric_features),\n",
    "        (\"cat\", categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9809a688-bfbc-4685-a77f-17a8b2b79ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set loss\n",
    "\n",
    "balanced_scorer = make_scorer(balanced_accuracy_score)\n",
    "recall_scorer = make_scorer(recall_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4f9b2bbf-5f8a-4ac1-8e6c-51bd0dd8ac85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_confusion_matrix(y_test, y_pred):\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def draw_roc_curve(X_test, y_test):\n",
    "    y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n",
    "\n",
    "    # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n",
    "    \n",
    "    # Calcul de l'aire sous la courbe ROC (AUC)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    plt.figure(figsize = (14, 8))\n",
    "    plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n",
    "    plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
    "    plt.xlabel('Taux de faux positifs (FPR)')\n",
    "    plt.ylabel('Taux de vrais positifs (TPR)')\n",
    "    plt.title('Courbe ROC : modèle logistique')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "206d9a95-7c37-4506-949b-e77d225e42c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter\n",
    "\n",
    "param_grid = {'logreg__C': np.logspace(-10, 6, 17, base=2),\n",
    "              'logreg__penalty': ['l1', 'l2'],\n",
    "               'logreg__class_weight': ['balanced', weight_dict]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ff2f7bd-efc1-4f7c-a3c9-caa916aa2f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                                  1.0: 7.048360235716116},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                                  1.0: 7.048360235716116},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;,\n",
       "                                  &#x27;nb_suppliers&#x27;, &#x27;nb_tickets_internet&#x27;,\n",
       "                                  &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse_output=False))]),\n",
       "                                 [&#x27;opt_in&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;nb_tickets_internet&#x27;, &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">cat</label><div class=\"sk-toggleable__content \"><pre>[&#x27;opt_in&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                 1.0: 7.048360235716116},\n",
       "                   max_iter=5000, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['nb_tickets', 'nb_purchases',\n",
       "                                                   'total_amount',\n",
       "                                                   'nb_suppliers',\n",
       "                                                   'nb_tickets_internet',\n",
       "                                                   'nb_campaigns',\n",
       "                                                   'nb_campaigns_opened']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  ['opt_in'])])),\n",
       "                ('logreg',\n",
       "                 LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                                  1.0: 7.048360235716116},\n",
       "                                    max_iter=5000, solver='saga'))])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pipeline\n",
    "\n",
    "pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preproc),\n",
    "    ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n",
    "                                  max_iter=5000))  \n",
    "])\n",
    "\n",
    "pipeline.set_output(transform=\"pandas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed415f60-9663-4179-877b-233faf6e1645",
   "metadata": {},
   "source": [
    "## Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b467511-2ae5-4a16-a502-397c3460471d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                                  1.0: 7.048360235716116},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                                  1.0: 7.048360235716116},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;,\n",
       "                                  &#x27;nb_suppliers&#x27;, &#x27;nb_tickets_internet&#x27;,\n",
       "                                  &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse_output=False))]),\n",
       "                                 [&#x27;opt_in&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;nb_tickets_internet&#x27;, &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;opt_in&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                 1.0: 7.048360235716116},\n",
       "                   max_iter=5000, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['nb_tickets', 'nb_purchases',\n",
       "                                                   'total_amount',\n",
       "                                                   'nb_suppliers',\n",
       "                                                   'nb_tickets_internet',\n",
       "                                                   'nb_campaigns',\n",
       "                                                   'nb_campaigns_opened']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  ['opt_in'])])),\n",
       "                ('logreg',\n",
       "                 LogisticRegression(class_weight={0.0: 0.5381774965030861,\n",
       "                                                  1.0: 7.048360235716116},\n",
       "                                    max_iter=5000, solver='saga'))])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6356e870-0dfc-4e60-9e48-e2de5e7f9f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.7563189143341815\n",
      "F1 Score: 0.2737001769613213\n",
      "Recall Score: 0.6419606680502026\n"
     ]
    }
   ],
   "source": [
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# Calculate the F1 score\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy Score: {acc}\")\n",
    "\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"F1 Score: {f1}\")\n",
    "\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f\"Recall Score: {recall}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "09387a09-0d53-4c54-baac-f3c2a57a629a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "580b58d7-596f-4207-8c99-4365aba2bc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_roc_curve(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7f0535de-34f1-4e97-b993-b429ecf0a554",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train['y_has_purchased']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f7fca463-d7d6-493b-8329-fdfa92457f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found:  {'logreg__C': 0.0009765625, 'logreg__class_weight': 'balanced', 'logreg__penalty': 'l1'}\n",
      "Best cross-validation score: 0.65\n",
      "Test set score: 0.64\n"
     ]
    }
   ],
   "source": [
    "# Cross validation\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, param_grid, cv=3, scoring=recall_scorer, error_score='raise',\n",
    "                          n_jobs=-1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and the best score\n",
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n",
    "\n",
    "# Evaluate the best model on the test set\n",
    "test_score = grid_search.score(X_test, y_test)\n",
    "print(\"Test set score: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "56bd7828-4de1-4166-bea0-5d5e152b9d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = grid_search.predict(X_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "319fe0eb-4d4a-492c-bd50-3f08ab483021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_roc_curve(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "210b931c-6d46-4ebf-a9c7-d1ee05c3fadf",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}