2071 lines
454 KiB
Plaintext
2071 lines
454 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3415114e-9577-4487-89eb-4931620ad9f0",
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"metadata": {},
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"source": [
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"# Predict Sales"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f271eb45-1470-4764-8c2e-31374efa1fe5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import s3fs\n",
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"import re\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n",
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"from sklearn.utils import class_weight\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.preprocessing import OneHotEncoder\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n",
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"from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
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"from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n",
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"\n",
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"import pickle\n",
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"import warnings\n",
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"#import scikitplot as skplt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3fecb606-22e5-4dee-8efa-f8dff0832299",
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"metadata": {},
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"outputs": [],
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"source": [
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"warnings.filterwarnings('ignore')\n",
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"warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
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"warnings.filterwarnings(\"ignore\", category=DataConversionWarning)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ae591854-3003-4c75-a0c7-5abf04246e81",
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"metadata": {},
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"source": [
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"### Load Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "59dd4694-a812-4923-b995-a2ee86c74f85",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create filesystem object\n",
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"S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
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"fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "017f7e9a-3ba0-40fa-bdc8-51b98cc1fdb3",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_train_test():\n",
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" BUCKET = \"projet-bdc2324-team1/Generalization/musee\"\n",
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" File_path_train = BUCKET + \"/Train_set.csv\"\n",
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" File_path_test = BUCKET + \"/Test_set.csv\"\n",
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" \n",
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" with fs.open( File_path_train, mode=\"rb\") as file_in:\n",
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" dataset_train = pd.read_csv(file_in, sep=\",\")\n",
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" # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n",
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"\n",
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" with fs.open(File_path_test, mode=\"rb\") as file_in:\n",
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" dataset_test = pd.read_csv(file_in, sep=\",\")\n",
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" # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n",
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" \n",
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" return dataset_train, dataset_test"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "c479b230-b4bd-4cfb-b76b-d9faf6d95772",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset_train, dataset_test = load_train_test()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "c24c446d-4e1c-4ac1-a048-f0b8d8559f36",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"customer_id 0\n",
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"nb_tickets 0\n",
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"nb_purchases 0\n",
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"total_amount 0\n",
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"nb_suppliers 0\n",
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"vente_internet_max 0\n",
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"purchase_date_min 0\n",
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"purchase_date_max 0\n",
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"time_between_purchase 0\n",
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"nb_tickets_internet 0\n",
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"street_id 0\n",
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"structure_id 389658\n",
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"mcp_contact_id 150354\n",
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"fidelity 0\n",
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"tenant_id 0\n",
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"is_partner 0\n",
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"deleted_at 434278\n",
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"gender 0\n",
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"is_email_true 0\n",
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"opt_in 0\n",
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"last_buying_date 183987\n",
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"max_price 183987\n",
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"ticket_sum 0\n",
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"average_price 94783\n",
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"average_purchase_delay 183987\n",
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"average_price_basket 183987\n",
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"average_ticket_basket 183987\n",
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"total_price 89204\n",
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"purchase_count 0\n",
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"first_buying_date 183987\n",
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"country 141237\n",
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"gender_label 0\n",
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"gender_female 0\n",
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"gender_male 0\n",
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"gender_other 0\n",
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"country_fr 141237\n",
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"nb_campaigns 0\n",
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"nb_campaigns_opened 0\n",
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"time_to_open 258182\n",
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"y_has_purchased 0\n",
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"dtype: int64"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dataset_train.isna().sum()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "825d14a3-6967-4733-bfd4-64bf61c2bd43",
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"metadata": {},
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"outputs": [],
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"source": [
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"def features_target_split(dataset_train, dataset_test):\n",
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" features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n",
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" 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n",
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" 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n",
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" X_train = dataset_train[features_l]\n",
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" y_train = dataset_train[['y_has_purchased']]\n",
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"\n",
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" X_test = dataset_test[features_l]\n",
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" y_test = dataset_test[['y_has_purchased']]\n",
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" return X_train, X_test, y_train, y_test"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "69eaec12-b30f-4d30-a461-ea520d5cbf77",
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "d039f31d-0093-46c6-9743-ddec1381f758",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Shape train : (434278, 17)\n",
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"Shape test : (186120, 17)\n"
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]
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}
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],
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"source": [
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"print(\"Shape train : \", X_train.shape)\n",
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"print(\"Shape test : \", X_test.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1d6de94-4e11-481a-a0ce-412bf29f692c",
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"metadata": {},
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"source": [
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"### Prepare preprocessing and Hyperparameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"id": "b808da43-c444-4e94-995a-7ec6ccd01e2d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{0.0: 0.5223906809346011, 1.0: 11.665359406898034}"
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]
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},
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Compute Weights\n",
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"weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n",
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" y = y_train['y_has_purchased'])\n",
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"\n",
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"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",
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"weight_dict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"id": "b32a79ea-907f-4dfc-9832-6c74bef3200c",
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"metadata": {},
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"outputs": [],
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"source": [
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"numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n",
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" 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n",
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" 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n",
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"\n",
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"numeric_transformer = Pipeline(steps=[\n",
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" #(\"imputer\", SimpleImputer(strategy=\"mean\")), \n",
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" (\"scaler\", StandardScaler()) \n",
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"])\n",
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"\n",
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"categorical_features = ['opt_in'] \n",
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"\n",
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"# Transformer for the categorical features\n",
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"categorical_transformer = Pipeline(steps=[\n",
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" #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n",
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" (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
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"])\n",
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"\n",
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"preproc = ColumnTransformer(\n",
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" transformers=[\n",
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" (\"num\", numeric_transformer, numeric_features),\n",
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" (\"cat\", categorical_transformer, categorical_features)\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"id": "9809a688-bfbc-4685-a77f-17a8b2b79ab3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set loss\n",
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"balanced_scorer = make_scorer(balanced_accuracy_score)\n",
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"recall_scorer = make_scorer(recall_score)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"id": "4f9b2bbf-5f8a-4ac1-8e6c-51bd0dd8ac85",
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"metadata": {},
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"outputs": [],
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"source": [
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"def draw_confusion_matrix(y_test, y_pred):\n",
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" conf_matrix = confusion_matrix(y_test, y_pred)\n",
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" sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
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" plt.xlabel('Predicted')\n",
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" plt.ylabel('Actual')\n",
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" plt.title('Confusion Matrix')\n",
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" plt.show()\n",
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"\n",
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"\n",
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"def draw_roc_curve(X_test, y_test):\n",
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" y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n",
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"\n",
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" # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n",
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" fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n",
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" \n",
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" # Calcul de l'aire sous la courbe ROC (AUC)\n",
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" roc_auc = auc(fpr, tpr)\n",
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" \n",
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" plt.figure(figsize = (14, 8))\n",
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" plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n",
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" plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n",
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" plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
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" plt.xlabel('Taux de faux positifs (FPR)')\n",
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" plt.ylabel('Taux de vrais positifs (TPR)')\n",
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" plt.title('Courbe ROC : modèle logistique')\n",
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" plt.legend(loc=\"lower right\")\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "206d9a95-7c37-4506-949b-e77d225e42c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Hyperparameter\n",
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"param_grid = {'logreg__C': np.logspace(-10, 6, 17, base=2),\n",
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" 'logreg__penalty': ['l1', 'l2'],\n",
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" 'logreg__class_weight': ['balanced', weight_dict]} "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "7ff2f7bd-efc1-4f7c-a3c9-caa916aa2f2b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style>#sk-container-id-4 {\n",
|
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" /* Definition of color scheme common for light and dark mode */\n",
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" --sklearn-color-text: black;\n",
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" --sklearn-color-line: gray;\n",
|
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" /* Definition of color scheme for unfitted estimators */\n",
|
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" --sklearn-color-unfitted-level-0: #fff5e6;\n",
|
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" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
|
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" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
|
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" --sklearn-color-unfitted-level-3: chocolate;\n",
|
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" /* Definition of color scheme for fitted estimators */\n",
|
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" --sklearn-color-fitted-level-0: #f0f8ff;\n",
|
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" --sklearn-color-fitted-level-1: #d4ebff;\n",
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" --sklearn-color-fitted-level-2: #b3dbfd;\n",
|
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" --sklearn-color-fitted-level-3: cornflowerblue;\n",
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"\n",
|
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" /* Specific color for light theme */\n",
|
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" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
|
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" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
|
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" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
|
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" --sklearn-color-icon: #696969;\n",
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"\n",
|
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" @media (prefers-color-scheme: dark) {\n",
|
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" /* Redefinition of color scheme for dark theme */\n",
|
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" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
|
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" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
|
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" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
|
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" --sklearn-color-icon: #878787;\n",
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" }\n",
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"}\n",
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"\n",
|
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"#sk-container-id-4 {\n",
|
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" color: var(--sklearn-color-text);\n",
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"}\n",
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"\n",
|
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"#sk-container-id-4 pre {\n",
|
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" padding: 0;\n",
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"}\n",
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"\n",
|
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"#sk-container-id-4 input.sk-hidden--visually {\n",
|
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" border: 0;\n",
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" clip: rect(1px 1px 1px 1px);\n",
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" clip: rect(1px, 1px, 1px, 1px);\n",
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" height: 1px;\n",
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" margin: -1px;\n",
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" overflow: hidden;\n",
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" padding: 0;\n",
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" position: absolute;\n",
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" width: 1px;\n",
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"}\n",
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"\n",
|
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"#sk-container-id-4 div.sk-dashed-wrapped {\n",
|
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" border: 1px dashed var(--sklearn-color-line);\n",
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" margin: 0 0.4em 0.5em 0.4em;\n",
|
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" box-sizing: border-box;\n",
|
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" padding-bottom: 0.4em;\n",
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" background-color: var(--sklearn-color-background);\n",
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"}\n",
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"\n",
|
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"#sk-container-id-4 div.sk-container {\n",
|
|
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
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|
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
|
|
" so we also need the `!important` here to be able to override the\n",
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" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
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" 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=[('preprocessor',\n",
|
|
" ColumnTransformer(transformers=[('num',\n",
|
|
" Pipeline(steps=[('scaler',\n",
|
|
" StandardScaler())]),\n",
|
|
" ['nb_tickets', 'nb_purchases',\n",
|
|
" 'total_amount',\n",
|
|
" 'nb_suppliers',\n",
|
|
" 'vente_internet_max',\n",
|
|
" 'purchase_date_min',\n",
|
|
" 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet',\n",
|
|
" 'fidelity', 'is_email_true',\n",
|
|
" 'opt_in', 'gender_female',\n",
|
|
" 'gender_male',\n",
|
|
" 'gender_other',\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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\n",
|
|
" max_iter=5000, solver='saga'))])</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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow \"> 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=[('preprocessor',\n",
|
|
" ColumnTransformer(transformers=[('num',\n",
|
|
" Pipeline(steps=[('scaler',\n",
|
|
" StandardScaler())]),\n",
|
|
" ['nb_tickets', 'nb_purchases',\n",
|
|
" 'total_amount',\n",
|
|
" 'nb_suppliers',\n",
|
|
" 'vente_internet_max',\n",
|
|
" 'purchase_date_min',\n",
|
|
" 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet',\n",
|
|
" 'fidelity', 'is_email_true',\n",
|
|
" 'opt_in', 'gender_female',\n",
|
|
" 'gender_male',\n",
|
|
" 'gender_other',\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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\n",
|
|
" max_iter=5000, solver='saga'))])</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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow \"> 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=[('num',\n",
|
|
" Pipeline(steps=[('scaler', StandardScaler())]),\n",
|
|
" ['nb_tickets', 'nb_purchases', 'total_amount',\n",
|
|
" 'nb_suppliers', 'vente_internet_max',\n",
|
|
" 'purchase_date_min', 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet', 'fidelity',\n",
|
|
" 'is_email_true', 'opt_in', 'gender_female',\n",
|
|
" 'gender_male', 'gender_other', 'nb_campaigns',\n",
|
|
" 'nb_campaigns_opened']),\n",
|
|
" ('cat',\n",
|
|
" Pipeline(steps=[('onehot',\n",
|
|
" OneHotEncoder(handle_unknown='ignore',\n",
|
|
" sparse_output=False))]),\n",
|
|
" ['opt_in'])])</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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']</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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow \"> 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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow \">cat</label><div class=\"sk-toggleable__content \"><pre>['opt_in']</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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow \"> 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='ignore', 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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow \"> 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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\n",
|
|
" max_iter=5000, solver='saga')</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",
|
|
" 'vente_internet_max',\n",
|
|
" 'purchase_date_min',\n",
|
|
" 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet',\n",
|
|
" 'fidelity', 'is_email_true',\n",
|
|
" 'opt_in', 'gender_female',\n",
|
|
" 'gender_male',\n",
|
|
" 'gender_other',\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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\n",
|
|
" max_iter=5000, solver='saga'))])"
|
|
]
|
|
},
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Pipeline\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-5 {\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-5 {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 pre {\n",
|
|
" padding: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
|
|
" display: flex;\n",
|
|
" flex-direction: column;\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
|
|
" align-self: flex-end;\n",
|
|
" width: 50%;\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
|
|
" align-self: flex-start;\n",
|
|
" width: 50%;\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
|
|
" width: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Serial-specific style estimator block */\n",
|
|
"\n",
|
|
"#sk-container-id-5 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-5 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-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Toggleable content - dropdown */\n",
|
|
"\n",
|
|
"#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
|
|
"#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
|
|
" text-align: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Estimator-specific */\n",
|
|
"#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* on hover */\n",
|
|
"#sk-container-id-5 div.sk-estimator:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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",
|
|
" 'vente_internet_max',\n",
|
|
" 'purchase_date_min',\n",
|
|
" 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet',\n",
|
|
" 'fidelity', 'is_email_true',\n",
|
|
" 'opt_in', 'gender_female',\n",
|
|
" 'gender_male',\n",
|
|
" 'gender_other',\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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\n",
|
|
" max_iter=5000, solver='saga'))])</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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> 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=[('preprocessor',\n",
|
|
" ColumnTransformer(transformers=[('num',\n",
|
|
" Pipeline(steps=[('scaler',\n",
|
|
" StandardScaler())]),\n",
|
|
" ['nb_tickets', 'nb_purchases',\n",
|
|
" 'total_amount',\n",
|
|
" 'nb_suppliers',\n",
|
|
" 'vente_internet_max',\n",
|
|
" 'purchase_date_min',\n",
|
|
" 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet',\n",
|
|
" 'fidelity', 'is_email_true',\n",
|
|
" 'opt_in', 'gender_female',\n",
|
|
" 'gender_male',\n",
|
|
" 'gender_other',\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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\n",
|
|
" max_iter=5000, solver='saga'))])</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-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> 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=[('num',\n",
|
|
" Pipeline(steps=[('scaler', StandardScaler())]),\n",
|
|
" ['nb_tickets', 'nb_purchases', 'total_amount',\n",
|
|
" 'nb_suppliers', 'vente_internet_max',\n",
|
|
" 'purchase_date_min', 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet', 'fidelity',\n",
|
|
" 'is_email_true', 'opt_in', 'gender_female',\n",
|
|
" 'gender_male', 'gender_other', 'nb_campaigns',\n",
|
|
" 'nb_campaigns_opened']),\n",
|
|
" ('cat',\n",
|
|
" Pipeline(steps=[('onehot',\n",
|
|
" OneHotEncoder(handle_unknown='ignore',\n",
|
|
" sparse_output=False))]),\n",
|
|
" ['opt_in'])])</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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']</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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> 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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>['opt_in']</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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> 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='ignore', 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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> 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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\n",
|
|
" max_iter=5000, solver='saga')</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",
|
|
" 'vente_internet_max',\n",
|
|
" 'purchase_date_min',\n",
|
|
" 'purchase_date_max',\n",
|
|
" 'time_between_purchase',\n",
|
|
" 'nb_tickets_internet',\n",
|
|
" 'fidelity', 'is_email_true',\n",
|
|
" 'opt_in', 'gender_female',\n",
|
|
" 'gender_male',\n",
|
|
" 'gender_other',\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: 0.5223906809346011,\n",
|
|
" 1.0: 11.665359406898034},\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": 38,
|
|
"id": "6356e870-0dfc-4e60-9e48-e2de5e7f9f87",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy Score: 0.9083440790887599\n",
|
|
"F1 Score: 0.4349266289045679\n",
|
|
"Recall Score: 0.8231974921630094\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": 39,
|
|
"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": 40,
|
|
"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": {
|
|
"jp-MarkdownHeadingCollapsed": true
|
|
},
|
|
"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": "markdown",
|
|
"id": "ab122f66-1591-43ea-a364-2564f09b2bb3",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Segmentation du score de prédiction"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"id": "279e18c7-29d8-4328-963a-18babd13c2c8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"coefficients = pipeline.named_steps['logreg'].coef_[0]\n",
|
|
"feature_names = pipeline.named_steps['logreg'].feature_names_in_\n",
|
|
"\n",
|
|
"# Tracer l'importance des caractéristiques\n",
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
"plt.barh(feature_names, coefficients, color='skyblue')\n",
|
|
"plt.xlabel('Importance des caractéristiques')\n",
|
|
"plt.ylabel('Caractéristiques')\n",
|
|
"plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n",
|
|
"plt.grid(True)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 70,
|
|
"id": "210b931c-6d46-4ebf-a9c7-d1ee05c3fadf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Création d'un dataframe avec le score\n",
|
|
"dataset_for_segmentation = dataset_test[['customer_id'] + numeric_features + categorical_features]\n",
|
|
"\n",
|
|
"y_predict_proba = pipeline.predict_proba(X_test)[:, 1]\n",
|
|
"\n",
|
|
"dataset_for_segmentation['prediction_probability'] = y_predict_proba\n",
|
|
"\n",
|
|
"# Arrondir les valeurs de la colonne 'prediction_probability' et les multiplier par 10\n",
|
|
"dataset_for_segmentation['category'] = dataset_for_segmentation['prediction_probability'].apply(lambda x: int(x * 10))\n",
|
|
"\n",
|
|
"dataset_for_segmentation['prediction'] = y_pred\n",
|
|
"\n",
|
|
"def premiere_partie(chaine):\n",
|
|
" if chaine:\n",
|
|
" return chaine.split('_')[0]\n",
|
|
" else:\n",
|
|
" return None\n",
|
|
"\n",
|
|
"dataset_for_segmentation['company_number'] = dataset_for_segmentation['customer_id'].apply(lambda x: premiere_partie(x))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "055e47dd-9ff3-4853-a46d-d5a5edc1f361",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 73,
|
|
"id": "969f1f92-d715-4d74-85a7-437e72838cb5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
" }\n",
|
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"\n",
|
|
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
|
" .dataframe thead tr th {\n",
|
|
" text-align: left;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead tr:last-of-type th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th>nb_tickets</th>\n",
|
|
" <th>nb_purchases</th>\n",
|
|
" <th>total_amount</th>\n",
|
|
" <th>nb_suppliers</th>\n",
|
|
" <th>vente_internet_max</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>fidelity</th>\n",
|
|
" <th>gender_female</th>\n",
|
|
" <th>gender_male</th>\n",
|
|
" <th>gender_other</th>\n",
|
|
" <th>nb_campaigns</th>\n",
|
|
" <th>nb_campaigns_opened</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>mean</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>category</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0.113637</td>\n",
|
|
" <td>0.006274</td>\n",
|
|
" <td>1.586366</td>\n",
|
|
" <td>0.005821</td>\n",
|
|
" <td>0.000647</td>\n",
|
|
" <td>548.790455</td>\n",
|
|
" <td>548.773103</td>\n",
|
|
" <td>-0.977118</td>\n",
|
|
" <td>0.001585</td>\n",
|
|
" <td>0.000776</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000032</td>\n",
|
|
" <td>0.999968</td>\n",
|
|
" <td>13.984219</td>\n",
|
|
" <td>1.302720</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0.810841</td>\n",
|
|
" <td>0.128432</td>\n",
|
|
" <td>9.611292</td>\n",
|
|
" <td>0.125295</td>\n",
|
|
" <td>0.018186</td>\n",
|
|
" <td>525.437516</td>\n",
|
|
" <td>525.275222</td>\n",
|
|
" <td>-0.729328</td>\n",
|
|
" <td>0.054312</td>\n",
|
|
" <td>0.111832</td>\n",
|
|
" <td>0.245480</td>\n",
|
|
" <td>0.495929</td>\n",
|
|
" <td>0.258591</td>\n",
|
|
" <td>18.413562</td>\n",
|
|
" <td>3.718711</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1.159419</td>\n",
|
|
" <td>0.339253</td>\n",
|
|
" <td>15.182143</td>\n",
|
|
" <td>0.337577</td>\n",
|
|
" <td>0.323824</td>\n",
|
|
" <td>501.529129</td>\n",
|
|
" <td>501.415505</td>\n",
|
|
" <td>-0.554439</td>\n",
|
|
" <td>0.969939</td>\n",
|
|
" <td>0.304757</td>\n",
|
|
" <td>0.392570</td>\n",
|
|
" <td>0.297258</td>\n",
|
|
" <td>0.310173</td>\n",
|
|
" <td>17.395042</td>\n",
|
|
" <td>2.608084</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2.153080</td>\n",
|
|
" <td>0.744161</td>\n",
|
|
" <td>27.820044</td>\n",
|
|
" <td>0.734881</td>\n",
|
|
" <td>0.600982</td>\n",
|
|
" <td>287.051054</td>\n",
|
|
" <td>286.675385</td>\n",
|
|
" <td>0.105360</td>\n",
|
|
" <td>1.776035</td>\n",
|
|
" <td>0.659878</td>\n",
|
|
" <td>0.288813</td>\n",
|
|
" <td>0.253244</td>\n",
|
|
" <td>0.457943</td>\n",
|
|
" <td>16.790421</td>\n",
|
|
" <td>4.173954</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2.044749</td>\n",
|
|
" <td>0.777640</td>\n",
|
|
" <td>27.353145</td>\n",
|
|
" <td>0.754549</td>\n",
|
|
" <td>0.079213</td>\n",
|
|
" <td>297.179255</td>\n",
|
|
" <td>295.019902</td>\n",
|
|
" <td>1.898178</td>\n",
|
|
" <td>0.293760</td>\n",
|
|
" <td>0.894877</td>\n",
|
|
" <td>0.666980</td>\n",
|
|
" <td>0.301424</td>\n",
|
|
" <td>0.031596</td>\n",
|
|
" <td>16.954707</td>\n",
|
|
" <td>6.060621</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>3.237988</td>\n",
|
|
" <td>0.958520</td>\n",
|
|
" <td>46.637380</td>\n",
|
|
" <td>0.807655</td>\n",
|
|
" <td>0.484785</td>\n",
|
|
" <td>387.464785</td>\n",
|
|
" <td>380.145068</td>\n",
|
|
" <td>7.111357</td>\n",
|
|
" <td>2.080397</td>\n",
|
|
" <td>1.164958</td>\n",
|
|
" <td>0.497758</td>\n",
|
|
" <td>0.259769</td>\n",
|
|
" <td>0.242473</td>\n",
|
|
" <td>27.006406</td>\n",
|
|
" <td>12.457719</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>3.592233</td>\n",
|
|
" <td>1.102881</td>\n",
|
|
" <td>49.989226</td>\n",
|
|
" <td>0.878014</td>\n",
|
|
" <td>0.599906</td>\n",
|
|
" <td>268.627019</td>\n",
|
|
" <td>250.949344</td>\n",
|
|
" <td>17.539247</td>\n",
|
|
" <td>2.525994</td>\n",
|
|
" <td>1.420921</td>\n",
|
|
" <td>0.534607</td>\n",
|
|
" <td>0.304259</td>\n",
|
|
" <td>0.161134</td>\n",
|
|
" <td>14.073285</td>\n",
|
|
" <td>4.604134</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>3.747016</td>\n",
|
|
" <td>1.391266</td>\n",
|
|
" <td>40.710335</td>\n",
|
|
" <td>0.914702</td>\n",
|
|
" <td>0.160990</td>\n",
|
|
" <td>309.716173</td>\n",
|
|
" <td>274.795570</td>\n",
|
|
" <td>34.796876</td>\n",
|
|
" <td>0.844250</td>\n",
|
|
" <td>1.963028</td>\n",
|
|
" <td>0.650364</td>\n",
|
|
" <td>0.263464</td>\n",
|
|
" <td>0.086172</td>\n",
|
|
" <td>26.186317</td>\n",
|
|
" <td>8.891703</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>5.698276</td>\n",
|
|
" <td>1.567006</td>\n",
|
|
" <td>63.033699</td>\n",
|
|
" <td>0.907915</td>\n",
|
|
" <td>0.334248</td>\n",
|
|
" <td>326.485952</td>\n",
|
|
" <td>257.940194</td>\n",
|
|
" <td>68.425460</td>\n",
|
|
" <td>2.794279</td>\n",
|
|
" <td>2.413009</td>\n",
|
|
" <td>0.606583</td>\n",
|
|
" <td>0.251567</td>\n",
|
|
" <td>0.141850</td>\n",
|
|
" <td>30.987461</td>\n",
|
|
" <td>11.676332</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>14.505956</td>\n",
|
|
" <td>3.211571</td>\n",
|
|
" <td>107.288514</td>\n",
|
|
" <td>1.011628</td>\n",
|
|
" <td>0.157119</td>\n",
|
|
" <td>369.696066</td>\n",
|
|
" <td>209.280306</td>\n",
|
|
" <td>160.348544</td>\n",
|
|
" <td>3.514464</td>\n",
|
|
" <td>5.394498</td>\n",
|
|
" <td>0.669314</td>\n",
|
|
" <td>0.223766</td>\n",
|
|
" <td>0.106920</td>\n",
|
|
" <td>45.928247</td>\n",
|
|
" <td>18.241634</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>2262.859155</td>\n",
|
|
" <td>45.619718</td>\n",
|
|
" <td>11051.732394</td>\n",
|
|
" <td>1.464789</td>\n",
|
|
" <td>0.154930</td>\n",
|
|
" <td>467.111875</td>\n",
|
|
" <td>31.146796</td>\n",
|
|
" <td>435.950994</td>\n",
|
|
" <td>54.295775</td>\n",
|
|
" <td>64.704225</td>\n",
|
|
" <td>0.507042</td>\n",
|
|
" <td>0.295775</td>\n",
|
|
" <td>0.197183</td>\n",
|
|
" <td>53.352113</td>\n",
|
|
" <td>26.070423</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" nb_tickets nb_purchases total_amount nb_suppliers \\\n",
|
|
" mean mean mean mean \n",
|
|
"category \n",
|
|
"0 0.113637 0.006274 1.586366 0.005821 \n",
|
|
"1 0.810841 0.128432 9.611292 0.125295 \n",
|
|
"2 1.159419 0.339253 15.182143 0.337577 \n",
|
|
"3 2.153080 0.744161 27.820044 0.734881 \n",
|
|
"4 2.044749 0.777640 27.353145 0.754549 \n",
|
|
"5 3.237988 0.958520 46.637380 0.807655 \n",
|
|
"6 3.592233 1.102881 49.989226 0.878014 \n",
|
|
"7 3.747016 1.391266 40.710335 0.914702 \n",
|
|
"8 5.698276 1.567006 63.033699 0.907915 \n",
|
|
"9 14.505956 3.211571 107.288514 1.011628 \n",
|
|
"10 2262.859155 45.619718 11051.732394 1.464789 \n",
|
|
"\n",
|
|
" vente_internet_max purchase_date_min purchase_date_max \\\n",
|
|
" mean mean mean \n",
|
|
"category \n",
|
|
"0 0.000647 548.790455 548.773103 \n",
|
|
"1 0.018186 525.437516 525.275222 \n",
|
|
"2 0.323824 501.529129 501.415505 \n",
|
|
"3 0.600982 287.051054 286.675385 \n",
|
|
"4 0.079213 297.179255 295.019902 \n",
|
|
"5 0.484785 387.464785 380.145068 \n",
|
|
"6 0.599906 268.627019 250.949344 \n",
|
|
"7 0.160990 309.716173 274.795570 \n",
|
|
"8 0.334248 326.485952 257.940194 \n",
|
|
"9 0.157119 369.696066 209.280306 \n",
|
|
"10 0.154930 467.111875 31.146796 \n",
|
|
"\n",
|
|
" time_between_purchase nb_tickets_internet fidelity gender_female \\\n",
|
|
" mean mean mean mean \n",
|
|
"category \n",
|
|
"0 -0.977118 0.001585 0.000776 0.000000 \n",
|
|
"1 -0.729328 0.054312 0.111832 0.245480 \n",
|
|
"2 -0.554439 0.969939 0.304757 0.392570 \n",
|
|
"3 0.105360 1.776035 0.659878 0.288813 \n",
|
|
"4 1.898178 0.293760 0.894877 0.666980 \n",
|
|
"5 7.111357 2.080397 1.164958 0.497758 \n",
|
|
"6 17.539247 2.525994 1.420921 0.534607 \n",
|
|
"7 34.796876 0.844250 1.963028 0.650364 \n",
|
|
"8 68.425460 2.794279 2.413009 0.606583 \n",
|
|
"9 160.348544 3.514464 5.394498 0.669314 \n",
|
|
"10 435.950994 54.295775 64.704225 0.507042 \n",
|
|
"\n",
|
|
" gender_male gender_other nb_campaigns nb_campaigns_opened \n",
|
|
" mean mean mean mean \n",
|
|
"category \n",
|
|
"0 0.000032 0.999968 13.984219 1.302720 \n",
|
|
"1 0.495929 0.258591 18.413562 3.718711 \n",
|
|
"2 0.297258 0.310173 17.395042 2.608084 \n",
|
|
"3 0.253244 0.457943 16.790421 4.173954 \n",
|
|
"4 0.301424 0.031596 16.954707 6.060621 \n",
|
|
"5 0.259769 0.242473 27.006406 12.457719 \n",
|
|
"6 0.304259 0.161134 14.073285 4.604134 \n",
|
|
"7 0.263464 0.086172 26.186317 8.891703 \n",
|
|
"8 0.251567 0.141850 30.987461 11.676332 \n",
|
|
"9 0.223766 0.106920 45.928247 18.241634 \n",
|
|
"10 0.295775 0.197183 53.352113 26.070423 "
|
|
]
|
|
},
|
|
"execution_count": 73,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Grouper le DataFrame par la colonne 'category' et calculer la moyenne pour chaque groupe\n",
|
|
"summary_stats = dataset_for_segmentation.groupby('category')[numeric_features].describe()\n",
|
|
"\n",
|
|
"# Sélectionner uniquement la colonne 'mean' pour chaque variable numérique\n",
|
|
"mean_stats = summary_stats.loc[:, (slice(None), 'mean')]\n",
|
|
"\n",
|
|
"# Afficher le DataFrame résultant\n",
|
|
"mean_stats"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 75,
|
|
"id": "14da601e-7b1b-469c-bab1-de8fad4047f2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 800x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot histogram\n",
|
|
"plt.figure(figsize=(8, 6))\n",
|
|
"plt.hist(y_predict_proba, bins=10, range=(0, 1), color='blue', alpha=0.7)\n",
|
|
"\n",
|
|
"# Réglage des limites des axes x et y\n",
|
|
"plt.xlim(0, 1)\n",
|
|
"plt.ylim(0, None) # Laissez le maximum sur l'axe y pour s'ajuster automatiquement\n",
|
|
"\n",
|
|
"plt.title('Histogramme des probabilités pour la classe 1')\n",
|
|
"plt.xlabel('Probabilité')\n",
|
|
"plt.ylabel('Fréquence')\n",
|
|
"plt.grid(True)\n",
|
|
"plt.show()\n"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|