diff --git "a/notebooks/Supervised-Unsupervised/customer_churn.ipynb" "b/notebooks/Supervised-Unsupervised/customer_churn.ipynb" deleted file mode 100644--- "a/notebooks/Supervised-Unsupervised/customer_churn.ipynb" +++ /dev/null @@ -1,713 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt \n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Telco customer churn \n" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [], - "source": [ - "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\data-hec-AI-DS\\telco-customer-churn.csv\"\n", - "path_savedata = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-ai-ds-hec\\data\\classification\\churn\"" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [], - "source": [ - "churn_data = pd.read_csv(path_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 7043 entries, 0 to 7042\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 customerID 7043 non-null object \n", - " 1 gender 7043 non-null object \n", - " 2 SeniorCitizen 7043 non-null int64 \n", - " 3 Partner 7043 non-null object \n", - " 4 Dependents 7043 non-null object \n", - " 5 tenure 7043 non-null int64 \n", - " 6 PhoneService 7043 non-null object \n", - " 7 MultipleLines 7043 non-null object \n", - " 8 InternetService 7043 non-null object \n", - " 9 OnlineSecurity 7043 non-null object \n", - " 10 OnlineBackup 7043 non-null object \n", - " 11 DeviceProtection 7043 non-null object \n", - " 12 TechSupport 7043 non-null object \n", - " 13 StreamingTV 7043 non-null object \n", - " 14 StreamingMovies 7043 non-null object \n", - " 15 Contract 7043 non-null object \n", - " 16 PaperlessBilling 7043 non-null object \n", - " 17 PaymentMethod 7043 non-null object \n", - " 18 MonthlyCharges 7043 non-null float64\n", - " 19 TotalCharges 7043 non-null object \n", - " 20 Churn 7043 non-null object \n", - "dtypes: float64(1), int64(2), object(18)\n", - "memory usage: 1.1+ MB\n" - ] - } - ], - "source": [ - "churn_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": {}, - "outputs": [], - "source": [ - "churn_data.drop(columns=[\"gender\",\"customerID\"],inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [], - "source": [ - "churn_data[\"MultipleLines\"] = churn_data[\"MultipleLines\"].apply(lambda x: 0 if x in [\"No\",\"No phone service\"] else 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [], - "source": [ - "for col in [\"OnlineSecurity\", \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\"]:\n", - " churn_data[col] = churn_data[col].apply(lambda x: 0 if x in [\"No internet service\", \"No\"] else 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [], - "source": [ - "binary_columns = [\"Partner\", \"Dependents\", \"PhoneService\", \"PaperlessBilling\", \"Churn\"]\n", - "for col in binary_columns:\n", - " churn_data[col] = churn_data[col].map({\"No\":0, \"Yes\":1})" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [], - "source": [ - "churn_data[\"TotalCharges\"] = churn_data[\"TotalCharges\"].apply(lambda x: np.nan if x==' ' else x).astype(float)" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PaymentMethod\n", - "Electronic check 2365\n", - "Mailed check 1612\n", - "Bank transfer (automatic) 1544\n", - "Credit card (automatic) 1522\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 195, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "churn_data[\"PaymentMethod\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": {}, - "outputs": [], - "source": [ - "def clean_paymentmethod(x):\n", - " if x == \"Bank transfer (automatic)\":\n", - " return \"Bank transfer\"\n", - " elif x == \"Credit card (automatic)\":\n", - " return \"Credit card\"\n", - " else:\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [], - "source": [ - "churn_data[\"PaymentMethod\"] = churn_data[\"PaymentMethod\"].apply(clean_paymentmethod)" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Electronic check', 'Mailed check', 'Bank transfer', 'Credit card'],\n", - " dtype=object)" - ] - }, - "execution_count": 198, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "churn_data[\"PaymentMethod\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [], - "source": [ - "churn_data.dropna(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [], - "source": [ - "churn_data.head(30).to_pickle(os.path.join(path_savedata, \"churn_train_raw.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 201, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.kdeplot(data=churn_data, x=\"MonthlyCharges\", hue=\"Churn\", fill=\"Churn\")" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Exploratory Data Analysis on categorical variables\n", - "\n", - "cat_columns_plot = [col for col in churn_data.select_dtypes(include=[\"object\",\"int\"]).columns if col not in [\"Churn\",\"tenure\"]]\n", - "\n", - "plt.figure(figsize=(20,12))\n", - "for i,col in enumerate(cat_columns_plot):\n", - " plt.subplot(4,4,i+1)\n", - "\n", - " df_plot = pd.crosstab(churn_data[col], churn_data[\"Churn\"].map({0:\"No\", 1:\"Yes\"}), normalize=\"index\").apply(lambda x: np.round(100*x)).reset_index(drop=False)\n", - " df_plot.rename_axis(None, axis=1, inplace=True)\n", - " df_plot = df_plot.melt(id_vars=[col])\n", - " df_plot.rename({\"variable\":\"Churn\"},axis=1, inplace=True)\n", - "\n", - " sns.barplot(data = df_plot, x=col, y=\"value\", hue=\"Churn\")\n", - " plt.xlabel(\"\")\n", - " plt.ylabel(\"\")\n", - " #plt.xticks(rotation=10)\n", - " plt.title(str(col))\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "X = churn_data.drop(columns=[\"Churn\"])\n", - "y = churn_data[\"Churn\"]\n", - "\n", - "# Split train/test set\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "metadata": {}, - "outputs": [], - "source": [ - "# Save raw test data for app \n", - "X_test_save = X_test.copy()[:1000]\n", - "X_test_save.to_pickle(os.path.join(path_savedata,\"churn_test_raw.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, OneHotEncoder\n", - "\n", - "num_columns = list(churn_data.select_dtypes(include=[\"float\"]).columns)\n", - "num_columns.append(\"tenure\")\n", - "\n", - "cat_columns = churn_data.select_dtypes(include=[\"object\"]).columns\n", - "\n", - "# Build data processing pipeline\n", - "ct = ColumnTransformer(\n", - " [(\"numerical\", RobustScaler(), num_columns), \n", - " (\"categorical\", OneHotEncoder(sparse_output=False), cat_columns)], \n", - " remainder='passthrough')\n", - "\n", - "X_train_pp = ct.fit_transform(X_train)\n", - "X_test_pp = ct.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": {}, - "outputs": [], - "source": [ - "# Save preprocessed test data \n", - "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n", - "\n", - "X_test_save_pp = X_test_pp.copy()[:1000]\n", - "X_test_save_pp = pd.DataFrame(X_test_save_pp, columns=columns_transform)\n", - "X_test_save_pp.to_pickle(os.path.join(path_savedata,\"churn_test_pp.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train Accuracy 0.8922666666666667\n", - "Test Accuracy 0.7995735607675906\n" - ] - } - ], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, roc_auc_score\n", - "\n", - "rf = RandomForestClassifier(max_depth=12, class_weight=\"balanced\", min_samples_split=5)\n", - "rf.fit(X_train_pp, y_train)\n", - "\n", - "y_pred_train = rf.predict(X_train_pp)\n", - "y_pred_test = rf.predict(X_test_pp)\n", - "\n", - "print(\"Train Accuracy\", accuracy_score(y_train, y_pred_train))\n", - "print(\"Test Accuracy\", accuracy_score(y_test, y_pred_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "\n", - "path_model = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-ai-ds-hec\\pretrained_models\\supervised_learning\"\n", - "with open(os.path.join(path_model,'churn_model.pkl'),'wb') as f:\n", - " pickle.dump(rf,f)" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 209, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", - "\n", - "cm = confusion_matrix(y_train, y_pred_train, normalize=\"true\")*100\n", - "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rf.classes_)\n", - "disp.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [], - "source": [ - "df_cm = pd.DataFrame(cm, columns=[\"No Churn\",\"Churn\"])\n", - "#df_cm.insert(0,\"Label\",[\"Good\",\"Poor\",\"Standard\"])\n", - "df_cm.to_pickle(os.path.join(path_savedata,\"churn_cm_train.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [], - "source": [ - "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n", - "\n", - "df_features_imp = pd.DataFrame({\"variable\":columns_transform,\n", - " \"importance\":rf.feature_importances_})\n", - "\n", - "df_features_imp[\"variable\"] = df_features_imp[\"variable\"].apply(lambda x: x.split(\"_\")[0] if \"_\" in x else x)\n", - "df_features_imp = df_features_imp.groupby(\"variable\")[\"importance\"].mean().to_frame().reset_index()\n", - "df_features_imp.sort_values(by=\"importance\", ascending=False, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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variableimportance
17tenure0.150953
16TotalCharges0.136729
4MonthlyCharges0.135956
0Contract0.078704
3InternetService0.029338
7OnlineSecurity0.023258
8PaperlessBilling0.021521
10PaymentMethod0.017772
15TechSupport0.017222
9Partner0.016507
1Dependents0.014212
6OnlineBackup0.014165
5MultipleLines0.014120
13StreamingMovies0.013998
14StreamingTV0.013701
12SeniorCitizen0.013402
2DeviceProtection0.011463
11PhoneService0.007577
\n", - "
" - ], - "text/plain": [ - " variable importance\n", - "17 tenure 0.150953\n", - "16 TotalCharges 0.136729\n", - "4 MonthlyCharges 0.135956\n", - "0 Contract 0.078704\n", - "3 InternetService 0.029338\n", - "7 OnlineSecurity 0.023258\n", - "8 PaperlessBilling 0.021521\n", - "10 PaymentMethod 0.017772\n", - "15 TechSupport 0.017222\n", - "9 Partner 0.016507\n", - "1 Dependents 0.014212\n", - "6 OnlineBackup 0.014165\n", - "5 MultipleLines 0.014120\n", - "13 StreamingMovies 0.013998\n", - "14 StreamingTV 0.013701\n", - "12 SeniorCitizen 0.013402\n", - "2 DeviceProtection 0.011463\n", - "11 PhoneService 0.007577" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_features_imp" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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", 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