fraud_ml_log_reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "#sklearn\n",
    "\n",
    "from scipy.stats import loguniform\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import f1_score, fbeta_score, make_scorer\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, roc_curve\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "from custom_functions import get_data_summary, our_metrics, eval_metrics#, class_metrics_var_threshold\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  try to initiate  logistic regression model without scale "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# unscaled Data (test_size = 0.25) :\n",
    "\n",
    "X_train = pd.read_csv(\"data/X_train.csv\")\n",
    "X_test = pd.read_csv(\"data/X_test.csv\")\n",
    "y_train = pd.read_csv(\"data/y_train.csv\")\n",
    "y_test = pd.read_csv(\"data/y_test.csv\")\n",
    "\n",
    "# scaled Data - minmax scaler\n",
    "\n",
    "X_train_std = pd.read_csv(\"data/X_train_std.csv\")\n",
    "X_test_std  = pd.read_csv(\"data/X_test_std.csv\")\n",
    "\n",
    "# scaled Data - standard scaler\n",
    "\n",
    "X_train_minmax  = pd.read_csv(\"data/X_train_minmax.csv\")\n",
    "X_test_minmax  = pd.read_csv(\"data/X_test_minmax.csv\")\n",
    "\n",
    "# minmax scaled with x_train_minmax and y_train split using test_size = 0.7\n",
    "\n",
    "X_train_minmax_small  = pd.read_csv(\"data/X_train_minmax_small.csv\")\n",
    "X_train_minmax_large  = pd.read_csv(\"data/X_train_minmax_large.csv\")\n",
    "\n",
    "# standard scaler with x_train_std and y_train split using test_size = 0.7\n",
    "\n",
    "X_train_std_small  = pd.read_csv(\"data/X_train_std_small.csv\")\n",
    "X_train_std_large  = pd.read_csv(\"data/X_train_std_large.csv\")\n",
    "\n",
    "y_train_small  = pd.read_csv(\"data/y_train_small.csv\")\n",
    "y_train_large  = pd.read_csv(\"data/y_train_large.csv\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random search logistic regression \n",
    "\n",
    "RSEED = 42\n",
    "ftwo_scorer = make_scorer(fbeta_score, beta=2)\n",
    "\n",
    "param_grid = {\n",
    "            'solver' : ['newton-cg', 'lbfgs', 'liblinear'],\n",
    "            'penalty' : ['none', 'l1', 'l2', 'elasticnet'],\n",
    "            'C' :  [10, loguniform(1e-5, 100)]#,\n",
    "            #'scoring' : ['roc_auc', 'balanced_accuracy', ftwo_scorer]\n",
    "    }\n",
    "\n",
    "# log_reg = LogisticRegression()\n",
    "# cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "\n",
    "\n",
    "# log_rscv = RandomizedSearchCV(log_reg, param_grid, n_iter=10, \n",
    "#                             verbose= 5, scoring = 'roc_auc', n_jobs=-1, cv=cv, random_state=RSEED)\n",
    "\n",
    "# log_fit = log_rscv.fit(X_train_minmax_small, y_train_small)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.6532951295662254\n",
      "Best Hyperparameters: {'solver': 'lbfgs', 'penalty': 'none', 'C': 10}\n"
     ]
    }
   ],
   "source": [
    "# best score and hyperparameters\n",
    "\n",
    "# print('Best Score: %s' % log_fit.best_score_)\n",
    "# print('Best Hyperparameters: %s' % log_fit.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Best Score: 0.6532951295662254\n",
    "* Best Hyperparameters: {'solver': 'lbfgs', 'penalty': 'none', 'C': 10}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LogisticRegression(C=10, penalty='none')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# training with best\n",
    "# roc_auc score of 0.65\n",
    "\n",
    "best_param = {\n",
    "            'solver' : 'lbfgs',\n",
    "            'penalty' : 'none',\n",
    "            'C' :  10\n",
    "    }\n",
    "\n",
    "\n",
    "log_model = LogisticRegression(**best_param)\n",
    "log_model.fit(X_train_minmax, y_train)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict\n",
    "\n",
    "y_pred_train = log_model.predict(X_train_minmax)\n",
    "y_pred_test = log_model.predict(X_test_minmax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC_AUC train : 0.5014444893925611\n",
      "precision train : 0.5123001230012301\n",
      "balanced accuracy train : 0.5014444893925611\n",
      "____________________________________________________________\n",
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.9211\n",
      "_____________________\n",
      "F1-score: 0.0063\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.0045\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.0039\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.0035\n",
      "_____________________\n",
      "Recall 0.0031\n",
      "_____________________\n",
      "Specificity 0.9997\n",
      "____________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate model\n",
    "\n",
    "print(f\"ROC_AUC train : {roc_auc_score(y_train, y_pred_train)}\")\n",
    "print(f\"precision train : {precision_score(y_train, y_pred_train)}\")\n",
    "print(f\"balanced accuracy train : {balanced_accuracy_score(y_train, y_pred_train)}\")\n",
    "\n",
    "print('______'*10)\n",
    "\n",
    "our_metrics(y_train, y_pred_train)\n",
    "\n",
    "print('______'*10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC_AUC test : 0.501529063446735\n",
      "precision test : 0.5534351145038168\n",
      "balanced accuracy test : 0.5015290634467349\n",
      "____________________________________________________________\n",
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.9212\n",
      "_____________________\n",
      "F1-score: 0.0065\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.0047\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.0041\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.0036\n",
      "_____________________\n",
      "Recall 0.0033\n",
      "_____________________\n",
      "Specificity 0.9998\n",
      "____________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate model\n",
    "\n",
    "\n",
    "print(f\"ROC_AUC test : {roc_auc_score(y_test, y_pred_test)}\")\n",
    "print(f\"precision test : {precision_score(y_test, y_pred_test)}\")\n",
    "print(f\"balanced accuracy test : {balanced_accuracy_score(y_test, y_pred_test)}\")\n",
    "\n",
    "print('______'*10)\n",
    "\n",
    "our_metrics(y_test, y_pred_test)\n",
    "\n",
    "print('______'*10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training with best\n",
    "# roc_auc score of 0.65\n",
    "\n",
    "best_param = {\n",
    "            'solver' : 'lbfgs',\n",
    "            'penalty' : 'none',\n",
    "            'C' :  10\n",
    "    }\n",
    "\n",
    "\n",
    "log_model1 = LogisticRegression(**best_param)\n",
    "log_model1.fit(X_train_std, y_train)\n",
    "\n",
    "# predict\n",
    "\n",
    "y_pred_train_sd = log_model1.predict(X_train_std)\n",
    "y_pred_test_sd = log_model1.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC_AUC test : 0.5015196755276687\n",
      "precision test : 0.555984555984556\n",
      "balanced accuracy test : 0.5015196755276686\n",
      "____________________________________________________________\n",
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.9212\n",
      "_____________________\n",
      "F1-score: 0.0065\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.0047\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.0041\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.0036\n",
      "_____________________\n",
      "Recall 0.0033\n",
      "_____________________\n",
      "Specificity 0.9998\n",
      "____________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate model : X_test_std, y_pred_test_std\n",
    "\n",
    "\n",
    "print(f\"ROC_AUC test : {roc_auc_score(y_test, y_pred_test_sd)}\")\n",
    "print(f\"precision test : {precision_score(y_test, y_pred_test_sd)}\")\n",
    "print(f\"balanced accuracy test : {balanced_accuracy_score(y_test, y_pred_test_sd)}\")\n",
    "\n",
    "print('______'*10)\n",
    "\n",
    "our_metrics(y_test, y_pred_test_sd)\n",
    "\n",
    "print('______'*10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_metrics_var_threshold(fitted_model, X_test, y_test, threshold=0.5):\n",
    "    \"\"\"\n",
    "    function to compute confusion matrix and classification metrics based on passed threshold.\n",
    "    required arguments: fitted model, X_test, y_test, threshold (float, default >= 0.5)\n",
    "    \"\"\"\n",
    "    # predicted probabilities based on fitted model\n",
    "    proba = fitted_model.predict_proba(X_test)\n",
    "    \n",
    "    # predicted y based on passed threshold\n",
    "    y_pred = [int(i>=threshold) for i in proba[:,1]]\n",
    "\n",
    "    # random line for nicer output :)\n",
    "    print('____________________')\n",
    "\n",
    "    # confusion matrix of actual y and predicted y\n",
    "    print ('Confusion Matrix :')\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    print(conf_matrix)\n",
    "    print('____________________')\n",
    "\n",
    "    # ROC AUC Score score of actual y and predicted y\n",
    "    print ('ROC AUC Score :', round(roc_auc_score(y_test, y_pred), 4))\n",
    "    print('____________________')\n",
    "\n",
    "    # FBeta Score score of actual y and predicted y\n",
    "    print ('FBeta Score, Beta = 2 :', round(fbeta_score(y_test, y_pred, beta=2), 4))\n",
    "    print('____________________')\n",
    "\n",
    "    # accuracy score of confusion matrix\n",
    "    print ('Accuracy Score :', round(accuracy_score(y_test, y_pred), 4))\n",
    "    print('____________________')\n",
    "\n",
    "    # classification report of confusion matrix\n",
    "    print ('Report : ')\n",
    "    print (classification_report(y_test, y_pred) )\n",
    "    print('____________________')\n",
    "\n",
    "    return proba\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "Confusion Matrix :\n",
      "[[1030662     234]\n",
      " [  87987     290]]\n",
      "____________________\n",
      "ROC AUC Score : 0.5015\n",
      "____________________\n",
      "FBeta Score, Beta = 2 : 0.0041\n",
      "____________________\n",
      "Accuracy Score : 0.9212\n",
      "____________________\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96   1030896\n",
      "           1       0.55      0.00      0.01     88277\n",
      "\n",
      "    accuracy                           0.92   1119173\n",
      "   macro avg       0.74      0.50      0.48   1119173\n",
      "weighted avg       0.89      0.92      0.88   1119173\n",
      "\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "# compute probabilities minmax\n",
    "\n",
    "#pred_prob_train = class_metrics_var_threshold(log_model, X_train_minmax, y_train, threshold=0.5)\n",
    "pred_prob_test = class_metrics_var_threshold(log_model, X_test_minmax, y_test, threshold=0.5)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "Confusion Matrix :\n",
      "[[785650 245246]\n",
      " [ 56546  31731]]\n",
      "____________________\n",
      "ROC AUC Score : 0.5608\n",
      "____________________\n",
      "FBeta Score, Beta = 2 : 0.2518\n",
      "____________________\n",
      "Accuracy Score : 0.7303\n",
      "____________________\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.76      0.84   1030896\n",
      "           1       0.11      0.36      0.17     88277\n",
      "\n",
      "    accuracy                           0.73   1119173\n",
      "   macro avg       0.52      0.56      0.51   1119173\n",
      "weighted avg       0.87      0.73      0.79   1119173\n",
      "\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "# compute probabilities :std threshold = 0.5\n",
    "\n",
    "#pred_prob_train_1 = class_metrics_var_threshold(log_model, X_train_std, y_train, threshold=0.5)\n",
    "pred_prob_test_1 = class_metrics_var_threshold(log_model, X_test_std, y_test, threshold=0.5)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "Confusion Matrix :\n",
      "[[1030666     230]\n",
      " [  87989     288]]\n",
      "____________________\n",
      "ROC AUC Score : 0.5015\n",
      "____________________\n",
      "FBeta Score, Beta = 2 : 0.0041\n",
      "____________________\n",
      "Accuracy Score : 0.9212\n",
      "____________________\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96   1030896\n",
      "           1       0.56      0.00      0.01     88277\n",
      "\n",
      "    accuracy                           0.92   1119173\n",
      "   macro avg       0.74      0.50      0.48   1119173\n",
      "weighted avg       0.89      0.92      0.88   1119173\n",
      "\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "# compute probabilities :std\n",
    "\n",
    "pred_prob_test_11 = class_metrics_var_threshold(log_model1, X_test_std, y_test, threshold=0.5)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "Confusion Matrix :\n",
      "[[847525 183371]\n",
      " [ 62760  25517]]\n",
      "____________________\n",
      "ROC AUC Score : 0.5556\n",
      "____________________\n",
      "FBeta Score, Beta = 2 : 0.227\n",
      "____________________\n",
      "Accuracy Score : 0.7801\n",
      "____________________\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.82      0.87   1030896\n",
      "           1       0.12      0.29      0.17     88277\n",
      "\n",
      "    accuracy                           0.78   1119173\n",
      "   macro avg       0.53      0.56      0.52   1119173\n",
      "weighted avg       0.87      0.78      0.82   1119173\n",
      "\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "# compute probabilities threshold = 0.6\n",
    "\n",
    "#pred_prob_train_2 = class_metrics_var_threshold(log_model, X_train_std, y_train, threshold=0.6)\n",
    "pred_prob_test_2 = class_metrics_var_threshold(log_model, X_test_std, y_test, threshold=0.6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "Confusion Matrix :\n",
      "[[724288 306608]\n",
      " [ 50592  37685]]\n",
      "____________________\n",
      "ROC AUC Score : 0.5647\n",
      "____________________\n",
      "FBeta Score, Beta = 2 : 0.2702\n",
      "____________________\n",
      "Accuracy Score : 0.6808\n",
      "____________________\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.70      0.80   1030896\n",
      "           1       0.11      0.43      0.17     88277\n",
      "\n",
      "    accuracy                           0.68   1119173\n",
      "   macro avg       0.52      0.56      0.49   1119173\n",
      "weighted avg       0.87      0.68      0.75   1119173\n",
      "\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "# compute probabilities threshold = 0.4\n",
    "\n",
    "#pred_prob_train_2 = class_metrics_var_threshold(log_model, X_train_std, y_train, threshold=0.4)\n",
    "pred_prob_test_3 = class_metrics_var_threshold(log_model, X_test_std, y_test, threshold=0.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "Confusion Matrix :\n",
      "[[622719 408177]\n",
      " [ 41256  47021]]\n",
      "____________________\n",
      "ROC AUC Score : 0.5684\n",
      "____________________\n",
      "FBeta Score, Beta = 2 : 0.2909\n",
      "____________________\n",
      "Accuracy Score : 0.5984\n",
      "____________________\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.60      0.73   1030896\n",
      "           1       0.10      0.53      0.17     88277\n",
      "\n",
      "    accuracy                           0.60   1119173\n",
      "   macro avg       0.52      0.57      0.45   1119173\n",
      "weighted avg       0.87      0.60      0.69   1119173\n",
      "\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "# compute probabilities threshold = 0.25\n",
    "\n",
    "#pred_prob_train_2 = class_metrics_var_threshold(log_model, X_train_std, y_train, threshold=0.25)\n",
    "pred_prob_test_4 = class_metrics_var_threshold(log_model, X_test_std, y_test, threshold=0.25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "Confusion Matrix :\n",
      "[[461312 569584]\n",
      " [ 27238  61039]]\n",
      "____________________\n",
      "ROC AUC Score : 0.5695\n",
      "____________________\n",
      "FBeta Score, Beta = 2 : 0.3102\n",
      "____________________\n",
      "Accuracy Score : 0.4667\n",
      "____________________\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.45      0.61   1030896\n",
      "           1       0.10      0.69      0.17     88277\n",
      "\n",
      "    accuracy                           0.47   1119173\n",
      "   macro avg       0.52      0.57      0.39   1119173\n",
      "weighted avg       0.88      0.47      0.57   1119173\n",
      "\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "# compute probabilities threshold = 0.1\n",
    "\n",
    "#pred_prob_train_2 = class_metrics_var_threshold(log_model, X_train_std, y_train, threshold=0.1)\n",
    "pred_prob_test_5 = class_metrics_var_threshold(log_model, X_test_std, y_test, threshold=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace_column names to original column names\n",
    "cols = X_test.columns.to_list()\n",
    "\n",
    "x_test_minmax = X_test_minmax.copy(deep=True)\n",
    "x_test_std = X_test_std.copy(deep=True)\n",
    "\n",
    "\n",
    "x_test_minmax.columns = cols\n",
    "x_test_std.columns = cols\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>member_years</th>\n",
       "      <th>index_change_month</th>\n",
       "      <th>quantity_counters</th>\n",
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       "<p>5 rows × 100 columns</p>\n",
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      ],
      "text/plain": [
       "   member_years  index_change_month  quantity_counters  client_catg_12   \n",
       "0      0.261905            0.784404           0.130137             0.0  \\\n",
       "1      0.119048            0.784407           0.152968             0.0   \n",
       "2      0.095238            0.784391           0.107306             0.0   \n",
       "3      0.666667            0.784717           0.139269             0.0   \n",
       "4      0.476190            0.784395           0.134703             0.0   \n",
       "\n",
       "   client_catg_51  region_103  region_104  region_105  region_106  region_107   \n",
       "0             0.0         0.0         0.0         0.0         0.0         1.0  \\\n",
       "1             0.0         1.0         0.0         0.0         0.0         0.0   \n",
       "2             1.0         0.0         1.0         0.0         0.0         0.0   \n",
       "3             0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4             0.0         1.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   ...  reading_remarque_7  reading_remarque_8  reading_remarque_9   \n",
       "0  ...                 0.0                 0.0                 0.0  \\\n",
       "1  ...                 0.0                 0.0                 0.0   \n",
       "2  ...                 0.0                 0.0                 0.0   \n",
       "3  ...                 0.0                 0.0                 0.0   \n",
       "4  ...                 0.0                 1.0                 0.0   \n",
       "\n",
       "   counter_type_GAZ  target  fraud_pred_lr  no_fraud_prob_lrmm   \n",
       "0               1.0       0              0            0.914634  \\\n",
       "1               1.0       0              0            0.864962   \n",
       "2               1.0       0              0            0.867971   \n",
       "3               0.0       0              0            0.945326   \n",
       "4               1.0       0              0            0.824911   \n",
       "\n",
       "   fraud_prob_lrmm  no_fraud_prob_lrsd  fraud_prob_lrsd  \n",
       "0         0.085366            0.763572         0.236428  \n",
       "1         0.135038            0.559889         0.440111  \n",
       "2         0.132029            0.862402         0.137598  \n",
       "3         0.054674            0.667667         0.332333  \n",
       "4         0.175089            0.418888         0.581112  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# store in dataframe\n",
    "\n",
    "df_prob_test_mm = pd.DataFrame(data=pred_prob_test, columns=['no_fraud_prob_lrmm','fraud_prob_lrmm'])\n",
    "df_prob_test_sd = pd.DataFrame(data=pred_prob_test_1, columns=['no_fraud_prob_lrsd','fraud_prob_lrsd'])\n",
    "df_prob_test_06 = pd.DataFrame(data=pred_prob_test_2, columns=['no_fraud_prob_lr06','fraud_prob_lr06'])\n",
    "df_prob_test_04 = pd.DataFrame(data=pred_prob_test_3, columns=['no_fraud_prob_lr04','fraud_prob_lr04'])\n",
    "df_prob_test_025 = pd.DataFrame(data=pred_prob_test_4, columns=['no_fraud_prob_lr025','fraud_prob_lr025'])\n",
    "df_prob_test_01 = pd.DataFrame(data=pred_prob_test_5, columns=['no_fraud_prob_lr01','fraud_prob_lr01'])\n",
    "\n",
    "df_y_test = pd.DataFrame(data=y_test, columns=['target'])\n",
    "df_y_test_pred = pd.DataFrame(data=y_pred_test, columns=['fraud_pred_lr'])\n",
    "\n",
    "# df_test_preds_lr = pd.concat([x_test_minmax, x_test_std, df_y_test, df_y_test_pred,\n",
    "#                     df_prob_t05_mm, df_prob_test_sd, df_prob_test_06, df_prob_test_04, \n",
    "#                                 df_prob_test_025,  df_prob_test_01], axis=1)\n",
    "\n",
    "df_test_preds_lr = pd.concat([x_test_minmax, x_test_std, df_y_test, \n",
    "                            df_y_test_pred, df_prob_test_mm, df_prob_test_sd], axis=1)\n",
    "df_test_preds_lr.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "\n",
    "\n",
    "df_test_preds_lr['pred_corr']= (df_test_preds_lr['target'] == df_test_preds_lr['fraud_pred_lr'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.175089</td>\n",
       "      <td>0.418888</td>\n",
       "      <td>0.581112</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   member_years  index_change_month  quantity_counters  client_catg_12   \n",
       "0      0.261905            0.784404           0.130137             0.0  \\\n",
       "1      0.119048            0.784407           0.152968             0.0   \n",
       "2      0.095238            0.784391           0.107306             0.0   \n",
       "3      0.666667            0.784717           0.139269             0.0   \n",
       "4      0.476190            0.784395           0.134703             0.0   \n",
       "\n",
       "   client_catg_51  region_103  region_104  region_105  region_106  region_107   \n",
       "0             0.0         0.0         0.0         0.0         0.0         1.0  \\\n",
       "1             0.0         1.0         0.0         0.0         0.0         0.0   \n",
       "2             1.0         0.0         1.0         0.0         0.0         0.0   \n",
       "3             0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4             0.0         1.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   ...  reading_remarque_8  reading_remarque_9  counter_type_GAZ  target   \n",
       "0  ...                 0.0                 0.0               1.0       0  \\\n",
       "1  ...                 0.0                 0.0               1.0       0   \n",
       "2  ...                 0.0                 0.0               1.0       0   \n",
       "3  ...                 0.0                 0.0               0.0       0   \n",
       "4  ...                 1.0                 0.0               1.0       0   \n",
       "\n",
       "   fraud_pred_lr  no_fraud_prob_lrmm  fraud_prob_lrmm  no_fraud_prob_lrsd   \n",
       "0              0            0.914634         0.085366            0.763572  \\\n",
       "1              0            0.864962         0.135038            0.559889   \n",
       "2              0            0.867971         0.132029            0.862402   \n",
       "3              0            0.945326         0.054674            0.667667   \n",
       "4              0            0.824911         0.175089            0.418888   \n",
       "\n",
       "   fraud_prob_lrsd  pred_corr  \n",
       "0         0.236428       True  \n",
       "1         0.440111       True  \n",
       "2         0.137598       True  \n",
       "3         0.332333       True  \n",
       "4         0.581112       True  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_test_preds_lr.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88221 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         member_years  index_change_month  quantity_counters  client_catg_12   \n",
       "50           0.166667            0.785148           0.114155             0.0  \\\n",
       "83           0.023810            0.784393           0.102740             0.0   \n",
       "92           0.095238            0.784438           0.068493             0.0   \n",
       "96           0.095238            0.784391           0.100457             0.0   \n",
       "113          0.095238            0.785426           0.349315             0.0   \n",
       "...               ...                 ...                ...             ...   \n",
       "1119039      0.500000            0.784391           0.184932             0.0   \n",
       "1119040      0.047619            0.784399           0.068493             0.0   \n",
       "1119072      0.571429            0.784517           0.086758             0.0   \n",
       "1119157      0.619048            0.784464           0.109589             0.0   \n",
       "1119169      0.119048            0.784750           0.148402             0.0   \n",
       "\n",
       "         client_catg_51  region_103  region_104  region_105  region_106   \n",
       "50                  0.0         0.0         0.0         0.0         0.0  \\\n",
       "83                  0.0         0.0         0.0         0.0         0.0   \n",
       "92                  0.0         0.0         0.0         0.0         0.0   \n",
       "96                  0.0         0.0         0.0         0.0         0.0   \n",
       "113                 0.0         0.0         0.0         0.0         0.0   \n",
       "...                 ...         ...         ...         ...         ...   \n",
       "1119039             0.0         0.0         0.0         0.0         0.0   \n",
       "1119040             0.0         0.0         0.0         0.0         0.0   \n",
       "1119072             0.0         0.0         0.0         0.0         0.0   \n",
       "1119157             0.0         0.0         0.0         0.0         0.0   \n",
       "1119169             0.0         1.0         0.0         0.0         0.0   \n",
       "\n",
       "         region_107  ...  reading_remarque_8  reading_remarque_9   \n",
       "50              0.0  ...                 0.0                 1.0  \\\n",
       "83              0.0  ...                 1.0                 0.0   \n",
       "92              0.0  ...                 0.0                 0.0   \n",
       "96              0.0  ...                 0.0                 0.0   \n",
       "113             1.0  ...                 0.0                 0.0   \n",
       "...             ...  ...                 ...                 ...   \n",
       "1119039         0.0  ...                 0.0                 0.0   \n",
       "1119040         0.0  ...                 1.0                 0.0   \n",
       "1119072         0.0  ...                 0.0                 0.0   \n",
       "1119157         0.0  ...                 0.0                 0.0   \n",
       "1119169         0.0  ...                 0.0                 1.0   \n",
       "\n",
       "         counter_type_GAZ  target  fraud_pred_lr  no_fraud_prob_lrmm   \n",
       "50                    0.0       1              0            0.963737  \\\n",
       "83                    0.0       1              0            0.924615   \n",
       "92                    0.0       1              0            0.949501   \n",
       "96                    1.0       1              0            0.914144   \n",
       "113                   0.0       1              0            0.878793   \n",
       "...                   ...     ...            ...                 ...   \n",
       "1119039               1.0       1              0            0.907564   \n",
       "1119040               0.0       1              0            0.948348   \n",
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       "1119157               0.0       1              0            0.950718   \n",
       "1119169               0.0       1              0            0.866579   \n",
       "\n",
       "         fraud_prob_lrmm  no_fraud_prob_lrsd  fraud_prob_lrsd  pred_corr  \n",
       "50              0.036263            0.973966         0.026034      False  \n",
       "83              0.075385            0.941909         0.058091      False  \n",
       "92              0.050499            0.985720         0.014280      False  \n",
       "96              0.085856            0.927356         0.072644      False  \n",
       "113             0.121207            0.006160         0.993840      False  \n",
       "...                  ...                 ...              ...        ...  \n",
       "1119039         0.092436            0.208170         0.791830      False  \n",
       "1119040         0.051652            0.986411         0.013589      False  \n",
       "1119072         0.055693            0.920985         0.079015      False  \n",
       "1119157         0.049282            0.838937         0.161063      False  \n",
       "1119169         0.133421            0.682668         0.317332      False  \n",
       "\n",
       "[88221 rows x 101 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_false_pred = df_test_preds_lr.query('target != fraud_pred_lr')\n",
    "\n",
    "df_false_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "### saving for use in other notebook\n",
    "\n",
    "df_test_preds_lr.to_csv('data/df_test_lrpreds.csv', index=False, chunksize=len(df_test_preds_lr)//8)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# cleaned data used in modelling notebook\n",
    "\n",
    "df_processed = pd.read_csv('data/df_processed_dmy.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "## Defining baseline model that predicts no one commits fraud\n",
    "def baseline_model(df):\n",
    "    y_pred = [0 for x in df.index]\n",
    "    return y_pred\n",
    "\n",
    "# Compute predictions with baseline model  1 for test set\n",
    "\n",
    "y_pred_baseline_1 = baseline_model(X_test)\n",
    "y_pred_baseline_11 = baseline_model(x_test_std)\n",
    "\n",
    "\n",
    "# Defining baseline model that predicts no one commits fraud for \n",
    "#  index change with respect to consuption per month <= 15% is fraud\n",
    "\n",
    "\n",
    "quantile_idx = df_processed[['index_change_month']].quantile(q=0.15)[0]\n",
    "quantile_idx\n",
    "\n",
    "def baseline_model_idx(df, quantile_idx):\n",
    "    y_pred = [1 if x <= quantile_idx else 0 for x in df['index_change_month']]\n",
    "    return y_pred\n",
    "\n",
    "# Compute predictions with baseline modelv2 for test set\n",
    "y_pred_baseline_2 = baseline_model_idx(X_test, quantile_idx)\n",
    "y_pred_baseline_21 = baseline_model_idx(x_test_std, quantile_idx)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.9211\n",
      "_____________________\n",
      "F1-score: 0.0\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.0\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.0\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.0\n",
      "_____________________\n",
      "Recall 0.0\n",
      "_____________________\n",
      "Specificity 1.0\n",
      "Baseline 1 Metrics:  \n",
      " None\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute probabilities , std threshold = 0.5\n",
    "# baseline 1\n",
    "\n",
    "print(f'Baseline 1 Metrics:  \\n {our_metrics(y_test, y_pred_baseline_1)}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.9211\n",
      "_____________________\n",
      "F1-score: 0.0\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.0\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.0\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.0\n",
      "_____________________\n",
      "Recall 0.0\n",
      "_____________________\n",
      "Specificity 1.0\n",
      "Baseline 1 Metrics:  \n",
      " None\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute probabilities , std threshold = 0.5\n",
    "# baseline 1 , X_test_std\n",
    "\n",
    "print(f'Baseline 1 Metrics:  \\n {our_metrics(y_test, y_pred_baseline_11)}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.7922\n",
      "_____________________\n",
      "F1-score: 0.0979\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.1114\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.1207\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.1309\n",
      "_____________________\n",
      "Recall 0.1429\n",
      "_____________________\n",
      "Specificity 0.8478\n",
      "Baseline 2 Metrics:  \n",
      " None\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute probabilities , std threshold = 0.5\n",
    "# baseline 2 , X_test\n",
    "\n",
    "print(f'Baseline 2 Metrics:  \\n {our_metrics(y_test, y_pred_baseline_2)}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.0791\n",
      "_____________________\n",
      "F1-score: 0.1462\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.2177\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.2997\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.4612\n",
      "_____________________\n",
      "Recall 0.9996\n",
      "_____________________\n",
      "Specificity 0.0003\n",
      "Baseline 2 Metrics:  \n",
      " None\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute probabilities , std threshold = 0.5\n",
    "# baseline 2 , X_test_std\n",
    "\n",
    "print(f'Baseline 2 Metrics:  \\n {our_metrics(y_test, y_pred_baseline_21)}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Metrics and Normalized Confusion Matrix\n",
      "_____________________\n",
      "_____________________\n",
      "Accuracy: 0.9212\n",
      "_____________________\n",
      "F1-score: 0.0065\n",
      "_____________________\n",
      "Fbeta_score with beta=1.5: 0.0047\n",
      "_____________________\n",
      "Fbeta_score with beta=2: 0.0041\n",
      "_____________________\n",
      "Fbeta_score with beta=3: 0.0036\n",
      "_____________________\n",
      "Recall 0.0033\n",
      "_____________________\n",
      "Specificity 0.9998\n",
      "Baseline 2 Metrics:  \n",
      " None\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute probabilities , std threshold = 0.5\n",
    "# baseline 2 , X_test_std\n",
    "\n",
    "print(f'Baseline 2 Metrics:  \\n {our_metrics(y_test, y_pred_test_sd)}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline 2\n",
    "\n",
    "Model Metrics and Normalized Confusion Matrix\n",
    "_____________________\n",
    "_____________________\n",
    "Accuracy: 0.7922\n",
    "_____________________\n",
    "F1-score: 0.0979\n",
    "_____________________\n",
    "Fbeta_score with beta=1.5: 0.1114\n",
    "_____________________\n",
    "Fbeta_score with beta=2: 0.1207\n",
    "_____________________\n",
    "Fbeta_score with beta=3: 0.1309\n",
    "_____________________\n",
    "Recall 0.1429\n",
    "_____________________\n",
    "Specificity 0.8478\n",
    "\n",
    "\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Logostic Regression model\n",
    "\n",
    "____________________\n",
    "Confusion Matrix :\n",
    "<br/>\n",
    "[[785650 || 245246]\n",
    "<br/>\n",
    " [ 56546 || 31731]]\n",
    "____________________\n",
    "ROC AUC Score : 0.5608\n",
    "____________________\n",
    "FBeta Score, Beta = 2 : 0.2518\n",
    "____________________\n",
    "Accuracy Score : 0.7303\n",
    "____________________\n",
    "Report : \n",
    "              precision    recall  f1-score   support\n",
    "\n",
    "           0       0.93      0.76      0.84   1030896\n",
    "           1       0.11      0.36      0.17     88277\n",
    "\n",
    "    accuracy                           0.73   1119173\n",
    "    \n",
    "   macro avg       0.52      0.56      0.51   1119173\n",
    "weighted avg       0.87      0.73      0.79   1119173\n",
    "\n",
    "____________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "430.46198199422844"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# True positive rate: TP/(TP+FN) = 785650 / 1030896 = 0.76\n",
    "tpr = 0.76\n",
    "# False positive rate: FP/(TN+FP) = 31731 / 88277 = 0.36\n",
    "fpr = 0.39\n",
    "# target value count\n",
    "# 0    4123581\n",
    "# 1     353109\n",
    "total_loss = 200000000\n",
    "num_fraud = 353109\n",
    "avg_loss = total_loss / num_fraud\n",
    "cost_falsely_acc = 0\n",
    "gain_per_client = avg_loss * tpr - cost_falsely_acc * fpr\n",
    "gain_per_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126000000.0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# expected gain during next period by detecting fraud in 63% of the fraud cases\n",
    "total_gain = 0.63 * total_loss\n",
    "total_gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">member_years</th>\n",
       "      <th>count</th>\n",
       "      <td>1.030896e+06</td>\n",
       "      <td>88277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.224795e-01</td>\n",
       "      <td>0.366768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.419332e-01</td>\n",
       "      <td>0.244836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.190476e-01</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.619048e-01</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.030896e+06</td>\n",
       "      <td>88277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.541082e-02</td>\n",
       "      <td>0.167278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.979754e-01</td>\n",
       "      <td>1.009949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.345640e+00</td>\n",
       "      <td>-1.345640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.545681e-01</td>\n",
       "      <td>-0.756354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.652819e-01</td>\n",
       "      <td>0.029361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.168618e-01</td>\n",
       "      <td>1.011505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.779364e+00</td>\n",
       "      <td>2.779364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">index_change_month</th>\n",
       "      <th>count</th>\n",
       "      <td>1.030896e+06</td>\n",
       "      <td>88277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.845107e-01</td>\n",
       "      <td>0.784568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.749239e-03</td>\n",
       "      <td>0.001046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.516875e-03</td>\n",
       "      <td>0.706500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.844062e-01</td>\n",
       "      <td>0.784408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.844508e-01</td>\n",
       "      <td>0.784452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.845134e-01</td>\n",
       "      <td>0.784535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.426535e-01</td>\n",
       "      <td>0.876020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.030896e+06</td>\n",
       "      <td>88277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-2.305175e-03</td>\n",
       "      <td>0.030305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.872177e-01</td>\n",
       "      <td>0.590094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.419004e+02</td>\n",
       "      <td>-44.029163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.126636e-02</td>\n",
       "      <td>-0.060269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-3.610714e-02</td>\n",
       "      <td>-0.035664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-7.512300e-04</td>\n",
       "      <td>0.011219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.281175e+01</td>\n",
       "      <td>51.642814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">quantity_counters</th>\n",
       "      <th>count</th>\n",
       "      <td>1.030896e+06</td>\n",
       "      <td>88277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.181985e-01</td>\n",
       "      <td>0.142224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.681157e-02</td>\n",
       "      <td>0.083871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.534247e-02</td>\n",
       "      <td>0.086758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.164384e-01</td>\n",
       "      <td>0.134703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.575342e-01</td>\n",
       "      <td>0.168950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.618721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.030896e+06</td>\n",
       "      <td>88277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-2.953561e-02</td>\n",
       "      <td>0.320142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.723921e-01</td>\n",
       "      <td>1.220672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.749826e+00</td>\n",
       "      <td>-1.749826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.532733e-01</td>\n",
       "      <td>-0.487129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-5.515350e-02</td>\n",
       "      <td>0.210678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.429663e-01</td>\n",
       "      <td>0.709111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.280442e+01</td>\n",
       "      <td>7.255199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                               0             1\n",
       "member_years       count  1.030896e+06  88277.000000\n",
       "                   mean   3.224795e-01      0.366768\n",
       "                   std    2.419332e-01      0.244836\n",
       "                   min    0.000000e+00      0.000000\n",
       "                   25%    1.190476e-01      0.142857\n",
       "                   50%    2.619048e-01      0.333333\n",
       "                   75%    5.000000e-01      0.571429\n",
       "                   max    1.000000e+00      1.000000\n",
       "                   count  1.030896e+06  88277.000000\n",
       "                   mean  -1.541082e-02      0.167278\n",
       "                   std    9.979754e-01      1.009949\n",
       "                   min   -1.345640e+00     -1.345640\n",
       "                   25%   -8.545681e-01     -0.756354\n",
       "                   50%   -2.652819e-01      0.029361\n",
       "                   75%    7.168618e-01      1.011505\n",
       "                   max    2.779364e+00      2.779364\n",
       "index_change_month count  1.030896e+06  88277.000000\n",
       "                   mean   7.845107e-01      0.784568\n",
       "                   std    1.749239e-03      0.001046\n",
       "                   min    1.516875e-03      0.706500\n",
       "                   25%    7.844062e-01      0.784408\n",
       "                   50%    7.844508e-01      0.784452\n",
       "                   75%    7.845134e-01      0.784535\n",
       "                   max    8.426535e-01      0.876020\n",
       "                   count  1.030896e+06  88277.000000\n",
       "                   mean  -2.305175e-03      0.030305\n",
       "                   std    9.872177e-01      0.590094\n",
       "                   min   -4.419004e+02    -44.029163\n",
       "                   25%   -6.126636e-02     -0.060269\n",
       "                   50%   -3.610714e-02     -0.035664\n",
       "                   75%   -7.512300e-04      0.011219\n",
       "                   max    3.281175e+01     51.642814\n",
       "quantity_counters  count  1.030896e+06  88277.000000\n",
       "                   mean   1.181985e-01      0.142224\n",
       "                   std    6.681157e-02      0.083871\n",
       "                   min    0.000000e+00      0.000000\n",
       "                   25%    7.534247e-02      0.086758\n",
       "                   50%    1.164384e-01      0.134703\n",
       "                   75%    1.575342e-01      0.168950\n",
       "                   max    1.000000e+00      0.618721\n",
       "                   count  1.030896e+06  88277.000000\n",
       "                   mean  -2.953561e-02      0.320142\n",
       "                   std    9.723921e-01      1.220672\n",
       "                   min   -1.749826e+00     -1.749826\n",
       "                   25%   -6.532733e-01     -0.487129\n",
       "                   50%   -5.515350e-02      0.210678\n",
       "                   75%    5.429663e-01      0.709111\n",
       "                   max    1.280442e+01      7.255199"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stat summary\n",
    "\n",
    "\n",
    "df_test_preds_lr.groupby('target')[['member_years','index_change_month', 'quantity_counters']].describe().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">member_years</th>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>87987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.562271</td>\n",
       "      <td>0.366132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.181404</td>\n",
       "      <td>0.244604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.547619</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.904762</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>87987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.973730</td>\n",
       "      <td>0.164657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.748293</td>\n",
       "      <td>1.008992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.363496</td>\n",
       "      <td>-1.345640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.324004</td>\n",
       "      <td>-0.756354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.913291</td>\n",
       "      <td>0.029361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.600791</td>\n",
       "      <td>1.011505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.386506</td>\n",
       "      <td>2.779364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">index_change_month</th>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>87987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.781680</td>\n",
       "      <td>0.784566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.052050</td>\n",
       "      <td>0.001008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.707392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.784393</td>\n",
       "      <td>0.784408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.785525</td>\n",
       "      <td>0.784451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.786653</td>\n",
       "      <td>0.784534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.795855</td>\n",
       "      <td>0.876020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>87987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.600122</td>\n",
       "      <td>0.028656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.375169</td>\n",
       "      <td>0.568770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-441.900383</td>\n",
       "      <td>-43.525979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.068803</td>\n",
       "      <td>-0.060269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.570263</td>\n",
       "      <td>-0.035775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.206780</td>\n",
       "      <td>0.010665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.400109</td>\n",
       "      <td>51.642814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">quantity_counters</th>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>87987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.680609</td>\n",
       "      <td>0.140935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.133685</td>\n",
       "      <td>0.080842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.445205</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.586758</td>\n",
       "      <td>0.086758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.134703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.703196</td>\n",
       "      <td>0.168950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.618721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>87987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.155929</td>\n",
       "      <td>0.301382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.945685</td>\n",
       "      <td>1.176601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.729805</td>\n",
       "      <td>-1.749826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.789995</td>\n",
       "      <td>-0.487129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.953005</td>\n",
       "      <td>0.210678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.484667</td>\n",
       "      <td>0.709111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.804421</td>\n",
       "      <td>7.255199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                             0             1\n",
       "member_years       count  234.000000  87987.000000\n",
       "                   mean     0.562271      0.366132\n",
       "                   std      0.181404      0.244604\n",
       "                   min      0.238095      0.000000\n",
       "                   25%      0.404762      0.142857\n",
       "                   50%      0.547619      0.333333\n",
       "                   75%      0.714286      0.571429\n",
       "                   max      0.904762      1.000000\n",
       "                   count  234.000000  87987.000000\n",
       "                   mean     0.973730      0.164657\n",
       "                   std      0.748293      1.008992\n",
       "                   min     -0.363496     -1.345640\n",
       "                   25%      0.324004     -0.756354\n",
       "                   50%      0.913291      0.029361\n",
       "                   75%      1.600791      1.011505\n",
       "                   max      2.386506      2.779364\n",
       "index_change_month count  234.000000  87987.000000\n",
       "                   mean     0.781680      0.784566\n",
       "                   std      0.052050      0.001008\n",
       "                   min      0.001517      0.707392\n",
       "                   25%      0.784393      0.784408\n",
       "                   50%      0.785525      0.784451\n",
       "                   75%      0.786653      0.784534\n",
       "                   max      0.795855      0.876020\n",
       "                   count  234.000000  87987.000000\n",
       "                   mean    -1.600122      0.028656\n",
       "                   std     29.375169      0.568770\n",
       "                   min   -441.900383    -43.525979\n",
       "                   25%     -0.068803     -0.060269\n",
       "                   50%      0.570263     -0.035775\n",
       "                   75%      1.206780      0.010665\n",
       "                   max      6.400109     51.642814\n",
       "quantity_counters  count  234.000000  87987.000000\n",
       "                   mean     0.680609      0.140935\n",
       "                   std      0.133685      0.080842\n",
       "                   min      0.445205      0.000000\n",
       "                   25%      0.586758      0.086758\n",
       "                   50%      0.666667      0.134703\n",
       "                   75%      0.703196      0.168950\n",
       "                   max      1.000000      0.618721\n",
       "                   count  234.000000  87987.000000\n",
       "                   mean     8.155929      0.301382\n",
       "                   std      1.945685      1.176601\n",
       "                   min      4.729805     -1.749826\n",
       "                   25%      6.789995     -0.487129\n",
       "                   50%      7.953005      0.210678\n",
       "                   75%      8.484667      0.709111\n",
       "                   max     12.804421      7.255199"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stat summary for false predictions\n",
    "\n",
    "df_false_pred.groupby('target')[['member_years','index_change_month', 'quantity_counters']].describe().T\n"
   ]
  }
 ],
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