Deriving_Financial_Insights (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your challenge can be found toward the end of this notebook. The code below will be needed in order to begin the challenge. Read through and execute all necessary portions of this code to complete the tasks for this challenge.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: graphviz in c:\\users\\viksb\\anaconda3\\lib\\site-packages (0.16)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #numerical computation\n",
    "import pandas as pd #data wrangling\n",
    "import matplotlib.pyplot as plt #plotting package\n",
    "#Next line helps with rendering plots\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl #add'l plotting functionality\n",
    "mpl.rcParams['figure.dpi'] = 400 #high res figures\n",
    "import graphviz #to visualize decision trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cleaning the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orig = pd.read_excel('default_of_credit_card_clients.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zero_mask = df_orig == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_zero_mask = df_zero_mask.iloc[:,1:].all(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "315"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(feature_zero_mask)\n",
    "# 315"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove all the rows with all zero features and response, confirm this that gets rid of the duplicate IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df_orig.loc[~feature_zero_mask,:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29685, 25)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.shape\n",
    "# (29685, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29685"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['ID'].nunique()\n",
    "# 29685"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up the `EDUCATION` and `MARRIAGE` features as in Chapter 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    13884\n",
       "1    10474\n",
       "3     4867\n",
       "5      275\n",
       "4      122\n",
       "6       49\n",
       "0       14\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['EDUCATION'].value_counts()\n",
    "# 2    13884\n",
    "# 1    10474\n",
    "# 3     4867\n",
    "# 5      275\n",
    "# 4      122\n",
    "# 6       49\n",
    "# 0       14\n",
    "# Name: EDUCATION, dtype: int64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Education (1 = graduate school; 2 = university; 3 = high school; 4 = others)\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign unknown categories to other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['EDUCATION'].replace(to_replace=[0, 5, 6], value=4, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    13884\n",
       "1    10474\n",
       "3     4867\n",
       "4      460\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['EDUCATION'].value_counts()\n",
    "# 2    13884\n",
    "# 1    10474\n",
    "# 3     4867\n",
    "# 4      460\n",
    "# Name: EDUCATION, dtype: int64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine and clean marriage feature as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15810\n",
       "1    13503\n",
       "3      318\n",
       "0       54\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['MARRIAGE'].value_counts()\n",
    "# 2    15810\n",
    "# 1    13503\n",
    "# 3      318\n",
    "# 0       54\n",
    "# Name: MARRIAGE, dtype: int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Should only be (1 = married; 2 = single; 3 = others).\n",
    "df_clean['MARRIAGE'].replace(to_replace=0, value=3, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15810\n",
       "1    13503\n",
       "3      372\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['MARRIAGE'].value_counts()\n",
    "# 2    15810\n",
    "# 1    13503\n",
    "# 3      372\n",
    "# Name: MARRIAGE, dtype: int64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now instead of removing rows with `PAY_1` = 'Not available', as done in Chapter 1, here select these out for addition to training and testing splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                13087\n",
       "-1                5047\n",
       "1                 3261\n",
       "Not available     3021\n",
       "-2                2476\n",
       "2                 2378\n",
       "3                  292\n",
       "4                   63\n",
       "5                   23\n",
       "8                   17\n",
       "6                   11\n",
       "7                    9\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['PAY_1'].value_counts()\n",
    "# 0                13087\n",
    "# -1                5047\n",
    "# 1                 3261\n",
    "# Not available     3021\n",
    "# -2                2476\n",
    "# 2                 2378\n",
    "# 3                  292\n",
    "# 4                   63\n",
    "# 5                   23\n",
    "# 8                   17\n",
    "# 6                   11\n",
    "# 7                    9\n",
    "# Name: PAY_1, dtype: int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_pay_1_mask = df_clean['PAY_1'] == 'Not available'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3021"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(missing_pay_1_mask)\n",
    "# 3021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_missing_pay_1 = df_clean.loc[missing_pay_1_mask,:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3021, 25)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_pay_1.shape\n",
    "# (3021, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    Not available\n",
       "28    Not available\n",
       "29    Not available\n",
       "Name: PAY_1, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_pay_1['PAY_1'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Not available    3021\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_pay_1['PAY_1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default payment next month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_pay_1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default payment next month', 'EDUCATION_CAT', 'graduate school',\n",
       "       'high school', 'others', 'university'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_response = df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "items_to_remove = ['ID', 'SEX', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6',\n",
    "                   'EDUCATION_CAT', 'graduate school', 'high school', 'none',\n",
    "                   'others', 'university']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LIMIT_BAL',\n",
       " 'EDUCATION',\n",
       " 'MARRIAGE',\n",
       " 'AGE',\n",
       " 'PAY_1',\n",
       " 'BILL_AMT1',\n",
       " 'BILL_AMT2',\n",
       " 'BILL_AMT3',\n",
       " 'BILL_AMT4',\n",
       " 'BILL_AMT5',\n",
       " 'BILL_AMT6',\n",
       " 'PAY_AMT1',\n",
       " 'PAY_AMT2',\n",
       " 'PAY_AMT3',\n",
       " 'PAY_AMT4',\n",
       " 'PAY_AMT5',\n",
       " 'PAY_AMT6',\n",
       " 'default payment next month']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_response = [item for item in features_response if item not in items_to_remove]\n",
    "features_response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mode and Random Imputation of `PAY_1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = \\\n",
    "train_test_split(df[features_response[:-1]].values, df['default payment next month'].values,\n",
    "test_size=0.2, random_state=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21331, 17)\n",
      "(5333, 17)\n",
      "(21331,)\n",
      "(5333,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "# (21331, 17)\n",
    "# (5333, 17)\n",
    "# (21331,)\n",
    "# (5333,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3021, 25)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_pay_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PAY_1'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_response[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(X_train[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(seed=1)\n",
    "fill_values = [0, np.random.choice(X_train[:,4], size=(3021,), replace=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "fill_strategy = ['mode', 'random']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  0, ...,  2,  0, -2], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_values[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2, figsize=(8,3))\n",
    "bin_edges = np.arange(-2,9)\n",
    "axs[0].hist(X_train[:,4], bins=bin_edges, align='left')\n",
    "axs[0].set_xticks(bin_edges)\n",
    "axs[0].set_title('Non-missing values of PAY_1')\n",
    "axs[1].hist(fill_values[-1], bins=bin_edges, align='left')\n",
    "axs[1].set_xticks(bin_edges)\n",
    "axs[1].set_title('Random selection for imputation')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do cross-validation on the training set, now we need to shuffle since all the samples with missing `PAY_1` were concatenated on to the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_folds = KFold(n_splits=4, shuffle=True, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't need to do a grid search, so we can use `cross_validate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the estimator, set the optimal hyperparameters determined in previous chapter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier\\\n",
    "(n_estimators=200, criterion='gini', max_depth=9,\n",
    "min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,\n",
    "max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None,\n",
    "random_state=4, verbose=1, warm_start=False, class_weight=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    8.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    8.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode imputation: mean testing score 0.772866246168149, std 0.0031479941297533737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:   12.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:   12.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random imputation: mean testing score 0.7692540439833129, std 0.003660875187678248\n"
     ]
    }
   ],
   "source": [
    "for counter in range(len(fill_values)):\n",
    "    #Copy the data frame with missing PAY_1 and assign imputed values\n",
    "    df_fill_pay_1_filled = df_missing_pay_1.copy()\n",
    "    df_fill_pay_1_filled['PAY_1'] = fill_values[counter]\n",
    "    \n",
    "    #Split imputed data in to training and testing, using the same\n",
    "    #80/20 split we have used for the data with non-missing PAY_1\n",
    "    X_fill_pay_1_train, X_fill_pay_1_test, y_fill_pay_1_train, y_fill_pay_1_test = \\\n",
    "    train_test_split(\n",
    "        df_fill_pay_1_filled[features_response[:-1]].values,\n",
    "        df_fill_pay_1_filled['default payment next month'].values,\n",
    "    test_size=0.2, random_state=24)\n",
    "    \n",
    "    #Concatenate the imputed data with the array of non-missing data\n",
    "    X_train_all = np.concatenate((X_train, X_fill_pay_1_train), axis=0)\n",
    "    y_train_all = np.concatenate((y_train, y_fill_pay_1_train), axis=0)\n",
    "    \n",
    "    #Use the KFolds splitter and the random forest model to get\n",
    "    #4-fold cross-validation scores for both imputation methods\n",
    "    imputation_compare_cv = cross_validate(rf, X_train_all, y_train_all, scoring='roc_auc',\n",
    "                                       cv=k_folds, n_jobs=-1, verbose=1,\n",
    "                                       return_train_score=True, return_estimator=True,\n",
    "                                       error_score='raise-deprecating')\n",
    "    \n",
    "    test_score = imputation_compare_cv['test_score']\n",
    "    print(fill_strategy[counter] + ' imputation: ' +\n",
    "          'mean testing score ' + str(np.mean(test_score)) +\n",
    "          ', std ' + str(np.std(test_score)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A Predictive Model for `PAY_1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pay_1_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_for_imputation = pay_1_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "items_to_remove_2 = ['ID', 'SEX', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6',\n",
    "                   'EDUCATION_CAT', 'graduate school', 'high school', 'none',\n",
    "                   'others', 'university', 'default payment next month', 'PAY_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LIMIT_BAL',\n",
       " 'EDUCATION',\n",
       " 'MARRIAGE',\n",
       " 'AGE',\n",
       " 'BILL_AMT1',\n",
       " 'BILL_AMT2',\n",
       " 'BILL_AMT3',\n",
       " 'BILL_AMT4',\n",
       " 'BILL_AMT5',\n",
       " 'BILL_AMT6',\n",
       " 'PAY_AMT1',\n",
       " 'PAY_AMT2',\n",
       " 'PAY_AMT3',\n",
       " 'PAY_AMT4',\n",
       " 'PAY_AMT5',\n",
       " 'PAY_AMT6']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_for_imputation = [item for item in features_for_imputation if item not in items_to_remove_2]\n",
    "features_for_imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building a Multiclass Classification Model for Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_impute_train, X_impute_test, y_impute_train, y_impute_test = \\\n",
    "train_test_split(\n",
    "    pay_1_df[features_for_imputation].values,\n",
    "    pay_1_df['PAY_1'].values,\n",
    "test_size=0.2, random_state=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_impute_params = {'max_depth':[3, 6, 9, 12],\n",
    "             'n_estimators':[10, 50, 100, 200]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to use accuracy here as ROC AUC is not supported for multiclass. Need to use multiclass and not regression because need to limit to integer values of `PAY_1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_rf_impute = GridSearchCV(rf, param_grid=rf_impute_params, scoring='accuracy',\n",
    "                            n_jobs=-1, iid=False, refit=True,\n",
    "                            cv=4, verbose=2, error_score=np.nan, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 16 candidates, totalling 64 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   12.0s\n",
      "[Parallel(n_jobs=-1)]: Done  64 out of  64 | elapsed:   46.9s finished\n",
      "C:\\Users\\viksb\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 200 out of 200 | elapsed:   12.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4,\n",
       "             estimator=RandomForestClassifier(max_depth=9, n_estimators=200,\n",
       "                                              random_state=4, verbose=1),\n",
       "             iid=False, n_jobs=-1,\n",
       "             param_grid={'max_depth': [3, 6, 9, 12],\n",
       "                         'n_estimators': [10, 50, 100, 200]},\n",
       "             return_train_score=True, scoring='accuracy', verbose=2)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf_impute.fit(X_impute_train, y_impute_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.199466</td>\n",
       "      <td>0.003074</td>\n",
       "      <td>0.013215</td>\n",
       "      <td>0.001088</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 10}</td>\n",
       "      <td>0.664729</td>\n",
       "      <td>0.669417</td>\n",
       "      <td>0.671479</td>\n",
       "      <td>0.664291</td>\n",
       "      <td>0.667479</td>\n",
       "      <td>0.003061</td>\n",
       "      <td>15</td>\n",
       "      <td>0.669396</td>\n",
       "      <td>0.670146</td>\n",
       "      <td>0.665146</td>\n",
       "      <td>0.670042</td>\n",
       "      <td>0.668682</td>\n",
       "      <td>0.002062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.899345</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>0.053358</td>\n",
       "      <td>0.004318</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 50}</td>\n",
       "      <td>0.657979</td>\n",
       "      <td>0.660229</td>\n",
       "      <td>0.660604</td>\n",
       "      <td>0.651350</td>\n",
       "      <td>0.657540</td>\n",
       "      <td>0.003712</td>\n",
       "      <td>16</td>\n",
       "      <td>0.664521</td>\n",
       "      <td>0.657145</td>\n",
       "      <td>0.654082</td>\n",
       "      <td>0.660854</td>\n",
       "      <td>0.659150</td>\n",
       "      <td>0.003920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.907153</td>\n",
       "      <td>0.015183</td>\n",
       "      <td>0.105718</td>\n",
       "      <td>0.008135</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>0.672605</td>\n",
       "      <td>0.673542</td>\n",
       "      <td>0.675792</td>\n",
       "      <td>0.667854</td>\n",
       "      <td>0.672448</td>\n",
       "      <td>0.002894</td>\n",
       "      <td>13</td>\n",
       "      <td>0.677835</td>\n",
       "      <td>0.675147</td>\n",
       "      <td>0.670521</td>\n",
       "      <td>0.676167</td>\n",
       "      <td>0.674918</td>\n",
       "      <td>0.002713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.982681</td>\n",
       "      <td>0.028094</td>\n",
       "      <td>0.248088</td>\n",
       "      <td>0.013645</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 200}</td>\n",
       "      <td>0.664917</td>\n",
       "      <td>0.666042</td>\n",
       "      <td>0.675792</td>\n",
       "      <td>0.666354</td>\n",
       "      <td>0.668276</td>\n",
       "      <td>0.004372</td>\n",
       "      <td>14</td>\n",
       "      <td>0.672334</td>\n",
       "      <td>0.668896</td>\n",
       "      <td>0.669021</td>\n",
       "      <td>0.672605</td>\n",
       "      <td>0.670714</td>\n",
       "      <td>0.001758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.445310</td>\n",
       "      <td>0.015200</td>\n",
       "      <td>0.026429</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 10}</td>\n",
       "      <td>0.710294</td>\n",
       "      <td>0.706919</td>\n",
       "      <td>0.713482</td>\n",
       "      <td>0.700675</td>\n",
       "      <td>0.707843</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>12</td>\n",
       "      <td>0.717027</td>\n",
       "      <td>0.717027</td>\n",
       "      <td>0.718465</td>\n",
       "      <td>0.711607</td>\n",
       "      <td>0.716032</td>\n",
       "      <td>0.002621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.588832</td>\n",
       "      <td>0.024697</td>\n",
       "      <td>0.091510</td>\n",
       "      <td>0.006592</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 50}</td>\n",
       "      <td>0.708419</td>\n",
       "      <td>0.709732</td>\n",
       "      <td>0.713107</td>\n",
       "      <td>0.703863</td>\n",
       "      <td>0.708780</td>\n",
       "      <td>0.003314</td>\n",
       "      <td>11</td>\n",
       "      <td>0.716652</td>\n",
       "      <td>0.716090</td>\n",
       "      <td>0.719777</td>\n",
       "      <td>0.715982</td>\n",
       "      <td>0.717125</td>\n",
       "      <td>0.001552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.092636</td>\n",
       "      <td>0.037946</td>\n",
       "      <td>0.111204</td>\n",
       "      <td>0.008268</td>\n",
       "      <td>6</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 100}</td>\n",
       "      <td>0.709544</td>\n",
       "      <td>0.709544</td>\n",
       "      <td>0.714420</td>\n",
       "      <td>0.705926</td>\n",
       "      <td>0.709859</td>\n",
       "      <td>0.003019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.718777</td>\n",
       "      <td>0.716777</td>\n",
       "      <td>0.719152</td>\n",
       "      <td>0.718420</td>\n",
       "      <td>0.718282</td>\n",
       "      <td>0.000906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.061856</td>\n",
       "      <td>0.046626</td>\n",
       "      <td>0.195727</td>\n",
       "      <td>0.010397</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 200}</td>\n",
       "      <td>0.710669</td>\n",
       "      <td>0.709357</td>\n",
       "      <td>0.715170</td>\n",
       "      <td>0.705551</td>\n",
       "      <td>0.710187</td>\n",
       "      <td>0.003436</td>\n",
       "      <td>9</td>\n",
       "      <td>0.719152</td>\n",
       "      <td>0.717652</td>\n",
       "      <td>0.719715</td>\n",
       "      <td>0.718107</td>\n",
       "      <td>0.718657</td>\n",
       "      <td>0.000818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.453038</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>0.016208</td>\n",
       "      <td>0.001088</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 10}</td>\n",
       "      <td>0.719670</td>\n",
       "      <td>0.725858</td>\n",
       "      <td>0.721920</td>\n",
       "      <td>0.720743</td>\n",
       "      <td>0.722048</td>\n",
       "      <td>0.002339</td>\n",
       "      <td>7</td>\n",
       "      <td>0.767721</td>\n",
       "      <td>0.773722</td>\n",
       "      <td>0.767221</td>\n",
       "      <td>0.771611</td>\n",
       "      <td>0.770069</td>\n",
       "      <td>0.002709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.836496</td>\n",
       "      <td>0.077637</td>\n",
       "      <td>0.143118</td>\n",
       "      <td>0.007209</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 50}</td>\n",
       "      <td>0.726608</td>\n",
       "      <td>0.728296</td>\n",
       "      <td>0.725858</td>\n",
       "      <td>0.731808</td>\n",
       "      <td>0.728142</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>6</td>\n",
       "      <td>0.770284</td>\n",
       "      <td>0.774347</td>\n",
       "      <td>0.771471</td>\n",
       "      <td>0.770611</td>\n",
       "      <td>0.771678</td>\n",
       "      <td>0.001601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.292430</td>\n",
       "      <td>0.041790</td>\n",
       "      <td>0.145112</td>\n",
       "      <td>0.024251</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 100}</td>\n",
       "      <td>0.728483</td>\n",
       "      <td>0.726608</td>\n",
       "      <td>0.725858</td>\n",
       "      <td>0.732371</td>\n",
       "      <td>0.728330</td>\n",
       "      <td>0.002521</td>\n",
       "      <td>5</td>\n",
       "      <td>0.770284</td>\n",
       "      <td>0.772534</td>\n",
       "      <td>0.775409</td>\n",
       "      <td>0.770298</td>\n",
       "      <td>0.772131</td>\n",
       "      <td>0.002103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13.247591</td>\n",
       "      <td>0.108652</td>\n",
       "      <td>0.443316</td>\n",
       "      <td>0.012062</td>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 200}</td>\n",
       "      <td>0.729608</td>\n",
       "      <td>0.726983</td>\n",
       "      <td>0.726045</td>\n",
       "      <td>0.731808</td>\n",
       "      <td>0.728611</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>4</td>\n",
       "      <td>0.771409</td>\n",
       "      <td>0.773472</td>\n",
       "      <td>0.775534</td>\n",
       "      <td>0.770361</td>\n",
       "      <td>0.772694</td>\n",
       "      <td>0.001986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.588178</td>\n",
       "      <td>0.009014</td>\n",
       "      <td>0.020445</td>\n",
       "      <td>0.005487</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 10}</td>\n",
       "      <td>0.721170</td>\n",
       "      <td>0.716670</td>\n",
       "      <td>0.719295</td>\n",
       "      <td>0.719617</td>\n",
       "      <td>0.719188</td>\n",
       "      <td>0.001618</td>\n",
       "      <td>8</td>\n",
       "      <td>0.835792</td>\n",
       "      <td>0.850919</td>\n",
       "      <td>0.845668</td>\n",
       "      <td>0.847053</td>\n",
       "      <td>0.844858</td>\n",
       "      <td>0.005577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.639598</td>\n",
       "      <td>0.015241</td>\n",
       "      <td>0.074053</td>\n",
       "      <td>0.003481</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 50}</td>\n",
       "      <td>0.734858</td>\n",
       "      <td>0.728858</td>\n",
       "      <td>0.729046</td>\n",
       "      <td>0.732558</td>\n",
       "      <td>0.731330</td>\n",
       "      <td>0.002514</td>\n",
       "      <td>3</td>\n",
       "      <td>0.854732</td>\n",
       "      <td>0.860983</td>\n",
       "      <td>0.862233</td>\n",
       "      <td>0.858741</td>\n",
       "      <td>0.859172</td>\n",
       "      <td>0.002853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.192104</td>\n",
       "      <td>0.281345</td>\n",
       "      <td>0.311169</td>\n",
       "      <td>0.027521</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 100}</td>\n",
       "      <td>0.731858</td>\n",
       "      <td>0.731296</td>\n",
       "      <td>0.731483</td>\n",
       "      <td>0.734246</td>\n",
       "      <td>0.732221</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>2</td>\n",
       "      <td>0.858545</td>\n",
       "      <td>0.860545</td>\n",
       "      <td>0.864671</td>\n",
       "      <td>0.860116</td>\n",
       "      <td>0.860969</td>\n",
       "      <td>0.002263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.492856</td>\n",
       "      <td>0.052054</td>\n",
       "      <td>0.189246</td>\n",
       "      <td>0.008384</td>\n",
       "      <td>12</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 200}</td>\n",
       "      <td>0.734858</td>\n",
       "      <td>0.731858</td>\n",
       "      <td>0.733546</td>\n",
       "      <td>0.737434</td>\n",
       "      <td>0.734424</td>\n",
       "      <td>0.002037</td>\n",
       "      <td>1</td>\n",
       "      <td>0.858920</td>\n",
       "      <td>0.861608</td>\n",
       "      <td>0.863358</td>\n",
       "      <td>0.861554</td>\n",
       "      <td>0.861360</td>\n",
       "      <td>0.001585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.199466      0.003074         0.013215        0.001088   \n",
       "1        0.899345      0.015099         0.053358        0.004318   \n",
       "2        1.907153      0.015183         0.105718        0.008135   \n",
       "3        4.982681      0.028094         0.248088        0.013645   \n",
       "4        0.445310      0.015200         0.026429        0.002056   \n",
       "5        2.588832      0.024697         0.091510        0.006592   \n",
       "6        5.092636      0.037946         0.111204        0.008268   \n",
       "7       10.061856      0.046626         0.195727        0.010397   \n",
       "8        0.453038      0.011660         0.016208        0.001088   \n",
       "9        3.836496      0.077637         0.143118        0.007209   \n",
       "10       6.292430      0.041790         0.145112        0.024251   \n",
       "11      13.247591      0.108652         0.443316        0.012062   \n",
       "12       0.588178      0.009014         0.020445        0.005487   \n",
       "13       4.639598      0.015241         0.074053        0.003481   \n",
       "14       8.192104      0.281345         0.311169        0.027521   \n",
       "15      12.492856      0.052054         0.189246        0.008384   \n",
       "\n",
       "   param_max_depth param_n_estimators                                  params  \\\n",
       "0                3                 10    {'max_depth': 3, 'n_estimators': 10}   \n",
       "1                3                 50    {'max_depth': 3, 'n_estimators': 50}   \n",
       "2                3                100   {'max_depth': 3, 'n_estimators': 100}   \n",
       "3                3                200   {'max_depth': 3, 'n_estimators': 200}   \n",
       "4                6                 10    {'max_depth': 6, 'n_estimators': 10}   \n",
       "5                6                 50    {'max_depth': 6, 'n_estimators': 50}   \n",
       "6                6                100   {'max_depth': 6, 'n_estimators': 100}   \n",
       "7                6                200   {'max_depth': 6, 'n_estimators': 200}   \n",
       "8                9                 10    {'max_depth': 9, 'n_estimators': 10}   \n",
       "9                9                 50    {'max_depth': 9, 'n_estimators': 50}   \n",
       "10               9                100   {'max_depth': 9, 'n_estimators': 100}   \n",
       "11               9                200   {'max_depth': 9, 'n_estimators': 200}   \n",
       "12              12                 10   {'max_depth': 12, 'n_estimators': 10}   \n",
       "13              12                 50   {'max_depth': 12, 'n_estimators': 50}   \n",
       "14              12                100  {'max_depth': 12, 'n_estimators': 100}   \n",
       "15              12                200  {'max_depth': 12, 'n_estimators': 200}   \n",
       "\n",
       "    split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0            0.664729           0.669417           0.671479   \n",
       "1            0.657979           0.660229           0.660604   \n",
       "2            0.672605           0.673542           0.675792   \n",
       "3            0.664917           0.666042           0.675792   \n",
       "4            0.710294           0.706919           0.713482   \n",
       "5            0.708419           0.709732           0.713107   \n",
       "6            0.709544           0.709544           0.714420   \n",
       "7            0.710669           0.709357           0.715170   \n",
       "8            0.719670           0.725858           0.721920   \n",
       "9            0.726608           0.728296           0.725858   \n",
       "10           0.728483           0.726608           0.725858   \n",
       "11           0.729608           0.726983           0.726045   \n",
       "12           0.721170           0.716670           0.719295   \n",
       "13           0.734858           0.728858           0.729046   \n",
       "14           0.731858           0.731296           0.731483   \n",
       "15           0.734858           0.731858           0.733546   \n",
       "\n",
       "    split3_test_score  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0            0.664291         0.667479        0.003061               15   \n",
       "1            0.651350         0.657540        0.003712               16   \n",
       "2            0.667854         0.672448        0.002894               13   \n",
       "3            0.666354         0.668276        0.004372               14   \n",
       "4            0.700675         0.707843        0.004744               12   \n",
       "5            0.703863         0.708780        0.003314               11   \n",
       "6            0.705926         0.709859        0.003019               10   \n",
       "7            0.705551         0.710187        0.003436                9   \n",
       "8            0.720743         0.722048        0.002339                7   \n",
       "9            0.731808         0.728142        0.002293                6   \n",
       "10           0.732371         0.728330        0.002521                5   \n",
       "11           0.731808         0.728611        0.002261                4   \n",
       "12           0.719617         0.719188        0.001618                8   \n",
       "13           0.732558         0.731330        0.002514                3   \n",
       "14           0.734246         0.732221        0.001187                2   \n",
       "15           0.737434         0.734424        0.002037                1   \n",
       "\n",
       "    split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0             0.669396            0.670146            0.665146   \n",
       "1             0.664521            0.657145            0.654082   \n",
       "2             0.677835            0.675147            0.670521   \n",
       "3             0.672334            0.668896            0.669021   \n",
       "4             0.717027            0.717027            0.718465   \n",
       "5             0.716652            0.716090            0.719777   \n",
       "6             0.718777            0.716777            0.719152   \n",
       "7             0.719152            0.717652            0.719715   \n",
       "8             0.767721            0.773722            0.767221   \n",
       "9             0.770284            0.774347            0.771471   \n",
       "10            0.770284            0.772534            0.775409   \n",
       "11            0.771409            0.773472            0.775534   \n",
       "12            0.835792            0.850919            0.845668   \n",
       "13            0.854732            0.860983            0.862233   \n",
       "14            0.858545            0.860545            0.864671   \n",
       "15            0.858920            0.861608            0.863358   \n",
       "\n",
       "    split3_train_score  mean_train_score  std_train_score  \n",
       "0             0.670042          0.668682         0.002062  \n",
       "1             0.660854          0.659150         0.003920  \n",
       "2             0.676167          0.674918         0.002713  \n",
       "3             0.672605          0.670714         0.001758  \n",
       "4             0.711607          0.716032         0.002621  \n",
       "5             0.715982          0.717125         0.001552  \n",
       "6             0.718420          0.718282         0.000906  \n",
       "7             0.718107          0.718657         0.000818  \n",
       "8             0.771611          0.770069         0.002709  \n",
       "9             0.770611          0.771678         0.001601  \n",
       "10            0.770298          0.772131         0.002103  \n",
       "11            0.770361          0.772694         0.001986  \n",
       "12            0.847053          0.844858         0.005577  \n",
       "13            0.858741          0.859172         0.002853  \n",
       "14            0.860116          0.860969         0.002263  \n",
       "15            0.861554          0.861360         0.001585  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute_df = pd.DataFrame(cv_rf_impute.cv_results_)\n",
    "impute_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 12, 'n_estimators': 200}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf_impute.best_params_\n",
    "# {'max_depth': 12, 'n_estimators': 100}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7344242187492307"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf_impute.best_score_\n",
    "# 0.7337676389523727"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pay_1_value_counts = pay_1_df['PAY_1'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2     2476\n",
       "-1     5047\n",
       " 0    13087\n",
       " 1     3261\n",
       " 2     2378\n",
       " 3      292\n",
       " 4       63\n",
       " 5       23\n",
       " 6       11\n",
       " 7        9\n",
       " 8       17\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_1_value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2    0.092859\n",
       "-1    0.189281\n",
       " 0    0.490812\n",
       " 1    0.122300\n",
       " 2    0.089184\n",
       " 3    0.010951\n",
       " 4    0.002363\n",
       " 5    0.000863\n",
       " 6    0.000413\n",
       " 7    0.000338\n",
       " 8    0.000638\n",
       "Name: PAY_1, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_1_value_counts/pay_1_value_counts.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 200 out of 200 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "y_impute_predict = cv_rf_impute.predict(X_impute_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7391711981998875"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_impute_test, y_impute_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2, figsize=(8,3))\n",
    "axs[0].hist(y_impute_test, bins=bin_edges, align='left')\n",
    "axs[0].set_xticks(bin_edges)\n",
    "axs[0].set_title('Non-missing values of PAY_1')\n",
    "axs[1].hist(y_impute_predict, bins=bin_edges, align='left')\n",
    "axs[1].set_xticks(bin_edges)\n",
    "axs[1].set_title('Model-based imputation')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_impute_all = pay_1_df[features_for_imputation].values\n",
    "y_impute_all = pay_1_df['PAY_1'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_impute = RandomForestClassifier(n_estimators=100, max_depth=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=12)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=12)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_impute.fit(X_impute_all, y_impute_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using the Imputation Model and Comparing it to Other Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fill_pay_1_model = df_missing_pay_1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    Not available\n",
       "28    Not available\n",
       "29    Not available\n",
       "54    Not available\n",
       "60    Not available\n",
       "Name: PAY_1, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill_pay_1_model['PAY_1'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fill_pay_1_model['PAY_1'] = rf_impute.predict(df_fill_pay_1_model[features_for_imputation].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    0\n",
       "28   -1\n",
       "29    0\n",
       "54    0\n",
       "60    0\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill_pay_1_model['PAY_1'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2      30\n",
       "-1     763\n",
       " 0    1715\n",
       " 1     438\n",
       " 2      64\n",
       " 3       7\n",
       " 4       2\n",
       " 6       1\n",
       " 8       1\n",
       "Name: PAY_1, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill_pay_1_model['PAY_1'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_fill_pay_1_train, X_fill_pay_1_test, y_fill_pay_1_train, y_fill_pay_1_test = \\\n",
    "train_test_split(\n",
    "    df_fill_pay_1_model[features_response[:-1]].values,\n",
    "    df_fill_pay_1_model['default payment next month'].values,\n",
    "test_size=0.2, random_state=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2416, 17)\n",
      "(605, 17)\n",
      "(2416,)\n",
      "(605,)\n"
     ]
    }
   ],
   "source": [
    "print(X_fill_pay_1_train.shape)\n",
    "print(X_fill_pay_1_test.shape)\n",
    "print(y_fill_pay_1_train.shape)\n",
    "print(y_fill_pay_1_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_all = np.concatenate((X_train, X_fill_pay_1_train), axis=0)\n",
    "y_train_all = np.concatenate((y_train, y_fill_pay_1_train), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23747, 17)\n",
      "(23747,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_all.shape)\n",
    "print(y_train_all.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=9, n_estimators=200, random_state=4, verbose=1)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    8.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    8.3s finished\n"
     ]
    }
   ],
   "source": [
    "imputation_compare_cv = cross_validate(rf, X_train_all, y_train_all, scoring='roc_auc',\n",
    "                                       cv=k_folds, n_jobs=-1, verbose=1,\n",
    "                                       return_train_score=True, return_estimator=True,\n",
    "                                       error_score='raise-deprecating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76890992, 0.77309591, 0.77166336, 0.77703366])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputation_compare_cv['test_score']\n",
    "# array([0.76890992, 0.77309591, 0.77166336, 0.77703366])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7726757126815554"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(imputation_compare_cv['test_score'])\n",
    "# 0.7726757126815554"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002931480680760725"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(imputation_compare_cv['test_score'])\n",
    "# 0.002931480680760725"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reassign values using mode imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fill_pay_1_model['PAY_1'] = np.zeros_like(df_fill_pay_1_model['PAY_1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill_pay_1_model['PAY_1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_fill_pay_1_train, X_fill_pay_1_test, y_fill_pay_1_train, y_fill_pay_1_test = \\\n",
    "train_test_split(\n",
    "    df_fill_pay_1_model[features_response[:-1]].values,\n",
    "    df_fill_pay_1_model['default payment next month'].values,\n",
    "test_size=0.2, random_state=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_all = np.concatenate((X_train, X_fill_pay_1_train), axis=0)\n",
    "X_test_all = np.concatenate((X_test, X_fill_pay_1_test), axis=0)\n",
    "y_train_all = np.concatenate((y_train, y_fill_pay_1_train), axis=0)\n",
    "y_test_all = np.concatenate((y_test, y_fill_pay_1_test), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23747, 17)\n",
      "(5938, 17)\n",
      "(23747,)\n",
      "(5938,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_all.shape)\n",
    "print(X_test_all.shape)\n",
    "print(y_train_all.shape)\n",
    "print(y_test_all.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:   10.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:   10.2s finished\n"
     ]
    }
   ],
   "source": [
    "imputation_compare_cv = cross_validate(rf, X_train_all, y_train_all, scoring='roc_auc',\n",
    "                                       cv=k_folds, n_jobs=-1, verbose=1,\n",
    "                                       return_train_score=True, return_estimator=True,\n",
    "                                       error_score='raise-deprecating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.772866246168149"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(imputation_compare_cv['test_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confirming Model Performance on the Unseen Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 200 out of 200 | elapsed:    8.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=9, n_estimators=200, random_state=4, verbose=1)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train_all, y_train_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 200 out of 200 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "y_test_all_predict_proba = rf.predict_proba(X_test_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7696243835824927"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test_all, y_test_all_predict_proba[:,1])\n",
    "# 0.7696243835824927"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Characterizing Costs and Savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresholds = np.linspace(0, 1, 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use mean bill amount to estimate savings per prevented default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BILL_AMT1'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[features_response[:-1]].columns[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51601.7433479286"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savings_per_default = np.mean(X_test_all[:, 5])\n",
    "savings_per_default\n",
    "# 51601.7433479286"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_per_counseling = 7500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "effectiveness = 0.70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_pos_pred = np.empty_like(thresholds)\n",
    "cost_of_all_counselings = np.empty_like(thresholds)\n",
    "n_true_pos = np.empty_like(thresholds)\n",
    "savings_of_all_counselings = np.empty_like(thresholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = 0\n",
    "for threshold in thresholds:\n",
    "    pos_pred = y_test_all_predict_proba[:,1]>threshold\n",
    "    n_pos_pred[counter] = sum(pos_pred)\n",
    "    cost_of_all_counselings[counter] = n_pos_pred[counter] * cost_per_counseling\n",
    "    true_pos = pos_pred & y_test_all.astype(bool)\n",
    "    n_true_pos[counter] = sum(true_pos)\n",
    "    savings_of_all_counselings[counter] = n_true_pos[counter] * savings_per_default * effectiveness\n",
    "    \n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_savings = savings_of_all_counselings - cost_of_all_counselings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(thresholds, cost_of_all_counselings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(thresholds, savings_of_all_counselings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.dpi'] = 400\n",
    "plt.plot(thresholds, net_savings)\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Net savings (NT$)')\n",
    "plt.xticks(np.linspace(0,1,11))\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_savings_ix = np.argmax(net_savings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the threshold at which maximum savings is achieved?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds[max_savings_ix]\n",
    "# 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the maximum possible savings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15446325.35991916"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_savings[max_savings_ix]\n",
    "# 15446325.35991916"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge: Deriving Financial Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Everything that is needed prior to this challenge has been included in the notebook above. You should run all the necessary portions of the provided code before beginning these tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(10000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 10 seconds\n"
     ]
    }
   ],
   "source": [
    "# This will autosave your notebook every ten seconds\n",
    "%autosave 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using the testing set, calculate the cost of all defaults if there were no counseling program and output your result.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "eid": "415a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66308240.202088244"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_of_all_defaults = sum(y_test_all == 1) * savings_per_default\n",
    "cost_of_all_defaults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________________\n",
    "**Next, calculate by what percent can the cost of defaults be decreased by the counseling program and output you result.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "eid": "96508"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.29472975431598 %\n"
     ]
    }
   ],
   "source": [
    "print(f\"{(net_savings[max_savings_ix] / cost_of_all_defaults) * 100} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________________\n",
    "**Then, calculate the net savings per account at the optimal threshold and output your result.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "eid": "5d78e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2601.2673223171373"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_savings_per_account = net_savings[max_savings_ix] / len(y_test_all)\n",
    "net_savings_per_account"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________________\n",
    "**Now, plot the net savings per account against the cost of counseling per account for each threshold.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "eid": "71d38"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cost_of_all_counselings / len(y_test_all), net_savings/len(y_test_all))\n",
    "plt.xlabel(\"cost of counseling per account\")\n",
    "plt.ylabel(\"net savings per account\")\n",
    "plt.title(\"Net savings per account vs The cost of counseling per account for each threshold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________________\n",
    "**Next, plot the fraction of accounts predicted as positive (this is called the \"flag rate\") at each threshold.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "eid": "6270e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fractions = n_pos_pred / len(y_test_all)\n",
    "\n",
    "plt.plot(thresholds, fractions)\n",
    "plt.xlabel(\"Thresholds\")\n",
    "plt.ylabel(\"Fraction of accounts (Flag Rate)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________________\n",
    "**Next, plot a precision-recall curve for the testing data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "eid": "0edc5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\viksb\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_true_pos / len(y_test_all), np.divide(n_true_pos,n_pos_pred))\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.title(\"Precion Recall Curve\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________________________________________________________________________\n",
    "**Finally, plot precision and recall separately on the y-axis against threshold on the x-axis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "eid": "88413"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\viksb\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAAAADAgDABAAAAAAAAADAgTAAAAAAAAAAADAgTAAAAAAAAAAADwgQAAAAAAAAAwIAwAQAAAAAAAAAwIEwAAAAAAAAAAAwIEwAAAAAAAAAAA8IEAAAAAAAAAMCAMAEAAAAAAAAAMCBMAAAAAAAAAAAMCBMAAAAAAAAAAAPCBAAAAAAAAADAgDABAAAAAAAAADAgTAAAAAAAAAAADAgTAAAAAAAAAAADwgQAAAAAAAAAwIAwAQAAAAAAAAAwIEwAAAAAAAAAAAwIEwAAAAAAAAAAA8IEAAAAAAAAAMCAMAEAAAAAAAAAMPB/AWUC+8Tnp1/QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision = n_true_pos / n_pos_pred\n",
    "recall = n_true_pos/sum(y_test_all)\n",
    "\n",
    "plt.plot(thresholds, precision, label = \"Precision\")\n",
    "plt.plot(thresholds, recall, label = \"Recall\")\n",
    "plt.xlabel(\"Thresholds\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(rf, open(\"final_model.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}