Multi Agent Credit Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d21ee28c",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "## This notebook extends the log and XGBoost model notebooks.\n",
    "\n",
    "In this notebook, a Multi-Agent model is used to predict loan defaults, with a primary focus on reducing false negatives—accurately identifying clients who will default on their loans. \n",
    "\n",
    "While there is less emphasis on reducing false positives (predicting defaults for clients who do not actually default), the Multi-Agent model aims to provide a more reliable assessment of loan applications.\n",
    "\n",
    "Overall, the model's accuracy and precision decreased by 3%, but recall increased significantly by 31%, improving the detection of actual defaulters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08516d7e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "credit_model.visualize_performance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ea7d096",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, precision_score, recall_score\n",
    "import xgboost as xgb\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"sklearn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "55b52a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 32581 rows and 12 features.\n",
      "person_age                      int64\n",
      "person_income                   int64\n",
      "person_home_ownership          object\n",
      "person_emp_length             float64\n",
      "loan_intent                    object\n",
      "loan_grade                     object\n",
      "loan_amnt                       int64\n",
      "loan_int_rate                 float64\n",
      "loan_status                     int64\n",
      "loan_percent_income           float64\n",
      "cb_person_default_on_file      object\n",
      "cb_person_cred_hist_length      int64\n",
      "dtype: object\n",
      "Logistic Regression Model Evaluation (PCA Log Model 1):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.92      0.90      8195\n",
      "           1       0.63      0.53      0.58      2230\n",
      "\n",
      "    accuracy                           0.83     10425\n",
      "   macro avg       0.76      0.72      0.74     10425\n",
      "weighted avg       0.83      0.83      0.83     10425\n",
      "\n",
      "XGBoost Model Evaluation (Multi-Agent Model):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.90      0.93      7684\n",
      "           1       0.70      0.84      0.76      2089\n",
      "\n",
      "    accuracy                           0.89      9773\n",
      "   macro avg       0.82      0.87      0.84      9773\n",
      "weighted avg       0.90      0.89      0.89      9773\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class CreditRiskModel:\n",
    "    def __init__(self, data_path, n_components=14):\n",
    "        self.data_path = data_path\n",
    "        self.data = None\n",
    "        self.cleaned_data = None\n",
    "        self.X_train = None\n",
    "        self.X_test = None\n",
    "        self.y_train = None\n",
    "        self.y_test = None\n",
    "        self.y_pred_thres = None\n",
    "        self.log_preds = None\n",
    "        self.scaler = MinMaxScaler(feature_range=[0, 1])\n",
    "        self.pca = PCA(n_components=n_components)\n",
    "        self.log_model = LogisticRegression()\n",
    "        self.xgb_model = xgb.XGBClassifier()\n",
    "        self.metrics = {'precision': [73, 94], 'recall': [53, 73]}  # Add provided metrics here\n",
    "    \n",
    "    \n",
    "    def load_data(self):\n",
    "        self.data = pd.read_csv(self.data_path)\n",
    "        print(f\"There are {self.data.shape[0]} rows and {self.data.shape[1]} features.\")\n",
    "        print(self.data.dtypes)\n",
    "        return self.data\n",
    "    \n",
    "    \n",
    "    def clean_data(self):\n",
    "        self.data = self.data.rename(\n",
    "            columns={\"cb_person_default_on_file\": \"default_hist\",\n",
    "                     \"cb_person_cred_hist_length\": \"cr_hist_len\"})\n",
    "        \n",
    "        # Handle missing values\n",
    "        self.data['person_emp_length'].fillna(self.data['person_emp_length'].median(), inplace=True)\n",
    "        self.data['loan_int_rate'].fillna(self.data['loan_int_rate'].median(), inplace=True)\n",
    "        \n",
    "        # Remove outliers\n",
    "        self.cleaned_data = self.data[self.data['person_age'] <= 100]\n",
    "\n",
    "        # One-hot encode categorical variables\n",
    "        num_col = self.cleaned_data.select_dtypes(exclude='object')\n",
    "        char_col = self.cleaned_data.select_dtypes(include='object')\n",
    "        encoded_char_col = pd.get_dummies(char_col)\n",
    "        self.cleaned_data = pd.concat([num_col, encoded_char_col], axis=1)\n",
    "        \n",
    "        return self.cleaned_data\n",
    "    \n",
    "    \n",
    "    def preprocess_data(self):\n",
    "        Y = self.cleaned_data['loan_status']\n",
    "        X = self.cleaned_data.drop(columns=['loan_status'])\n",
    "        \n",
    "        # Scale features\n",
    "        data_rescaled = self.scaler.fit_transform(X)\n",
    "\n",
    "        # Apply PCA\n",
    "        pca_dataset = self.pca.fit_transform(data_rescaled)\n",
    "        \n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(pca_dataset, Y, random_state=2020, test_size=0.32)\n",
    "    \n",
    "    \n",
    "    def train_log_model(self):\n",
    "        self.log_model.fit(self.X_train, np.ravel(self.y_train))\n",
    "        y_pred_proba = self.log_model.predict_proba(self.X_test)[:, 1]\n",
    "        pca_predict_log = pd.DataFrame(y_pred_proba, columns=['prob_default'])\n",
    "        self.y_pred_thres = pca_predict_log['prob_default'].apply(lambda x: 1 if x > 0.36 else 0)\n",
    "        self.log_preds = pd.DataFrame(self.y_pred_thres, columns=['log_preds'])\n",
    "        \n",
    "        print(\"Logistic Regression Model Evaluation (PCA Log Model 1):\")\n",
    "        print(classification_report(self.y_test, self.y_pred_thres))\n",
    "        \n",
    "        # Calculate and store precision and recall\n",
    "        precision = precision_score(self.y_test, self.y_pred_thres)\n",
    "        recall = recall_score(self.y_test, self.y_pred_thres)\n",
    "\n",
    "        \n",
    "    def preprocess_for_xgb(self):\n",
    "        Y = self.cleaned_data['loan_status']\n",
    "        X = self.cleaned_data.drop(columns=['loan_status'])\n",
    "        \n",
    "        # Add log_preds as a feature and convert to numerical type\n",
    "        log_preds_numerical = self.log_preds.astype(int)  # Ensure log_preds is numerical\n",
    "        X = pd.concat([X.reset_index(drop=True), log_preds_numerical.reset_index(drop=True)], axis=1)\n",
    "        \n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, Y, random_state=2020, test_size=0.30)\n",
    "    \n",
    "    \n",
    "    def train_xgb_model(self):\n",
    "        self.xgb_model.fit(self.X_train, np.ravel(self.y_train))\n",
    "    \n",
    "    \n",
    "    def evaluate_xgb_model(self, log_threshold=0.36):\n",
    "        predict_xgb = self.xgb_model.predict_proba(self.X_test)[:, 1]\n",
    "        predict_xgb_prob = pd.DataFrame(predict_xgb, columns=['Default Probability'])\n",
    "        y_pred_thres = predict_xgb_prob['Default Probability'].apply(lambda x: 1 if x > log_threshold else 0)\n",
    "        \n",
    "        print(\"XGBoost Model Evaluation (Multi-Agent Model):\")\n",
    "        print(classification_report(self.y_test, y_pred_thres))\n",
    "        \n",
    "        # Calculate and store precision and recall\n",
    "        precision = precision_score(self.y_test, y_pred_thres)\n",
    "        recall = recall_score(self.y_test, y_pred_thres)\n",
    "        self.metrics['precision'].append(round(precision * 100))\n",
    "        self.metrics['recall'].append(round(recall * 100))\n",
    "    \n",
    "    \n",
    "    def visualize_performance(self):\n",
    "        models = ['Log Model Only', 'XGB Model Only', 'Multi-Agent Model']\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "\n",
    "        for metric in self.metrics:\n",
    "            values = self.metrics[metric]\n",
    "            plt.plot(models, values, marker='o', label=metric)\n",
    "            for i, txt in enumerate(values):\n",
    "                plt.annotate(f'{txt}', (models[i], values[i]), textcoords=\"offset points\", xytext=(0,10), ha='center')\n",
    "\n",
    "        plt.xlabel('Models')\n",
    "        plt.ylabel('Scores (%)')\n",
    "        plt.title('Precision and Recall Across Models')\n",
    "        plt.legend()\n",
    "        plt.ylim(50, 100)\n",
    "        plt.show()\n",
    "        \n",
    "\n",
    "        \n",
    "# Workflow\n",
    "credit_model = CreditRiskModel('credit_risk_dataset.csv')\n",
    "credit_model.load_data()\n",
    "credit_model.clean_data()\n",
    "credit_model.preprocess_data()\n",
    "credit_model.train_log_model()\n",
    "credit_model.preprocess_for_xgb()\n",
    "credit_model.train_xgb_model()\n",
    "credit_model.evaluate_xgb_model(log_threshold=0.17)\n",
    "credit_model.visualize_performance()\n",
    "\n",
    "\n",
    "# Access the final predictions for further use\n",
    "final_predictions = credit_model.y_pred_thres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2cb495ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Model Evaluation (Multi-Agent Model):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.92      0.94      7684\n",
      "           1       0.74      0.82      0.78      2089\n",
      "\n",
      "    accuracy                           0.90      9773\n",
      "   macro avg       0.84      0.87      0.86      9773\n",
      "weighted avg       0.90      0.90      0.90      9773\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#0.20\n",
    "credit_model.evaluate_xgb_model(log_threshold=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cd845f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Model Evaluation (Multi-Agent Model):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.90      0.93      7684\n",
      "           1       0.70      0.84      0.76      2089\n",
      "\n",
      "    accuracy                           0.89      9773\n",
      "   macro avg       0.82      0.87      0.84      9773\n",
      "weighted avg       0.90      0.89      0.89      9773\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#0.15\n",
    "credit_model.evaluate_xgb_model(log_threshold=0.17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fee3f3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "10420    1\n",
       "10421    0\n",
       "10422    0\n",
       "10423    0\n",
       "10424    1\n",
       "Name: prob_default, Length: 10425, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a1c4852",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "type object 'CreditRiskModel' has no attribute 'y_test'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/wj/gwqv8kfj18317lgnlmf6zypw0000gn/T/ipykernel_20749/3379262972.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mCreditRiskModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: type object 'CreditRiskModel' has no attribute 'y_test'"
     ]
    }
   ],
   "source": [
    "CreditRiskModel.y_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bf28c229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32581"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9fc711bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        1\n",
      "1        0\n",
      "2        1\n",
      "3        1\n",
      "4        1\n",
      "        ..\n",
      "32571    0\n",
      "32572    0\n",
      "32573    1\n",
      "32574    0\n",
      "32575    0\n",
      "Name: Default Probability, Length: 32576, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Predict with new data\n",
    "new_predictions = credit_model.predict_new_data('credit_risk_dataset.csv')\n",
    "print(new_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6a00c55c",
   "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>person_age</th>\n",
       "      <th>person_income</th>\n",
       "      <th>person_home_ownership</th>\n",
       "      <th>person_emp_length</th>\n",
       "      <th>loan_intent</th>\n",
       "      <th>loan_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_int_rate</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>loan_percent_income</th>\n",
       "      <th>cb_person_default_on_file</th>\n",
       "      <th>cb_person_cred_hist_length</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>59000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>123.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>D</td>\n",
       "      <td>35000</td>\n",
       "      <td>16.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.59</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>9600</td>\n",
       "      <td>OWN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>B</td>\n",
       "      <td>1000</td>\n",
       "      <td>11.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>9600</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>5500</td>\n",
       "      <td>12.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.57</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>65500</td>\n",
       "      <td>RENT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>15.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>54400</td>\n",
       "      <td>RENT</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>14.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   person_age  person_income person_home_ownership  person_emp_length  \\\n",
       "0          22          59000                  RENT              123.0   \n",
       "1          21           9600                   OWN                5.0   \n",
       "2          25           9600              MORTGAGE                1.0   \n",
       "3          23          65500                  RENT                4.0   \n",
       "4          24          54400                  RENT                8.0   \n",
       "\n",
       "  loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
       "0    PERSONAL          D      35000          16.02            1   \n",
       "1   EDUCATION          B       1000          11.14            0   \n",
       "2     MEDICAL          C       5500          12.87            1   \n",
       "3     MEDICAL          C      35000          15.23            1   \n",
       "4     MEDICAL          C      35000          14.27            1   \n",
       "\n",
       "   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \\\n",
       "0                 0.59                         Y                           3   \n",
       "1                 0.10                         N                           2   \n",
       "2                 0.57                         N                           3   \n",
       "3                 0.53                         N                           2   \n",
       "4                 0.55                         Y                           4   \n",
       "\n",
       "   y_pred  \n",
       "0     1.0  \n",
       "1     0.0  \n",
       "2     1.0  \n",
       "3     1.0  \n",
       "4     1.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"credit_risk_dataset.csv\")\n",
    "data['y_pred'] = new_predictions\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5e33cee0",
   "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>new_predictions</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.650011</td>\n",
       "      <td>0.131703</td>\n",
       "      <td>0.781713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.156594</td>\n",
       "      <td>0.061539</td>\n",
       "      <td>0.218133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.806605</td>\n",
       "      <td>0.193241</td>\n",
       "      <td>0.999847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "new_predictions         0         1       All\n",
       "loan_status                                  \n",
       "0                0.650011  0.131703  0.781713\n",
       "1                0.156594  0.061539  0.218133\n",
       "All              0.806605  0.193241  0.999847"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "pivot_table = pd.crosstab(data['loan_status'], new_predictions, rownames=['loan_status'], colnames=['new_predictions'], margins=True)/num_rows\n",
    "\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2bac4326",
   "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></th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cb_person_default_on_file</th>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">N</th>\n",
       "      <th>0</th>\n",
       "      <td>21896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Y</th>\n",
       "      <th>0</th>\n",
       "      <td>3573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       y_pred\n",
       "cb_person_default_on_file loan_status        \n",
       "N                         0             21896\n",
       "                          1              4935\n",
       "Y                         0              3573\n",
       "                          1              2172"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_rows = data.shape[0]\n",
    "\n",
    "# group values bby history, status, and the values are y_pred\n",
    "group_hst_status_pred_sum = data.groupby(['cb_person_default_on_file', 'loan_status'])['y_pred'].count()\n",
    "group_hst_status_pred_sum = pd.DataFrame(group_hst_status_pred_sum)\n",
    "group_hst_status_pred_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dfe9d2f5",
   "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>loan_status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cb_person_default_on_file</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>0.672048</td>\n",
       "      <td>0.151469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.109665</td>\n",
       "      <td>0.066665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_status                       0         1\n",
       "cb_person_default_on_file                    \n",
       "N                          0.672048  0.151469\n",
       "Y                          0.109665  0.066665"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create pivot table where the values are the count of pred values, {0,1}\n",
    "pivot_table = group_hst_status_pred_sum.reset_index().pivot(index='cb_person_default_on_file', columns='loan_status', values='y_pred')\n",
    "pivot_table / num_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "768114ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### round(0.151469+0.066665,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bbd311c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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