2.Credit Risk Modeling_PD Model Building.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Risk Modeling: PD Model Building\n",
    "*Author: Shawn Sun*\n",
    "<br>\n",
    "*Date: 2021/01/23*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_inputs_train = \\\n",
    "pd.read_csv('loan_data_inputs_train.csv', index_col = 0)\n",
    "loan_data_targets_train = \\\n",
    "pd.read_csv('loan_data_targets_train.csv', index_col = 0)\n",
    "loan_data_inputs_test = \\\n",
    "pd.read_csv('loan_data_inputs_test.csv', index_col = 0)\n",
    "loan_data_targets_test = \\\n",
    "pd.read_csv('loan_data_targets_test.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7450.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.84</td>\n",
       "      <td>282.40</td>\n",
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       "<p>5 rows × 324 columns</p>\n",
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       "        Unnamed: 0.1        id  member_id  loan_amnt  funded_amnt  \\\n",
       "427211        427211  12796369   14818505      24000        24000   \n",
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       "\n",
       "        funded_amnt_inv        term  int_rate  installment grade  ...  \\\n",
       "427211          24000.0   36 months      8.90       762.08     A  ...   \n",
       "206088          10000.0   36 months     14.33       343.39     C  ...   \n",
       "136020          20425.0   36 months      8.90       648.56     A  ...   \n",
       "412305          17200.0   36 months     16.59       609.73     D  ...   \n",
       "36159            7450.0   36 months     12.84       282.40     C  ...   \n",
       "\n",
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       "\n",
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       "\n",
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       "136020                            0                          0  \n",
       "412305                            0                          0  \n",
       "36159                             0                          0  \n",
       "\n",
       "[5 rows x 324 columns]"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "loan_data_inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "loan_data_targets_train.head()"
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  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373028, 324)"
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_inputs_train.shape"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(373028, 1)"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_targets_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 324)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data_inputs_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "loan_data_targets_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Selecting Features: dataframe only with dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_cat = pd.read_csv('List of Categories.csv')\n",
    "#Read the csv: list of categories\n",
    "cat_lst = lst_cat.iloc[:,0].tolist()\n",
    "#Convert the dataframe to a list\n",
    "inputs_train_with_ref_cat = loan_data_inputs_train.loc[: ,cat_lst]\n",
    "#Only select columns of dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_ref = pd.read_csv('Reference Categories.csv')\n",
    "ref_lst = lst_ref.iloc[:,0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
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       "136020        1        0        0        0        0        0   \n",
       "412305        0        0        0        1        0        0   \n",
       "36159         0        0        1        0        0        0   \n",
       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "427211                   0                        1                 0   \n",
       "206088                   0                        1                 0   \n",
       "136020                   0                        1                 0   \n",
       "412305                   0                        0                 0   \n",
       "36159                    0                        1                 0   \n",
       "\n",
       "        addr_state:NY  ...  mths_since_last_delinq:Missing  \\\n",
       "427211              0  ...                               1   \n",
       "206088              0  ...                               0   \n",
       "136020              0  ...                               0   \n",
       "412305              0  ...                               0   \n",
       "36159               0  ...                               1   \n",
       "\n",
       "        mths_since_last_delinq:4-30  mths_since_last_delinq:31-56  \\\n",
       "427211                            0                             0   \n",
       "206088                            1                             0   \n",
       "136020                            0                             1   \n",
       "412305                            1                             0   \n",
       "36159                             0                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:Missing  \\\n",
       "427211                            0                               1   \n",
       "206088                            0                               1   \n",
       "136020                            0                               1   \n",
       "412305                            0                               1   \n",
       "36159                             0                               1   \n",
       "\n",
       "        mths_since_last_record:3-20  mths_since_last_record:21-31  \\\n",
       "427211                            0                             0   \n",
       "206088                            0                             0   \n",
       "136020                            0                             0   \n",
       "412305                            0                             0   \n",
       "36159                             0                             0   \n",
       "\n",
       "        mths_since_last_record:32-80  mths_since_last_record:81-86  \\\n",
       "427211                             0                             0   \n",
       "206088                             0                             0   \n",
       "136020                             0                             0   \n",
       "412305                             0                             0   \n",
       "36159                              0                             0   \n",
       "\n",
       "        mths_since_last_record:>86  \n",
       "427211                           0  \n",
       "206088                           0  \n",
       "136020                           0  \n",
       "412305                           0  \n",
       "36159                            0  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_train = inputs_train_with_ref_cat.drop(ref_lst, axis = 1)\n",
    "# From the dataframe with input variables, we drop the variables with variable names in the list with reference categories. \n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. PD Model Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LogisticRegression()\n",
    "# Create an instance of an object from the 'LogisticRegression' class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(inputs_train, loan_data_targets_train)\n",
    "# Estimates the coefficients of the object from the 'LogisticRegression' class\n",
    "#  with inputs (independent variables) contained in the first dataframe\n",
    "#  and targets (dependent variables) contained in the second dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.33110033])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reg.intercept_\n",
    "# Displays the intercept contain in the estimated (\"fitted\") object \n",
    "#  from the 'LogisticRegression' class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.16008588,  0.90605059,  0.70886039,  0.51906067,  0.33683742,\n",
       "         0.14489978,  0.09080023,  0.10678409,  0.02966993,  0.0552502 ,\n",
       "         0.05660928,  0.05940581,  0.07497421,  0.13244703,  0.09936455,\n",
       "         0.18112665,  0.2249629 ,  0.2203493 ,  0.25915686,  0.31662656,\n",
       "         0.51750841,  0.08806786, -0.01075401,  0.30676978,  0.2025115 ,\n",
       "         0.21415482,  0.26666793,  0.05443059,  0.07807787,  0.10017768,\n",
       "         0.1253007 ,  0.08959918,  0.06084498,  0.12334573,  1.07505717,\n",
       "         0.87435371,  0.77403265,  0.57294223,  0.41066048,  0.16587621,\n",
       "        -0.06981087,  0.8505668 ,  0.5383496 ,  0.28958857,  0.10065063,\n",
       "         0.05613176,  0.03783793,  0.0795651 ,  0.11955873,  0.12625758,\n",
       "         0.07428006,  0.03130486,  0.63477458,  0.49137981,  0.282013  ,\n",
       "        -0.01101435, -0.10865451, -0.1343336 , -0.14973945, -0.15435139,\n",
       "        -0.11932467, -0.20970968,  0.12264508,  0.17843345, -0.02093652,\n",
       "         0.02532919,  0.21758349,  0.03470324,  0.0027314 ,  0.00123197,\n",
       "         0.01449311,  0.03490339,  0.06103252,  0.20173698, -0.06016152,\n",
       "         0.00974039,  0.10682947,  0.17591629,  0.25042255,  0.32488202,\n",
       "         0.40130163,  0.41911819,  0.49752298,  0.58507497,  0.51955976,\n",
       "         0.20193409,  0.33251395,  0.3576544 ,  0.29867307,  0.22517035,\n",
       "         0.12727895,  0.11596556,  0.08168446,  0.0426093 ,  0.08301141,\n",
       "         0.15152294,  0.16196566,  0.11387327,  0.3403718 ,  0.43560984,\n",
       "         0.36448053,  0.52706722,  0.20185205,  0.25469222]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reg.coef_\n",
    "# Displays the coefficients contained in the estimated (\"fitted\") object \n",
    "#  from the 'LogisticRegression' class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = inputs_train.columns.values\n",
    "# Stores the names of the columns of a dataframe in a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
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       "    }\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>Feature Name</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.331100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.160086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.906051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.708860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.519061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.364481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.527067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.201852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.254692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Feature Name  Coefficients\n",
       "0                       Intercept     -1.331100\n",
       "1                         grade:A      1.160086\n",
       "2                         grade:B      0.906051\n",
       "3                         grade:C      0.708860\n",
       "4                         grade:D      0.519061\n",
       "..                            ...           ...\n",
       "100   mths_since_last_record:3-20      0.435610\n",
       "101  mths_since_last_record:21-31      0.364481\n",
       "102  mths_since_last_record:32-80      0.527067\n",
       "103  mths_since_last_record:81-86      0.201852\n",
       "104    mths_since_last_record:>86      0.254692\n",
       "\n",
       "[105 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table = pd.DataFrame(columns=['Feature Name'], data = feature_name)\n",
    "# Create a dataframe with a column titled 'Feature name' and \n",
    "#  row values contained in the 'feature_name' variable.\n",
    "summary_table['Coefficients'] = np.transpose(reg.coef_)\n",
    "# Creates a new column in the dataframe, called 'Coefficients',\n",
    "#  with row values the transposed coefficients \n",
    "#  from the 'LogisticRegression' object\n",
    "summary_table.index = summary_table.index + 1\n",
    "# Increase the index of every row of the dataframe with 1.\n",
    "summary_table.loc[0] = ['Intercept', reg.intercept_[0]]\n",
    "# Assigns values of the row with index 0 of the dataframe.\n",
    "summary_table = summary_table.sort_index()\n",
    "# Sort the dataframe by index.\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Build Logistic Regression Model with P-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As there is no built-in method to calcualte P values for \n",
    "#  sklearn logistic regression\n",
    "\n",
    "# Build a Class to display p-values for logistic regression in sklearn.\n",
    "\n",
    "from sklearn import linear_model\n",
    "import scipy.stats as stat\n",
    "\n",
    "class LogisticRegression_with_p_values:\n",
    "    \n",
    "    def __init__(self,*args,**kwargs):\n",
    "        self.model = linear_model.LogisticRegression(*args,**kwargs)\n",
    "\n",
    "    def fit(self,X,y):\n",
    "        self.model.fit(X,y)\n",
    "        denom = (2.0 * (1.0 + np.cosh(self.model.decision_function(X))))\n",
    "        denom = np.tile(denom,(X.shape[1],1)).T\n",
    "        F_ij = np.dot((X / denom).T,X)\n",
    "        Cramer_Rao = np.linalg.inv(F_ij)\n",
    "        sigma_estimates = np.sqrt(np.diagonal(Cramer_Rao))\n",
    "        z_scores = self.model.coef_[0] / sigma_estimates\n",
    "        p_values = [stat.norm.sf(abs(x)) * 2 for x in z_scores]\n",
    "        self.coef_ = self.model.coef_\n",
    "        self.intercept_ = self.model.intercept_\n",
    "        self.p_values = p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.331100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.160086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.906051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.708860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.519061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.364481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.527067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.201852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.254692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Feature name  Coefficients\n",
       "0                       Intercept     -1.331100\n",
       "1                         grade:A      1.160086\n",
       "2                         grade:B      0.906051\n",
       "3                         grade:C      0.708860\n",
       "4                         grade:D      0.519061\n",
       "..                            ...           ...\n",
       "100   mths_since_last_record:3-20      0.435610\n",
       "101  mths_since_last_record:21-31      0.364481\n",
       "102  mths_since_last_record:32-80      0.527067\n",
       "103  mths_since_last_record:81-86      0.201852\n",
       "104    mths_since_last_record:>86      0.254692\n",
       "\n",
       "[105 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replicate the codes in early section\n",
    "reg = LogisticRegression_with_p_values()\n",
    "reg.fit(inputs_train, loan_data_targets_train)\n",
    "summary_table = pd.DataFrame(columns = ['Feature name'], data = feature_name)\n",
    "summary_table['Coefficients'] = np.transpose(reg.coef_)\n",
    "summary_table.index = summary_table.index + 1\n",
    "summary_table.loc[0] = ['Intercept', reg.intercept_[0]]\n",
    "summary_table = summary_table.sort_index()\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store p-values for easier access\n",
    "p_values = reg.p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the value 'NaN' in the beginning of the variable with p-values\n",
    "# Convert it to a array\n",
    "p_values = np.append(np.nan, np.array(p_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.331100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.160086</td>\n",
       "      <td>1.708063e-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.906051</td>\n",
       "      <td>1.040571e-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.708860</td>\n",
       "      <td>6.551899e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.519061</td>\n",
       "      <td>4.997560e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.435610</td>\n",
       "      <td>6.075661e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.364481</td>\n",
       "      <td>1.780753e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.527067</td>\n",
       "      <td>5.046704e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.201852</td>\n",
       "      <td>5.754549e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.254692</td>\n",
       "      <td>4.097882e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Feature name  Coefficients      P Values\n",
       "0                       Intercept     -1.331100           NaN\n",
       "1                         grade:A      1.160086  1.708063e-37\n",
       "2                         grade:B      0.906051  1.040571e-49\n",
       "3                         grade:C      0.708860  6.551899e-36\n",
       "4                         grade:D      0.519061  4.997560e-22\n",
       "..                            ...           ...           ...\n",
       "100   mths_since_last_record:3-20      0.435610  6.075661e-04\n",
       "101  mths_since_last_record:21-31      0.364481  1.780753e-03\n",
       "102  mths_since_last_record:32-80      0.527067  5.046704e-09\n",
       "103  mths_since_last_record:81-86      0.201852  5.754549e-02\n",
       "104    mths_since_last_record:>86      0.254692  4.097882e-03\n",
       "\n",
       "[105 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table['P Values'] = p_values\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Build Logistic Regression Models without Insignificant Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  grade:G  \\\n",
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       "\n",
       "        home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  \\\n",
       "427211                                   0                   0   \n",
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       "...                                    ...                 ...   \n",
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       "\n",
       "        home_ownership:MORTGAGE  ...  mths_since_last_delinq:4-30  \\\n",
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       "\n",
       "[373028 rows x 101 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Overwrite the dataframe with the significant categories\n",
    "\n",
    "lst_cat = pd.read_csv('List of Sig Categories.csv')\n",
    "#Read the csv: list of significant categories\n",
    "cat_lst = lst_cat.iloc[:,0].tolist()\n",
    "#Convert the dataframe to a list\n",
    "inputs_train_with_ref_cat = loan_data_inputs_train.loc[: ,cat_lst]\n",
    "#Only select columns of dummy variables\n",
    "inputs_train_with_ref_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>grade:B</th>\n",
       "      <th>grade:C</th>\n",
       "      <th>grade:D</th>\n",
       "      <th>grade:E</th>\n",
       "      <th>grade:F</th>\n",
       "      <th>home_ownership:OWN</th>\n",
       "      <th>home_ownership:MORTGAGE</th>\n",
       "      <th>addr_state:NM_VA</th>\n",
       "      <th>addr_state:NY</th>\n",
       "      <th>...</th>\n",
       "      <th>mths_since_last_delinq:Missing</th>\n",
       "      <th>mths_since_last_delinq:4-30</th>\n",
       "      <th>mths_since_last_delinq:31-56</th>\n",
       "      <th>mths_since_last_delinq:&gt;=57</th>\n",
       "      <th>mths_since_last_record:Missing</th>\n",
       "      <th>mths_since_last_record:3-20</th>\n",
       "      <th>mths_since_last_record:21-31</th>\n",
       "      <th>mths_since_last_record:32-80</th>\n",
       "      <th>mths_since_last_record:81-86</th>\n",
       "      <th>mths_since_last_record:&gt;86</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>427211</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206088</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136020</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412305</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36159</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259178</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365838</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131932</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146867</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373028 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "427211        1        0        0        0        0        0   \n",
       "206088        0        0        1        0        0        0   \n",
       "136020        1        0        0        0        0        0   \n",
       "412305        0        0        0        1        0        0   \n",
       "36159         0        0        1        0        0        0   \n",
       "...         ...      ...      ...      ...      ...      ...   \n",
       "259178        0        1        0        0        0        0   \n",
       "365838        0        1        0        0        0        0   \n",
       "131932        0        1        0        0        0        0   \n",
       "146867        0        1        0        0        0        0   \n",
       "121958        0        1        0        0        0        0   \n",
       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "427211                   0                        1                 0   \n",
       "206088                   0                        1                 0   \n",
       "136020                   0                        1                 0   \n",
       "412305                   0                        0                 0   \n",
       "36159                    0                        1                 0   \n",
       "...                    ...                      ...               ...   \n",
       "259178                   0                        1                 0   \n",
       "365838                   0                        0                 0   \n",
       "131932                   0                        0                 0   \n",
       "146867                   0                        1                 0   \n",
       "121958                   0                        1                 0   \n",
       "\n",
       "        addr_state:NY  ...  mths_since_last_delinq:Missing  \\\n",
       "427211              0  ...                               1   \n",
       "206088              0  ...                               0   \n",
       "136020              0  ...                               0   \n",
       "412305              0  ...                               0   \n",
       "36159               0  ...                               1   \n",
       "...               ...  ...                             ...   \n",
       "259178              0  ...                               0   \n",
       "365838              1  ...                               0   \n",
       "131932              0  ...                               1   \n",
       "146867              0  ...                               1   \n",
       "121958              0  ...                               0   \n",
       "\n",
       "        mths_since_last_delinq:4-30  mths_since_last_delinq:31-56  \\\n",
       "427211                            0                             0   \n",
       "206088                            1                             0   \n",
       "136020                            0                             1   \n",
       "412305                            1                             0   \n",
       "36159                             0                             0   \n",
       "...                             ...                           ...   \n",
       "259178                            1                             0   \n",
       "365838                            1                             0   \n",
       "131932                            0                             0   \n",
       "146867                            0                             0   \n",
       "121958                            0                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:Missing  \\\n",
       "427211                            0                               1   \n",
       "206088                            0                               1   \n",
       "136020                            0                               1   \n",
       "412305                            0                               1   \n",
       "36159                             0                               1   \n",
       "...                             ...                             ...   \n",
       "259178                            0                               0   \n",
       "365838                            0                               1   \n",
       "131932                            0                               1   \n",
       "146867                            0                               1   \n",
       "121958                            1                               0   \n",
       "\n",
       "        mths_since_last_record:3-20  mths_since_last_record:21-31  \\\n",
       "427211                            0                             0   \n",
       "206088                            0                             0   \n",
       "136020                            0                             0   \n",
       "412305                            0                             0   \n",
       "36159                             0                             0   \n",
       "...                             ...                           ...   \n",
       "259178                            0                             0   \n",
       "365838                            0                             0   \n",
       "131932                            0                             0   \n",
       "146867                            0                             0   \n",
       "121958                            0                             0   \n",
       "\n",
       "        mths_since_last_record:32-80  mths_since_last_record:81-86  \\\n",
       "427211                             0                             0   \n",
       "206088                             0                             0   \n",
       "136020                             0                             0   \n",
       "412305                             0                             0   \n",
       "36159                              0                             0   \n",
       "...                              ...                           ...   \n",
       "259178                             1                             0   \n",
       "365838                             0                             0   \n",
       "131932                             0                             0   \n",
       "146867                             0                             0   \n",
       "121958                             0                             0   \n",
       "\n",
       "        mths_since_last_record:>86  \n",
       "427211                           0  \n",
       "206088                           0  \n",
       "136020                           0  \n",
       "412305                           0  \n",
       "36159                            0  \n",
       "...                            ...  \n",
       "259178                           0  \n",
       "365838                           0  \n",
       "131932                           0  \n",
       "146867                           0  \n",
       "121958                           1  \n",
       "\n",
       "[373028 rows x 84 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the reference categories\n",
    "lst_ref = pd.read_csv('Sig Ref Categories.csv')\n",
    "ref_lst = lst_ref.iloc[:,0].tolist()\n",
    "inputs_train = inputs_train_with_ref_cat.drop(ref_lst, axis = 1)\n",
    "inputs_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "reg2 = LogisticRegression_with_p_values()\n",
    "reg2.fit(inputs_train, loan_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.440625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.350710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.502956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.175839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.232707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Feature name  Coefficients\n",
       "0                      Intercept     -1.374036\n",
       "1                        grade:A      1.123662\n",
       "2                        grade:B      0.878918\n",
       "3                        grade:C      0.684796\n",
       "4                        grade:D      0.496923\n",
       "..                           ...           ...\n",
       "80   mths_since_last_record:3-20      0.440625\n",
       "81  mths_since_last_record:21-31      0.350710\n",
       "82  mths_since_last_record:32-80      0.502956\n",
       "83  mths_since_last_record:81-86      0.175839\n",
       "84    mths_since_last_record:>86      0.232707\n",
       "\n",
       "[85 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_name = inputs_train.columns.values\n",
    "summary_table = pd.DataFrame(columns = ['Feature name'], data = feature_name)\n",
    "summary_table['Coefficients'] = np.transpose(reg2.coef_)\n",
    "summary_table.index = summary_table.index + 1\n",
    "summary_table.loc[0] = ['Intercept', reg2.intercept_[0]]\n",
    "summary_table = summary_table.sort_index()\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123662</td>\n",
       "      <td>3.228237e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "      <td>4.278551e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "      <td>6.712386e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
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       "      <th>80</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.440625</td>\n",
       "      <td>3.347130e-04</td>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.350710</td>\n",
       "      <td>1.832454e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.502956</td>\n",
       "      <td>3.964444e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.175839</td>\n",
       "      <td>8.604680e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.232707</td>\n",
       "      <td>5.710388e-03</td>\n",
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       "</table>\n",
       "<p>85 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                    Feature name  Coefficients      P Values\n",
       "0                      Intercept     -1.374036           NaN\n",
       "1                        grade:A      1.123662  3.228237e-35\n",
       "2                        grade:B      0.878918  4.278551e-47\n",
       "3                        grade:C      0.684796  6.712386e-34\n",
       "4                        grade:D      0.496923  1.347044e-20\n",
       "..                           ...           ...           ...\n",
       "80   mths_since_last_record:3-20      0.440625  3.347130e-04\n",
       "81  mths_since_last_record:21-31      0.350710  1.832454e-03\n",
       "82  mths_since_last_record:32-80      0.502956  3.964444e-09\n",
       "83  mths_since_last_record:81-86      0.175839  8.604680e-02\n",
       "84    mths_since_last_record:>86      0.232707  5.710388e-03\n",
       "\n",
       "[85 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add P values\n",
    "p_values = reg2.p_values\n",
    "p_values = np.append(np.nan, np.array(p_values))\n",
    "summary_table['P Values'] = p_values\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(reg2, open('pd_model.sav', 'wb'))\n",
    "# Here we export our model to a 'SAV' file with file name 'pd_model.sav'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. PD Model Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Remove insignificant variables from test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  grade:G  \\\n",
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       "\n",
       "        home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  \\\n",
       "362514                                   0                   0   \n",
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       "\n",
       "        home_ownership:MORTGAGE  ...  mths_since_last_delinq:4-30  \\\n",
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       "\n",
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       "\n",
       "[93257 rows x 101 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat = loan_data_inputs_test.loc[:,cat_lst]\n",
    "inputs_test_with_ref_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
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       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "362514                   0                        1                 0   \n",
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       "\n",
       "        addr_state:NY  ...  mths_since_last_delinq:Missing  \\\n",
       "362514              0  ...                               1   \n",
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       "\n",
       "        mths_since_last_delinq:4-30  mths_since_last_delinq:31-56  \\\n",
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       "        mths_since_last_delinq:>=57  mths_since_last_record:Missing  \\\n",
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       "\n",
       "        mths_since_last_record:3-20  mths_since_last_record:21-31  \\\n",
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       "\n",
       "[93257 rows x 84 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test = inputs_test_with_ref_cat.drop(ref_lst, axis = 1)\n",
    "inputs_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Estimate the probability of default for each observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test = reg2.model.predict(inputs_test)\n",
    "# Calculates the predicted values for the dependent variable (targets)\n",
    "#  based on the values of the independent variables (inputs) supplied as an argument.\n",
    "y_hat_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07569437, 0.92430563],\n",
       "       [0.15076123, 0.84923877],\n",
       "       [0.11465081, 0.88534919],\n",
       "       ...,\n",
       "       [0.02678659, 0.97321341],\n",
       "       [0.04020859, 0.95979141],\n",
       "       [0.04763349, 0.95236651]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba = reg2.model.predict_proba(inputs_test)\n",
    "# Calculates the predicted probability values for the dependent variable (targets)\n",
    "# based on the values of the independent variables (inputs) supplied as an argument.\n",
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92430563, 0.84923877, 0.88534919, ..., 0.97321341, 0.95979141,\n",
       "       0.95236651])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_test_proba = y_hat_test_proba[: ][: , 1]\n",
    "# Only leave the 'good' probabilities in the array\n",
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_data_targets_test_temp = loan_data_targets_test.copy()\n",
    "loan_data_targets_test_temp.reset_index(drop = True, inplace = True)\n",
    "# Reset the index of a dataframe, as we need to concat the dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>263083</th>\n",
       "      <td>1</td>\n",
       "      <td>0.940636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165001</th>\n",
       "      <td>1</td>\n",
       "      <td>0.968665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_data_targets_test  y_hat_test_proba\n",
       "362514                       1          0.924306\n",
       "288564                       1          0.849239\n",
       "213591                       1          0.885349\n",
       "263083                       1          0.940636\n",
       "165001                       1          0.968665"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs = \\\n",
    "pd.concat([loan_data_targets_test_temp, pd.DataFrame(y_hat_test_proba)], axis = 1)\n",
    "# Concatenates two dataframes\n",
    "df_actual_predicted_probs.columns = \\\n",
    "['loan_data_targets_test', 'y_hat_test_proba']\n",
    "# Rename the columns\n",
    "df_actual_predicted_probs.index = loan_data_targets_test.index\n",
    "# Revert the index\n",
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "      <th>y_hat_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>362514</th>\n",
       "      <td>1</td>\n",
       "      <td>0.924306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288564</th>\n",
       "      <td>1</td>\n",
       "      <td>0.849239</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213591</th>\n",
       "      <td>1</td>\n",
       "      <td>0.885349</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263083</th>\n",
       "      <td>1</td>\n",
       "      <td>0.940636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165001</th>\n",
       "      <td>1</td>\n",
       "      <td>0.968665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1</td>\n",
       "      <td>0.880174</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296284</th>\n",
       "      <td>1</td>\n",
       "      <td>0.966433</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61777</th>\n",
       "      <td>0</td>\n",
       "      <td>0.973213</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91763</th>\n",
       "      <td>1</td>\n",
       "      <td>0.959791</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167512</th>\n",
       "      <td>1</td>\n",
       "      <td>0.952367</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93257 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "362514                       1          0.924306           1\n",
       "288564                       1          0.849239           0\n",
       "213591                       1          0.885349           0\n",
       "263083                       1          0.940636           1\n",
       "165001                       1          0.968665           1\n",
       "...                        ...               ...         ...\n",
       "115                          1          0.880174           0\n",
       "296284                       1          0.966433           1\n",
       "61777                        0          0.973213           1\n",
       "91763                        1          0.959791           1\n",
       "167512                       1          0.952367           1\n",
       "\n",
       "[93257 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr = 0.9\n",
    "df_actual_predicted_probs['y_hat_test'] = \\\n",
    "np.where(df_actual_predicted_probs['y_hat_test_proba'] >= tr, 1,0)\n",
    "df_actual_predicted_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7374</td>\n",
       "      <td>2816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35813</td>\n",
       "      <td>47254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted      0      1\n",
       "Actual                 \n",
       "0           7374   2816\n",
       "1          35813  47254"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix = pd.crosstab(df_actual_predicted_probs['loan_data_targets_test'],\n",
    "                               df_actual_predicted_probs['y_hat_test'],\n",
    "                               rownames = ['Actual'], colnames = ['Predicted'])\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.079072</td>\n",
       "      <td>0.030196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.384025</td>\n",
       "      <td>0.506707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted         0         1\n",
       "Actual                       \n",
       "0          0.079072  0.030196\n",
       "1          0.384025  0.506707"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix_per = confusion_matrix / df_actual_predicted_probs.shape[0]\n",
    "confusion_matrix_per"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5857790836076648"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_neg = confusion_matrix_per.iloc[0,0]\n",
    "true_pos = confusion_matrix_per.iloc[1,1]\n",
    "true_rate = true_neg + true_pos\n",
    "true_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 ROC Curve and AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.        , 0.        , ..., 0.99960746, 1.        ,\n",
       "        1.        ]),\n",
       " array([0.00000000e+00, 1.20384750e-05, 1.20384750e-04, ...,\n",
       "        9.99975923e-01, 9.99975923e-01, 1.00000000e+00]),\n",
       " array([1.99262869, 0.99262869, 0.99069779, ..., 0.4879087 , 0.39373676,\n",
       "        0.37528319]))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_roc = roc_curve(df_actual_predicted_probs['loan_data_targets_test'], \n",
    "                    df_actual_predicted_probs['y_hat_test_proba'])\n",
    "log_roc\n",
    "# Returns the Receiver Operating Characteristic (ROC) Curve from \n",
    "#  a set of actual values and their predicted probabilities\n",
    "# As a result, we get three arrays: the false positive rates, \n",
    "#  the true positive rates, and the thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr,tpr,thresholds = log_roc\n",
    "# Store each of the three arrays in a separate variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC curve')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr,tpr)\n",
    "plt.plot(tpr,tpr, linestyle = '--', color ='k')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.702208104993648"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUROC = roc_auc_score(df_actual_predicted_probs['loan_data_targets_test'], \n",
    "                      df_actual_predicted_probs['y_hat_test_proba'])\n",
    "AUROC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Gini and Kolmogorov-Smirnov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>index</th>\n",
       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "      <th>y_hat_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42341</td>\n",
       "      <td>1</td>\n",
       "      <td>0.375283</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42344</td>\n",
       "      <td>1</td>\n",
       "      <td>0.392101</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39810</td>\n",
       "      <td>0</td>\n",
       "      <td>0.393737</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40518</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.457734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93252</th>\n",
       "      <td>262480</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93253</th>\n",
       "      <td>231463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991304</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93254</th>\n",
       "      <td>239228</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991652</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93255</th>\n",
       "      <td>261086</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992058</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93256</th>\n",
       "      <td>242624</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992629</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93257 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  loan_data_targets_test  y_hat_test_proba  y_hat_test\n",
       "0       42341                       1          0.375283           0\n",
       "1       42344                       1          0.392101           0\n",
       "2       39810                       0          0.393737           0\n",
       "3       40518                       0          0.448968           0\n",
       "4       42396                       0          0.457734           0\n",
       "...       ...                     ...               ...         ...\n",
       "93252  262480                       1          0.991292           1\n",
       "93253  231463                       1          0.991304           1\n",
       "93254  239228                       1          0.991652           1\n",
       "93255  261086                       1          0.992058           1\n",
       "93256  242624                       1          0.992629           1\n",
       "\n",
       "[93257 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the table by test_prob values in ascending order\n",
    "df_actual_predicted_probs = \\\n",
    "df_actual_predicted_probs.sort_values('y_hat_test_proba')\n",
    "\n",
    "# Reset the index from 0\n",
    "df_actual_predicted_probs = df_actual_predicted_probs.reset_index()\n",
    "df_actual_predicted_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
       "      <th>y_hat_test</th>\n",
       "      <th>Cumulative N Population</th>\n",
       "      <th>Cumulative N Good</th>\n",
       "      <th>Cumulative N Bad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>40518</td>\n",
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       "      <td>2</td>\n",
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       "      <td>42396</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.991292</td>\n",
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       "      <td>83063</td>\n",
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       "      <th>93254</th>\n",
       "      <td>239228</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991652</td>\n",
       "      <td>1</td>\n",
       "      <td>93255</td>\n",
       "      <td>83065</td>\n",
       "      <td>10190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93255</th>\n",
       "      <td>261086</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992058</td>\n",
       "      <td>1</td>\n",
       "      <td>93256</td>\n",
       "      <td>83066</td>\n",
       "      <td>10190</td>\n",
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       "    <tr>\n",
       "      <th>93256</th>\n",
       "      <td>242624</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992629</td>\n",
       "      <td>1</td>\n",
       "      <td>93257</td>\n",
       "      <td>83067</td>\n",
       "      <td>10190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93257 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "0       42341                       1          0.375283           0   \n",
       "1       42344                       1          0.392101           0   \n",
       "2       39810                       0          0.393737           0   \n",
       "3       40518                       0          0.448968           0   \n",
       "4       42396                       0          0.457734           0   \n",
       "...       ...                     ...               ...         ...   \n",
       "93252  262480                       1          0.991292           1   \n",
       "93253  231463                       1          0.991304           1   \n",
       "93254  239228                       1          0.991652           1   \n",
       "93255  261086                       1          0.992058           1   \n",
       "93256  242624                       1          0.992629           1   \n",
       "\n",
       "       Cumulative N Population  Cumulative N Good  Cumulative N Bad  \n",
       "0                            1                  1                 0  \n",
       "1                            2                  2                 0  \n",
       "2                            3                  2                 1  \n",
       "3                            4                  2                 2  \n",
       "4                            5                  2                 3  \n",
       "...                        ...                ...               ...  \n",
       "93252                    93253              83063             10190  \n",
       "93253                    93254              83064             10190  \n",
       "93254                    93255              83065             10190  \n",
       "93255                    93256              83066             10190  \n",
       "93256                    93257              83067             10190  \n",
       "\n",
       "[93257 rows x 7 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs['Cumulative N Population'] = \\\n",
    "df_actual_predicted_probs.index + 1\n",
    "df_actual_predicted_probs['Cumulative N Good'] = \\\n",
    "df_actual_predicted_probs['loan_data_targets_test'].cumsum()\n",
    "df_actual_predicted_probs['Cumulative N Bad'] = \\\n",
    "df_actual_predicted_probs['Cumulative N Population'] - \\\n",
    "df_actual_predicted_probs['Cumulative N Good']\n",
    "df_actual_predicted_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>loan_data_targets_test</th>\n",
       "      <th>y_hat_test_proba</th>\n",
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       "      <th>Cumulative Perc Population</th>\n",
       "      <th>Cumulative Perc Good</th>\n",
       "      <th>Cumulative Perc Bad</th>\n",
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       "      <td>0.000032</td>\n",
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       "      <td>0.000098</td>\n",
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       "      <th>3</th>\n",
       "      <td>40518</td>\n",
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       "      <td>0.448968</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000196</td>\n",
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       "    <tr>\n",
       "      <th>93252</th>\n",
       "      <td>262480</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991292</td>\n",
       "      <td>1</td>\n",
       "      <td>93253</td>\n",
       "      <td>83063</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999957</td>\n",
       "      <td>0.999952</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>93253</th>\n",
       "      <td>231463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991304</td>\n",
       "      <td>1</td>\n",
       "      <td>93254</td>\n",
       "      <td>83064</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.999964</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>93254</th>\n",
       "      <td>239228</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991652</td>\n",
       "      <td>1</td>\n",
       "      <td>93255</td>\n",
       "      <td>83065</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>0.999976</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93255</th>\n",
       "      <td>261086</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992058</td>\n",
       "      <td>1</td>\n",
       "      <td>93256</td>\n",
       "      <td>83066</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.999988</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93256</th>\n",
       "      <td>242624</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992629</td>\n",
       "      <td>1</td>\n",
       "      <td>93257</td>\n",
       "      <td>83067</td>\n",
       "      <td>10190</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>93257 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "0       42341                       1          0.375283           0   \n",
       "1       42344                       1          0.392101           0   \n",
       "2       39810                       0          0.393737           0   \n",
       "3       40518                       0          0.448968           0   \n",
       "4       42396                       0          0.457734           0   \n",
       "...       ...                     ...               ...         ...   \n",
       "93252  262480                       1          0.991292           1   \n",
       "93253  231463                       1          0.991304           1   \n",
       "93254  239228                       1          0.991652           1   \n",
       "93255  261086                       1          0.992058           1   \n",
       "93256  242624                       1          0.992629           1   \n",
       "\n",
       "       Cumulative N Population  Cumulative N Good  Cumulative N Bad  \\\n",
       "0                            1                  1                 0   \n",
       "1                            2                  2                 0   \n",
       "2                            3                  2                 1   \n",
       "3                            4                  2                 2   \n",
       "4                            5                  2                 3   \n",
       "...                        ...                ...               ...   \n",
       "93252                    93253              83063             10190   \n",
       "93253                    93254              83064             10190   \n",
       "93254                    93255              83065             10190   \n",
       "93255                    93256              83066             10190   \n",
       "93256                    93257              83067             10190   \n",
       "\n",
       "       Cumulative Perc Population  Cumulative Perc Good  Cumulative Perc Bad  \n",
       "0                        0.000011              0.000012             0.000000  \n",
       "1                        0.000021              0.000024             0.000000  \n",
       "2                        0.000032              0.000024             0.000098  \n",
       "3                        0.000043              0.000024             0.000196  \n",
       "4                        0.000054              0.000024             0.000294  \n",
       "...                           ...                   ...                  ...  \n",
       "93252                    0.999957              0.999952             1.000000  \n",
       "93253                    0.999968              0.999964             1.000000  \n",
       "93254                    0.999979              0.999976             1.000000  \n",
       "93255                    0.999989              0.999988             1.000000  \n",
       "93256                    1.000000              1.000000             1.000000  \n",
       "\n",
       "[93257 rows x 10 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_predicted_probs['Cumulative Perc Population'] = \\\n",
    "df_actual_predicted_probs['Cumulative N Population'] / df_actual_predicted_probs.shape[0]\n",
    "df_actual_predicted_probs['Cumulative Perc Good'] = \\\n",
    "df_actual_predicted_probs['Cumulative N Good'] / df_actual_predicted_probs['loan_data_targets_test'].sum()\n",
    "df_actual_predicted_probs['Cumulative Perc Bad'] = \\\n",
    "df_actual_predicted_probs['Cumulative N Bad'] / (df_actual_predicted_probs.shape[0] - df_actual_predicted_probs['loan_data_targets_test'].sum())\n",
    "df_actual_predicted_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gini')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Gini\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Population'],\n",
    "         df_actual_predicted_probs['Cumulative Perc Bad'])\n",
    "plt.plot(df_actual_predicted_probs['Cumulative Perc Bad'],\n",
    "         df_actual_predicted_probs['Cumulative Perc Bad'],\n",
    "        linestyle = '--', color ='k')\n",
    "plt.xlabel('Cumulative % Population')\n",
    "plt.ylabel('Cumulative % Bad')\n",
    "plt.title('Gini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4044162099872961"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gini = AUROC * 2 - 1\n",
    "Gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Kolmogorov-Smirnov')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot KS\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'],\n",
    "         df_actual_predicted_probs['Cumulative Perc Bad'])\n",
    "plt.plot(df_actual_predicted_probs['y_hat_test_proba'],\n",
    "         df_actual_predicted_probs['Cumulative Perc Good'],\n",
    "        linestyle = '--', color ='k')\n",
    "plt.xlabel('Estimated Probability for being Good')\n",
    "plt.ylabel('Cumulative %')\n",
    "plt.title('Kolmogorov-Smirnov')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2966746932223847"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KS = max(df_actual_predicted_probs['Cumulative Perc Bad'] - df_actual_predicted_probs['Cumulative Perc Good'])\n",
    "KS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. PD Model and Scorecard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Join the tables and prepare the needed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123662</td>\n",
       "      <td>3.228237e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "      <td>4.278551e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "      <td>6.712386e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>mths_since_last_record:3-20</td>\n",
       "      <td>0.440625</td>\n",
       "      <td>3.347130e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>mths_since_last_record:21-31</td>\n",
       "      <td>0.350710</td>\n",
       "      <td>1.832454e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>mths_since_last_record:32-80</td>\n",
       "      <td>0.502956</td>\n",
       "      <td>3.964444e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>mths_since_last_record:81-86</td>\n",
       "      <td>0.175839</td>\n",
       "      <td>8.604680e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>mths_since_last_record:&gt;86</td>\n",
       "      <td>0.232707</td>\n",
       "      <td>5.710388e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Feature name  Coefficients      P Values\n",
       "0                      Intercept     -1.374036           NaN\n",
       "1                        grade:A      1.123662  3.228237e-35\n",
       "2                        grade:B      0.878918  4.278551e-47\n",
       "3                        grade:C      0.684796  6.712386e-34\n",
       "4                        grade:D      0.496923  1.347044e-20\n",
       "..                           ...           ...           ...\n",
       "80   mths_since_last_record:3-20      0.440625  3.347130e-04\n",
       "81  mths_since_last_record:21-31      0.350710  1.832454e-03\n",
       "82  mths_since_last_record:32-80      0.502956  3.964444e-09\n",
       "83  mths_since_last_record:81-86      0.175839  8.604680e-02\n",
       "84    mths_since_last_record:>86      0.232707  5.710388e-03\n",
       "\n",
       "[85 rows x 3 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['grade:G',\n",
       " 'home_ownership:RENT_OTHER_NONE_ANY',\n",
       " 'addr_state:ND_NE_IA_NV_FL_HI_AL',\n",
       " 'verification_status:Verified',\n",
       " 'purpose:educ__sm_b__wedd__ren_en__mov__house',\n",
       " 'initial_list_status:f',\n",
       " 'term:60',\n",
       " 'emp_length:0',\n",
       " 'mths_since_issue_d:>84',\n",
       " 'int_rate:>20.281',\n",
       " 'mths_since_earliest_cr_line:<140',\n",
       " 'inq_last_6mths:>6',\n",
       " 'acc_now_delinq:0',\n",
       " 'annual_inc:<20K',\n",
       " 'dti:>35',\n",
       " 'mths_since_last_delinq:0-3',\n",
       " 'mths_since_last_record:0-2']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>grade:G</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>home_ownership:RENT_OTHER_NONE_ANY</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>addr_state:ND_NE_IA_NV_FL_HI_AL</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>verification_status:Verified</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purpose:educ__sm_b__wedd__ren_en__mov__house</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>initial_list_status:f</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>term:60</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>emp_length:0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mths_since_issue_d:&gt;84</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>int_rate:&gt;20.281</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mths_since_earliest_cr_line:&lt;140</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>inq_last_6mths:&gt;6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Feature name  Coefficients  P Values\n",
       "0                                        grade:G             0       NaN\n",
       "1             home_ownership:RENT_OTHER_NONE_ANY             0       NaN\n",
       "2                addr_state:ND_NE_IA_NV_FL_HI_AL             0       NaN\n",
       "3                   verification_status:Verified             0       NaN\n",
       "4   purpose:educ__sm_b__wedd__ren_en__mov__house             0       NaN\n",
       "5                          initial_list_status:f             0       NaN\n",
       "6                                        term:60             0       NaN\n",
       "7                                   emp_length:0             0       NaN\n",
       "8                         mths_since_issue_d:>84             0       NaN\n",
       "9                               int_rate:>20.281             0       NaN\n",
       "10              mths_since_earliest_cr_line:<140             0       NaN\n",
       "11                             inq_last_6mths:>6             0       NaN\n",
       "12                              acc_now_delinq:0             0       NaN\n",
       "13                               annual_inc:<20K             0       NaN\n",
       "14                                       dti:>35             0       NaN\n",
       "15                    mths_since_last_delinq:0-3             0       NaN\n",
       "16                    mths_since_last_record:0-2             0       NaN"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ref_cat = pd.DataFrame(columns = ['Feature name'], data = ref_lst)\n",
    "df_ref_cat['Coefficients'] = 0\n",
    "df_ref_cat['P Values'] = np.nan\n",
    "df_ref_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123662</td>\n",
       "      <td>3.228237e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "      <td>4.278551e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "      <td>6.712386e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Feature name  Coefficients      P Values\n",
       "0                     Intercept     -1.374036           NaN\n",
       "1                       grade:A      1.123662  3.228237e-35\n",
       "2                       grade:B      0.878918  4.278551e-47\n",
       "3                       grade:C      0.684796  6.712386e-34\n",
       "4                       grade:D      0.496923  1.347044e-20\n",
       "..                          ...           ...           ...\n",
       "97             acc_now_delinq:0      0.000000           NaN\n",
       "98              annual_inc:<20K      0.000000           NaN\n",
       "99                      dti:>35      0.000000           NaN\n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN\n",
       "101  mths_since_last_record:0-2      0.000000           NaN\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat the ref cats with summary_table\n",
    "df_scorecard = pd.concat([summary_table, df_ref_cat])\n",
    "df_scorecard = df_scorecard.reset_index()\n",
    "df_scorecard.drop(['index'], axis= 1, inplace = True)\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "      <th>Original feature name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123662</td>\n",
       "      <td>3.228237e-35</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "      <td>4.278551e-47</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "      <td>6.712386e-34</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acc_now_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  \n",
       "0                 Intercept  \n",
       "1                     grade  \n",
       "2                     grade  \n",
       "3                     grade  \n",
       "4                     grade  \n",
       "..                      ...  \n",
       "97           acc_now_delinq  \n",
       "98               annual_inc  \n",
       "99                      dti  \n",
       "100  mths_since_last_delinq  \n",
       "101  mths_since_last_record  \n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract original feature name\n",
    "df_scorecard['Original feature name'] = \\\n",
    "df_scorecard['Feature name'].str.split(':').str[0]\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Build grouped tables and calculate the SUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the coefficients by feature name\n",
    "df_scorecard_group_min = \\\n",
    "df_scorecard.groupby('Original feature name')['Coefficients'].min()\n",
    "df_scorecard_group_max = \\\n",
    "df_scorecard.groupby('Original feature name')['Coefficients'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Original feature name\n",
       "Intercept                     -1.374036\n",
       "acc_now_delinq                 0.000000\n",
       "addr_state                     0.000000\n",
       "annual_inc                    -0.081518\n",
       "dti                            0.000000\n",
       "emp_length                     0.000000\n",
       "grade                          0.000000\n",
       "home_ownership                 0.000000\n",
       "initial_list_status            0.000000\n",
       "inq_last_6mths                 0.000000\n",
       "int_rate                       0.000000\n",
       "mths_since_earliest_cr_line    0.000000\n",
       "mths_since_issue_d            -0.071788\n",
       "mths_since_last_delinq         0.000000\n",
       "mths_since_last_record         0.000000\n",
       "purpose                        0.000000\n",
       "term                           0.000000\n",
       "verification_status           -0.011185\n",
       "Name: Coefficients, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard_group_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Original feature name\n",
       "Intercept                     -1.374036\n",
       "acc_now_delinq                 0.180365\n",
       "addr_state                     0.521965\n",
       "annual_inc                     0.552377\n",
       "dti                            0.384450\n",
       "emp_length                     0.125853\n",
       "grade                          1.123662\n",
       "home_ownership                 0.106249\n",
       "initial_list_status            0.053827\n",
       "inq_last_6mths                 0.666267\n",
       "int_rate                       0.883147\n",
       "mths_since_earliest_cr_line    0.129361\n",
       "mths_since_issue_d             1.084206\n",
       "mths_since_last_delinq         0.183094\n",
       "mths_since_last_record         0.502956\n",
       "purpose                        0.301856\n",
       "term                           0.078943\n",
       "verification_status            0.085719\n",
       "Name: Coefficients, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard_group_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.5385271799584626"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_sum_coef = df_scorecard_group_min.sum()\n",
    "min_sum_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.590260910298645"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sum_coef = df_scorecard_group_max.sum()\n",
    "max_sum_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_score = 300\n",
    "max_score = 850"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Calculate the scores for each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "      <th>Original feature name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123662</td>\n",
       "      <td>3.228237e-35</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "      <td>4.278551e-47</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "      <td>6.712386e-34</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
       "      <td>grade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acc_now_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>annual_inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  \n",
       "0                 Intercept  \n",
       "1                     grade  \n",
       "2                     grade  \n",
       "3                     grade  \n",
       "4                     grade  \n",
       "..                      ...  \n",
       "97           acc_now_delinq  \n",
       "98               annual_inc  \n",
       "99                      dti  \n",
       "100  mths_since_last_delinq  \n",
       "101  mths_since_last_record  \n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000000</td>\n",
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       "      <th>98</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
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       "      <th>100</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>mths_since_last_record</td>\n",
       "      <td>0.000000</td>\n",
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       "<p>102 rows × 5 columns</p>\n",
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      "text/plain": [
       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  Score - Calculation  \n",
       "0                 Intercept          -106.009580  \n",
       "1                     grade            86.692717  \n",
       "2                     grade            67.810287  \n",
       "3                     grade            52.833390  \n",
       "4                     grade            38.338564  \n",
       "..                      ...                  ...  \n",
       "97           acc_now_delinq             0.000000  \n",
       "98               annual_inc             0.000000  \n",
       "99                      dti             0.000000  \n",
       "100  mths_since_last_delinq             0.000000  \n",
       "101  mths_since_last_record             0.000000  \n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard['Score - Calculation'] = \\\n",
    "df_scorecard['Coefficients']*(max_score - min_score)/(max_sum_coef - min_sum_coef)\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-77-c2693a138a16>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_scorecard['Score - Calculation'][0] = \\\n"
     ]
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       "      <td>mths_since_last_record:0-2</td>\n",
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       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  Score - Calculation  \n",
       "0                 Intercept           312.690813  \n",
       "1                     grade            86.692717  \n",
       "2                     grade            67.810287  \n",
       "3                     grade            52.833390  \n",
       "4                     grade            38.338564  \n",
       "..                      ...                  ...  \n",
       "97           acc_now_delinq             0.000000  \n",
       "98               annual_inc             0.000000  \n",
       "99                      dti             0.000000  \n",
       "100  mths_since_last_delinq             0.000000  \n",
       "101  mths_since_last_record             0.000000  \n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the intercept\n",
    "df_scorecard['Score - Calculation'][0] = \\\n",
    "(max_score - min_score)/(max_sum_coef - min_sum_coef) * \\\n",
    "(df_scorecard['Coefficients'][0] - min_sum_coef) + min_score\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  Score - Calculation  Score - Preliminary  \n",
       "0                 Intercept           312.690813                313.0  \n",
       "1                     grade            86.692717                 87.0  \n",
       "2                     grade            67.810287                 68.0  \n",
       "3                     grade            52.833390                 53.0  \n",
       "4                     grade            38.338564                 38.0  \n",
       "..                      ...                  ...                  ...  \n",
       "97           acc_now_delinq             0.000000                  0.0  \n",
       "98               annual_inc             0.000000                  0.0  \n",
       "99                      dti             0.000000                  0.0  \n",
       "100  mths_since_last_delinq             0.000000                  0.0  \n",
       "101  mths_since_last_record             0.000000                  0.0  \n",
       "\n",
       "[102 rows x 6 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Round the scores\n",
    "df_scorecard['Score - Preliminary'] = df_scorecard['Score - Calculation'].round()\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the sum of the scores would be correct\n",
    "min_sum_score_prel = \\\n",
    "df_scorecard.groupby('Original feature name')['Score - Preliminary'].min().sum()\n",
    "min_sum_score_prel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "851.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sum_score_prel = \\\n",
    "df_scorecard.groupby('Original feature name')['Score - Preliminary'].max().sum()\n",
    "max_sum_score_prel\n",
    "# Have to subtract the 1 extra point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>P Values</th>\n",
       "      <th>Original feature name</th>\n",
       "      <th>Score - Calculation</th>\n",
       "      <th>Score - Preliminary</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.374036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intercept</td>\n",
       "      <td>312.690813</td>\n",
       "      <td>313.0</td>\n",
       "      <td>0.309187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
       "      <td>1.123662</td>\n",
       "      <td>3.228237e-35</td>\n",
       "      <td>grade</td>\n",
       "      <td>86.692717</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.307283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "      <td>4.278551e-47</td>\n",
       "      <td>grade</td>\n",
       "      <td>67.810287</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.189713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "      <td>6.712386e-34</td>\n",
       "      <td>grade</td>\n",
       "      <td>52.833390</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.166610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
       "      <td>grade</td>\n",
       "      <td>38.338564</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.338564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acc_now_delinq</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dti</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  Score - Calculation  Score - Preliminary  \\\n",
       "0                 Intercept           312.690813                313.0   \n",
       "1                     grade            86.692717                 87.0   \n",
       "2                     grade            67.810287                 68.0   \n",
       "3                     grade            52.833390                 53.0   \n",
       "4                     grade            38.338564                 38.0   \n",
       "..                      ...                  ...                  ...   \n",
       "97           acc_now_delinq             0.000000                  0.0   \n",
       "98               annual_inc             0.000000                  0.0   \n",
       "99                      dti             0.000000                  0.0   \n",
       "100  mths_since_last_delinq             0.000000                  0.0   \n",
       "101  mths_since_last_record             0.000000                  0.0   \n",
       "\n",
       "     Difference  \n",
       "0      0.309187  \n",
       "1      0.307283  \n",
       "2      0.189713  \n",
       "3      0.166610  \n",
       "4     -0.338564  \n",
       "..          ...  \n",
       "97     0.000000  \n",
       "98     0.000000  \n",
       "99     0.000000  \n",
       "100    0.000000  \n",
       "101    0.000000  \n",
       "\n",
       "[102 rows x 7 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard['Difference'] = \\\n",
    "df_scorecard['Score - Preliminary'] - df_scorecard['Score - Calculation']\n",
    "pd.options.display.max_rows = 30\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the max score among each parent category\n",
    "\n",
    "ind = list(df_scorecard.index) #Create an index list\n",
    "max_difference = [] #Create an empty set to store max difference for each cat\n",
    "for i in ind:\n",
    "    ori_name = df_scorecard['Original feature name'][i]\n",
    "    this_score = df_scorecard['Score - Preliminary'][i]\n",
    "    max_score_t = df_scorecard.loc[df_scorecard['Original feature name']\n",
    "                               == ori_name]['Score - Preliminary'].max()\n",
    "    this_diff = df_scorecard['Difference'][i]\n",
    "    \n",
    "    if this_score == max_score_t:\n",
    "        max_difference.append(this_diff)\n",
    "    else:\n",
    "        max_difference.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-83-af39ea40863c>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_scorecard['Max_Difference'][0] = 0\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade:A</td>\n",
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       "      <td>grade</td>\n",
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       "      <td>0.307283</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grade:B</td>\n",
       "      <td>0.878918</td>\n",
       "      <td>4.278551e-47</td>\n",
       "      <td>grade</td>\n",
       "      <td>67.810287</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.189713</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grade:C</td>\n",
       "      <td>0.684796</td>\n",
       "      <td>6.712386e-34</td>\n",
       "      <td>grade</td>\n",
       "      <td>52.833390</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.166610</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
       "      <td>grade</td>\n",
       "      <td>38.338564</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.338564</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>97</th>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acc_now_delinq</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dti</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mths_since_last_record:0-2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_record</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  Score - Calculation  Score - Preliminary  \\\n",
       "0                 Intercept           312.690813                313.0   \n",
       "1                     grade            86.692717                 87.0   \n",
       "2                     grade            67.810287                 68.0   \n",
       "3                     grade            52.833390                 53.0   \n",
       "4                     grade            38.338564                 38.0   \n",
       "..                      ...                  ...                  ...   \n",
       "97           acc_now_delinq             0.000000                  0.0   \n",
       "98               annual_inc             0.000000                  0.0   \n",
       "99                      dti             0.000000                  0.0   \n",
       "100  mths_since_last_delinq             0.000000                  0.0   \n",
       "101  mths_since_last_record             0.000000                  0.0   \n",
       "\n",
       "     Difference  Max_Difference  \n",
       "0      0.309187        0.000000  \n",
       "1      0.307283        0.307283  \n",
       "2      0.189713        0.000000  \n",
       "3      0.166610        0.000000  \n",
       "4     -0.338564        0.000000  \n",
       "..          ...             ...  \n",
       "97     0.000000        0.000000  \n",
       "98     0.000000        0.000000  \n",
       "99     0.000000        0.000000  \n",
       "100    0.000000        0.000000  \n",
       "101    0.000000        0.000000  \n",
       "\n",
       "[102 rows x 8 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard['Max_Difference'] = max_difference\n",
    "df_scorecard['Max_Difference'][0] = 0\n",
    "#pd.options.display.max_rows = 30\n",
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-84-23964fa0d0f5>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_scorecard['Score - Final'][max_diff_ind] = \\\n"
     ]
    }
   ],
   "source": [
    "# Get the item rounded the most among high-score categories\n",
    "max_diff_ind = df_scorecard['Max_Difference'].idxmax()\n",
    "df_scorecard['Score - Final'] = df_scorecard['Score - Preliminary']\n",
    "df_scorecard['Score - Final'][max_diff_ind] = \\\n",
    "df_scorecard['Score - Final'][max_diff_ind] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sum_score_prel = \\\n",
    "df_scorecard.groupby('Original feature name')['Score - Final'].max().sum()\n",
    "max_sum_score_prel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_sum_score_prel = \\\n",
    "df_scorecard.groupby('Original feature name')['Score - Final'].min().sum()\n",
    "min_sum_score_prel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Calculate credit scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  grade:G  \\\n",
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       "\n",
       "        home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  \\\n",
       "362514                                   0                   0   \n",
       "288564                                   0                   0   \n",
       "213591                                   0                   0   \n",
       "263083                                   0                   0   \n",
       "165001                                   0                   0   \n",
       "\n",
       "        home_ownership:MORTGAGE  ...  mths_since_last_delinq:4-30  \\\n",
       "362514                        1  ...                            0   \n",
       "288564                        1  ...                            0   \n",
       "213591                        1  ...                            0   \n",
       "263083                        1  ...                            0   \n",
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       "\n",
       "        mths_since_last_delinq:31-56  mths_since_last_delinq:>=57  \\\n",
       "362514                             0                            0   \n",
       "288564                             0                            0   \n",
       "213591                             1                            0   \n",
       "263083                             0                            0   \n",
       "165001                             1                            0   \n",
       "\n",
       "        mths_since_last_record:Missing  mths_since_last_record:0-2  \\\n",
       "362514                               1                           0   \n",
       "288564                               1                           0   \n",
       "213591                               1                           0   \n",
       "263083                               1                           0   \n",
       "165001                               1                           0   \n",
       "\n",
       "        mths_since_last_record:3-20  mths_since_last_record:21-31  \\\n",
       "362514                            0                             0   \n",
       "288564                            0                             0   \n",
       "213591                            0                             0   \n",
       "263083                            0                             0   \n",
       "165001                            0                             0   \n",
       "\n",
       "        mths_since_last_record:32-80  mths_since_last_record:81-86  \\\n",
       "362514                             0                             0   \n",
       "288564                             0                             0   \n",
       "213591                             0                             0   \n",
       "263083                             0                             0   \n",
       "165001                             0                             0   \n",
       "\n",
       "        mths_since_last_record:>86  \n",
       "362514                           0  \n",
       "288564                           0  \n",
       "213591                           0  \n",
       "263083                           0  \n",
       "165001                           0  \n",
       "\n",
       "[5 rows x 101 columns]"
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     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Score - Final</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>grade</td>\n",
       "      <td>52.833390</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.166610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grade:D</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>1.347044e-20</td>\n",
       "      <td>grade</td>\n",
       "      <td>38.338564</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.338564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.0</td>\n",
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       "      <th>97</th>\n",
       "      <td>acc_now_delinq:0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acc_now_delinq</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>annual_inc:&lt;20K</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>dti:&gt;35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dti</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mths_since_last_delinq:0-3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "                   Feature name  Coefficients      P Values  \\\n",
       "0                     Intercept     -1.374036           NaN   \n",
       "1                       grade:A      1.123662  3.228237e-35   \n",
       "2                       grade:B      0.878918  4.278551e-47   \n",
       "3                       grade:C      0.684796  6.712386e-34   \n",
       "4                       grade:D      0.496923  1.347044e-20   \n",
       "..                          ...           ...           ...   \n",
       "97             acc_now_delinq:0      0.000000           NaN   \n",
       "98              annual_inc:<20K      0.000000           NaN   \n",
       "99                      dti:>35      0.000000           NaN   \n",
       "100  mths_since_last_delinq:0-3      0.000000           NaN   \n",
       "101  mths_since_last_record:0-2      0.000000           NaN   \n",
       "\n",
       "      Original feature name  Score - Calculation  Score - Preliminary  \\\n",
       "0                 Intercept           312.690813                313.0   \n",
       "1                     grade            86.692717                 87.0   \n",
       "2                     grade            67.810287                 68.0   \n",
       "3                     grade            52.833390                 53.0   \n",
       "4                     grade            38.338564                 38.0   \n",
       "..                      ...                  ...                  ...   \n",
       "97           acc_now_delinq             0.000000                  0.0   \n",
       "98               annual_inc             0.000000                  0.0   \n",
       "99                      dti             0.000000                  0.0   \n",
       "100  mths_since_last_delinq             0.000000                  0.0   \n",
       "101  mths_since_last_record             0.000000                  0.0   \n",
       "\n",
       "     Difference  Max_Difference  Score - Final  \n",
       "0      0.309187        0.000000          313.0  \n",
       "1      0.307283        0.307283           87.0  \n",
       "2      0.189713        0.000000           68.0  \n",
       "3      0.166610        0.000000           53.0  \n",
       "4     -0.338564        0.000000           38.0  \n",
       "..          ...             ...            ...  \n",
       "97     0.000000        0.000000            0.0  \n",
       "98     0.000000        0.000000            0.0  \n",
       "99     0.000000        0.000000            0.0  \n",
       "100    0.000000        0.000000            0.0  \n",
       "101    0.000000        0.000000            0.0  \n",
       "\n",
       "[102 rows x 9 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_test_with_ref_cat_w_intercept = inputs_test_with_ref_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>167512</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93257 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Intercept  grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "362514          1        0        0        1        0        0        0   \n",
       "288564          1        0        0        0        0        1        0   \n",
       "213591          1        0        0        1        0        0        0   \n",
       "263083          1        0        0        1        0        0        0   \n",
       "165001          1        1        0        0        0        0        0   \n",
       "...           ...      ...      ...      ...      ...      ...      ...   \n",
       "115             1        0        1        0        0        0        0   \n",
       "296284          1        0        1        0        0        0        0   \n",
       "61777           1        1        0        0        0        0        0   \n",
       "91763           1        1        0        0        0        0        0   \n",
       "167512          1        1        0        0        0        0        0   \n",
       "\n",
       "        grade:G  home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  ...  \\\n",
       "362514        0                                   0                   0  ...   \n",
       "288564        0                                   0                   0  ...   \n",
       "213591        0                                   0                   0  ...   \n",
       "263083        0                                   0                   0  ...   \n",
       "165001        0                                   0                   0  ...   \n",
       "...         ...                                 ...                 ...  ...   \n",
       "115           0                                   0                   0  ...   \n",
       "296284        0                                   1                   0  ...   \n",
       "61777         0                                   0                   0  ...   \n",
       "91763         0                                   0                   0  ...   \n",
       "167512        0                                   1                   0  ...   \n",
       "\n",
       "        mths_since_last_delinq:4-30  mths_since_last_delinq:31-56  \\\n",
       "362514                            0                             0   \n",
       "288564                            0                             0   \n",
       "213591                            0                             1   \n",
       "263083                            0                             0   \n",
       "165001                            0                             1   \n",
       "...                             ...                           ...   \n",
       "115                               0                             0   \n",
       "296284                            0                             0   \n",
       "61777                             0                             0   \n",
       "91763                             0                             0   \n",
       "167512                            0                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:Missing  \\\n",
       "362514                            0                               1   \n",
       "288564                            0                               1   \n",
       "213591                            0                               1   \n",
       "263083                            0                               1   \n",
       "165001                            0                               1   \n",
       "...                             ...                             ...   \n",
       "115                               0                               1   \n",
       "296284                            1                               1   \n",
       "61777                             0                               1   \n",
       "91763                             0                               1   \n",
       "167512                            0                               1   \n",
       "\n",
       "        mths_since_last_record:0-2  mths_since_last_record:3-20  \\\n",
       "362514                           0                            0   \n",
       "288564                           0                            0   \n",
       "213591                           0                            0   \n",
       "263083                           0                            0   \n",
       "165001                           0                            0   \n",
       "...                            ...                          ...   \n",
       "115                              0                            0   \n",
       "296284                           0                            0   \n",
       "61777                            0                            0   \n",
       "91763                            0                            0   \n",
       "167512                           0                            0   \n",
       "\n",
       "        mths_since_last_record:21-31  mths_since_last_record:32-80  \\\n",
       "362514                             0                             0   \n",
       "288564                             0                             0   \n",
       "213591                             0                             0   \n",
       "263083                             0                             0   \n",
       "165001                             0                             0   \n",
       "...                              ...                           ...   \n",
       "115                                0                             0   \n",
       "296284                             0                             0   \n",
       "61777                              0                             0   \n",
       "91763                              0                             0   \n",
       "167512                             0                             0   \n",
       "\n",
       "        mths_since_last_record:81-86  mths_since_last_record:>86  \n",
       "362514                             0                           0  \n",
       "288564                             0                           0  \n",
       "213591                             0                           0  \n",
       "263083                             0                           0  \n",
       "165001                             0                           0  \n",
       "...                              ...                         ...  \n",
       "115                                0                           0  \n",
       "296284                             0                           0  \n",
       "61777                              0                           0  \n",
       "91763                              0                           0  \n",
       "167512                             0                           0  \n",
       "\n",
       "[93257 rows x 102 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a 'intercept' column\n",
    "inputs_test_with_ref_cat_w_intercept.insert(0, 'Intercept', 1)\n",
    "inputs_test_with_ref_cat_w_intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Intercept</th>\n",
       "      <th>grade:A</th>\n",
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       "      <th>inq_last_6mths:&gt;6</th>\n",
       "      <th>acc_now_delinq:0</th>\n",
       "      <th>annual_inc:&lt;20K</th>\n",
       "      <th>dti:&gt;35</th>\n",
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       "        Intercept  grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
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       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  ...  \\\n",
       "362514                   0                        1                 0  ...   \n",
       "288564                   0                        1                 0  ...   \n",
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       "167512                   0                        0                 0  ...   \n",
       "\n",
       "        emp_length:0  mths_since_issue_d:>84  int_rate:>20.281  \\\n",
       "362514             1                       0                 0   \n",
       "288564             0                       0                 1   \n",
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       "\n",
       "        mths_since_earliest_cr_line:<140  inq_last_6mths:>6  acc_now_delinq:0  \\\n",
       "362514                                 0                  0                 1   \n",
       "288564                                 0                  0                 1   \n",
       "213591                                 0                  0                 1   \n",
       "263083                                 1                  0                 1   \n",
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       "...                                  ...                ...               ...   \n",
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       "61777                                  0                  0                 1   \n",
       "91763                                  0                  0                 1   \n",
       "167512                                 0                  0                 1   \n",
       "\n",
       "        annual_inc:<20K  dti:>35  mths_since_last_delinq:0-3  \\\n",
       "362514                0        0                           0   \n",
       "288564                0        0                           1   \n",
       "213591                0        0                           0   \n",
       "263083                0        0                           0   \n",
       "165001                0        0                           0   \n",
       "...                 ...      ...                         ...   \n",
       "115                   0        0                           0   \n",
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       "61777                 0        0                           0   \n",
       "91763                 0        0                           0   \n",
       "167512                0        0                           0   \n",
       "\n",
       "        mths_since_last_record:0-2  \n",
       "362514                           0  \n",
       "288564                           0  \n",
       "213591                           0  \n",
       "263083                           0  \n",
       "165001                           0  \n",
       "...                            ...  \n",
       "115                              0  \n",
       "296284                           0  \n",
       "61777                            0  \n",
       "91763                            0  \n",
       "167512                           0  \n",
       "\n",
       "[93257 rows x 102 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make the column order match the row orde of the scorecard df\n",
    "inputs_test_with_ref_cat_w_intercept = \\\n",
    "inputs_test_with_ref_cat_w_intercept[df_scorecard['Feature name'].values]\n",
    "inputs_test_with_ref_cat_w_intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102,)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorecard_scores = df_scorecard['Score - Final']\n",
    "scorecard_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93257, 102)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_test_with_ref_cat_w_intercept.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 1)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape the scorecard_scores\n",
    "scorecard_scores = scorecard_scores.values.reshape(scorecard_scores.shape[0],1)\n",
    "scorecard_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
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       "            0\n",
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       "\n",
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     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate credit scores all accounts\n",
    "y_scores = inputs_test_with_ref_cat_w_intercept.dot(scorecard_scores)\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5 From Credit Score to PD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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       "               0\n",
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       "...          ...\n",
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       "167512  2.997974\n",
       "\n",
       "[93257 rows x 1 columns]"
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     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert PD to sum of the coeffs\n",
    "sum_coef_from_score = \\\n",
    "((y_scores - min_score) / (max_score - min_score)) * \\\n",
    "(max_sum_coef - min_sum_coef) + min_sum_coef\n",
    "sum_coef_from_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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       "               0\n",
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   "source": [
    "y_hat_proba_from_score = \\\n",
    "np.exp(sum_coef_from_score) / (np.exp(sum_coef_from_score) + 1)\n",
    "y_hat_proba_from_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92430563, 0.84923877, 0.88534919, ..., 0.97321341, 0.95979141,\n",
       "       0.95236651])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.6 Setting Cut-offs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000021</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39810</td>\n",
       "      <td>0</td>\n",
       "      <td>0.393737</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40518</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448968</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.457734</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93252</th>\n",
       "      <td>262480</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991292</td>\n",
       "      <td>1</td>\n",
       "      <td>93253</td>\n",
       "      <td>83063</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999957</td>\n",
       "      <td>0.999952</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93253</th>\n",
       "      <td>231463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991304</td>\n",
       "      <td>1</td>\n",
       "      <td>93254</td>\n",
       "      <td>83064</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.999964</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93254</th>\n",
       "      <td>239228</td>\n",
       "      <td>1</td>\n",
       "      <td>0.991652</td>\n",
       "      <td>1</td>\n",
       "      <td>93255</td>\n",
       "      <td>83065</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>0.999976</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93255</th>\n",
       "      <td>261086</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992058</td>\n",
       "      <td>1</td>\n",
       "      <td>93256</td>\n",
       "      <td>83066</td>\n",
       "      <td>10190</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.999988</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93256</th>\n",
       "      <td>242624</td>\n",
       "      <td>1</td>\n",
       "      <td>0.992629</td>\n",
       "      <td>1</td>\n",
       "      <td>93257</td>\n",
       "      <td>83067</td>\n",
       "      <td>10190</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93257 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  loan_data_targets_test  y_hat_test_proba  y_hat_test  \\\n",
       "0       42341                       1          0.375283           0   \n",
       "1       42344                       1          0.392101           0   \n",
       "2       39810                       0          0.393737           0   \n",
       "3       40518                       0          0.448968           0   \n",
       "4       42396                       0          0.457734           0   \n",
       "...       ...                     ...               ...         ...   \n",
       "93252  262480                       1          0.991292           1   \n",
       "93253  231463                       1          0.991304           1   \n",
       "93254  239228                       1          0.991652           1   \n",
       "93255  261086                       1          0.992058           1   \n",
       "93256  242624                       1          0.992629           1   \n",
       "\n",
       "       Cumulative N Population  Cumulative N Good  Cumulative N Bad  \\\n",
       "0                            1                  1                 0   \n",
       "1                            2                  2                 0   \n",
       "2                            3                  2                 1   \n",
       "3                            4                  2                 2   \n",
       "4                            5                  2                 3   \n",
       "...                        ...                ...               ...   \n",
       "93252                    93253              83063             10190   \n",
       "93253                    93254              83064             10190   \n",
       "93254                    93255              83065             10190   \n",
       "93255                    93256              83066             10190   \n",
       "93256                    93257              83067             10190   \n",
       "\n",
       "       Cumulative Perc Population  Cumulative Perc Good  Cumulative Perc Bad  \n",
       "0                        0.000011              0.000012             0.000000  \n",
       "1                        0.000021              0.000024             0.000000  \n",
       "2                        0.000032              0.000024             0.000098  \n",
       "3                        0.000043              0.000024             0.000196  \n",
       "4                        0.000054              0.000024             0.000294  \n",
       "...                           ...                   ...                  ...  \n",
       "93252                    0.999957              0.999952             1.000000  \n",
       "93253                    0.999968              0.999964             1.000000  \n",
       "93254                    0.999979              0.999976             1.000000  \n",
       "93255                    0.999989              0.999988             1.000000  \n",
       "93256                    1.000000              1.000000             1.000000  \n",
       "\n",
       "[93257 rows x 10 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr = 0.9\n",
    "df_actual_predicted_probs['y_hat_test'] = \\\n",
    "np.where(df_actual_predicted_probs['y_hat_test_proba'] >= tr, 1,0)\n",
    "df_actual_predicted_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1.99262869, 0.99262869, 0.99069779, ..., 0.4879087 , 0.39373676,\n",
       "        0.37528319]),\n",
       " array([0.        , 0.        , 0.        , ..., 0.99960746, 1.        ,\n",
       "        1.        ]),\n",
       " array([0.00000000e+00, 1.20384750e-05, 1.20384750e-04, ...,\n",
       "        9.99975923e-01, 9.99975923e-01, 1.00000000e+00])]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[thresholds, fpr, tpr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "              0         0         0\n",
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       "17249  0.493407  0.999411  0.999964\n",
       "17250  0.488602  0.999607  0.999964\n",
       "17251  0.487909  0.999607  0.999976\n",
       "17252  0.393737  1.000000  0.999976\n",
       "17253  0.375283  1.000000  1.000000\n",
       "\n",
       "[17254 rows x 3 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs = \\\n",
    "pd.concat([pd.DataFrame(thresholds), pd.DataFrame(fpr), pd.DataFrame(tpr)], axis = 1)\n",
    "df_cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>17254 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "       thresholds       fpr       tpr\n",
       "0        1.992629  0.000000  0.000000\n",
       "1        0.992629  0.000000  0.000012\n",
       "2        0.990698  0.000000  0.000120\n",
       "3        0.990653  0.000098  0.000120\n",
       "4        0.989762  0.000098  0.000433\n",
       "...           ...       ...       ...\n",
       "17249    0.493407  0.999411  0.999964\n",
       "17250    0.488602  0.999607  0.999964\n",
       "17251    0.487909  0.999607  0.999976\n",
       "17252    0.393737  1.000000  0.999976\n",
       "17253    0.375283  1.000000  1.000000\n",
       "\n",
       "[17254 rows x 3 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs.columns = ['thresholds', 'fpr', 'tpr']\n",
    "df_cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>17251</th>\n",
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       "<p>17254 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "       thresholds       fpr       tpr\n",
       "0        1.000000  0.000000  0.000000\n",
       "1        0.992629  0.000000  0.000012\n",
       "2        0.990698  0.000000  0.000120\n",
       "3        0.990653  0.000098  0.000120\n",
       "4        0.989762  0.000098  0.000433\n",
       "...           ...       ...       ...\n",
       "17249    0.493407  0.999411  0.999964\n",
       "17250    0.488602  0.999607  0.999964\n",
       "17251    0.487909  0.999607  0.999976\n",
       "17252    0.393737  1.000000  0.999976\n",
       "17253    0.375283  1.000000  1.000000\n",
       "\n",
       "[17254 rows x 3 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs['thresholds'][0] = 1 - 1 / np.power(10, 16)\n",
    "# Let the first threshold (the value of the thresholds column with index 0) be \n",
    "#  equal to a number, very close to 1\n",
    "df_cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thresholds</th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "      <th>Score</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.992629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>797.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.990698</td>\n",
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       "      <td>0.000120</td>\n",
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       "      <td>0.999964</td>\n",
       "      <td>415.0</td>\n",
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       "    <tr>\n",
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       "<p>17254 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "       thresholds       fpr       tpr  Score\n",
       "0        1.000000  0.000000  0.000000  850.0\n",
       "1        0.992629  0.000000  0.000012  797.0\n",
       "2        0.990698  0.000000  0.000120  779.0\n",
       "3        0.990653  0.000098  0.000120  778.0\n",
       "4        0.989762  0.000098  0.000433  771.0\n",
       "...           ...       ...       ...    ...\n",
       "17249    0.493407  0.999411  0.999964  417.0\n",
       "17250    0.488602  0.999607  0.999964  415.0\n",
       "17251    0.487909  0.999607  0.999976  415.0\n",
       "17252    0.393737  1.000000  0.999976  385.0\n",
       "17253    0.375283  1.000000  1.000000  379.0\n",
       "\n",
       "[17254 rows x 4 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert the threshold to equivalent Credit Scores\n",
    "df_cutoffs['Score'] = \\\n",
    "((np.log(df_cutoffs['thresholds'] / (1 - df_cutoffs['thresholds'])) \\\n",
    "  - min_sum_coef) * ((max_score - min_score) \\\n",
    "                     / (max_sum_coef - min_sum_coef)) + min_score).round()\n",
    "df_cutoffs['Score'][0] = max_score\n",
    "df_cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function that assigns 1 if predicted probability is greater \n",
    "#  than the parameter p (threshold), and a value 0 if it is not\n",
    "# Then, it sums the column, returns the total number of rows greater than the p\n",
    "def n_approved(p):\n",
    "    return df_actual_predicted_probs.\\\n",
    "loc[df_actual_predicted_probs['y_hat_test_proba'] >= p]['index'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>thresholds</th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "      <th>Score</th>\n",
       "      <th>N Approved</th>\n",
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       "      <th>Rejection Rate</th>\n",
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       "      <td>0.000118</td>\n",
       "      <td>0.999882</td>\n",
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       "      <td>0.000433</td>\n",
       "      <td>771.0</td>\n",
       "      <td>37</td>\n",
       "      <td>93220</td>\n",
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       "      <td>93250</td>\n",
       "      <td>7</td>\n",
       "      <td>0.999925</td>\n",
       "      <td>0.000075</td>\n",
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       "    <tr>\n",
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       "      <td>0.487909</td>\n",
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       "      <td>0.999976</td>\n",
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       "<p>17254 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "       thresholds       fpr       tpr  Score  N Approved  N Rejected  \\\n",
       "0        1.000000  0.000000  0.000000  850.0           0       93257   \n",
       "1        0.992629  0.000000  0.000012  797.0           1       93256   \n",
       "2        0.990698  0.000000  0.000120  779.0          10       93247   \n",
       "3        0.990653  0.000098  0.000120  778.0          11       93246   \n",
       "4        0.989762  0.000098  0.000433  771.0          37       93220   \n",
       "...           ...       ...       ...    ...         ...         ...   \n",
       "17249    0.493407  0.999411  0.999964  417.0       93248           9   \n",
       "17250    0.488602  0.999607  0.999964  415.0       93250           7   \n",
       "17251    0.487909  0.999607  0.999976  415.0       93251           6   \n",
       "17252    0.393737  1.000000  0.999976  385.0       93255           2   \n",
       "17253    0.375283  1.000000  1.000000  379.0       93257           0   \n",
       "\n",
       "       Approval Rate  Rejection Rate  \n",
       "0           0.000000        1.000000  \n",
       "1           0.000011        0.999989  \n",
       "2           0.000107        0.999893  \n",
       "3           0.000118        0.999882  \n",
       "4           0.000397        0.999603  \n",
       "...              ...             ...  \n",
       "17249       0.999903        0.000097  \n",
       "17250       0.999925        0.000075  \n",
       "17251       0.999936        0.000064  \n",
       "17252       0.999979        0.000021  \n",
       "17253       1.000000        0.000000  \n",
       "\n",
       "[17254 rows x 8 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cutoffs['N Approved'] = df_cutoffs['thresholds'].apply(n_approved)\n",
    "df_cutoffs['N Rejected'] = \\\n",
    "df_actual_predicted_probs.shape[0] - df_cutoffs['N Approved']\n",
    "df_cutoffs['Approval Rate'] = \\\n",
    "df_cutoffs['N Approved'] / df_actual_predicted_probs.shape[0]\n",
    "df_cutoffs['Rejection Rate'] = 1 - df_cutoffs['Approval Rate']\n",
    "df_cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_train_with_ref_cat.to_csv('inputs_train_with_ref_cat.csv')\n",
    "df_scorecard.to_csv('df_scorecard.csv')"
   ]
  }
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