credit-log-modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Summary*\n",
    "\n",
    "In this notebook, the credit risk data was cleaned and explored to better understand the current credit risk situation. The data was then modeled to accurately predict the probability of loan defaults, facilitating more precise automation of loan approval and denial processes.\n",
    "\n",
    "The model achieved an accuracy of 86% in predicting loan defaults on a dataset of 32,576 loans and 12 benchmarks. With this model, the default rate is projected to decrease by 8%, minimizing risk for both lenders and applicants.\n",
    "\n",
    "By optimizing the probability threshold to 0.45, recall improved by 5%, from 48% to 53%, without impacting accuracy.\n",
    "\n",
    "Further optimization led to a recall improvement to 55% without compromising accuracy. We prioritize recall as it helps minimize false negatives, ensuring that potential defaulters are more accurately identified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# chck*\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection,linear_model, metrics\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 32581 rows and 12 features.\n",
      "person_age                      int64\n",
      "person_income                   int64\n",
      "person_home_ownership          object\n",
      "person_emp_length             float64\n",
      "loan_intent                    object\n",
      "loan_grade                     object\n",
      "loan_amnt                       int64\n",
      "loan_int_rate                 float64\n",
      "loan_status                     int64\n",
      "loan_percent_income           float64\n",
      "cb_person_default_on_file      object\n",
      "cb_person_cred_hist_length      int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_age</th>\n",
       "      <th>person_income</th>\n",
       "      <th>person_home_ownership</th>\n",
       "      <th>person_emp_length</th>\n",
       "      <th>loan_intent</th>\n",
       "      <th>loan_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_int_rate</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>loan_percent_income</th>\n",
       "      <th>cb_person_default_on_file</th>\n",
       "      <th>cb_person_cred_hist_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>59000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>123.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>D</td>\n",
       "      <td>35000</td>\n",
       "      <td>16.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.59</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>9600</td>\n",
       "      <td>OWN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>B</td>\n",
       "      <td>1000</td>\n",
       "      <td>11.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>9600</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>5500</td>\n",
       "      <td>12.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.57</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>65500</td>\n",
       "      <td>RENT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>15.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>54400</td>\n",
       "      <td>RENT</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>14.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32576</th>\n",
       "      <td>57</td>\n",
       "      <td>53000</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>C</td>\n",
       "      <td>5800</td>\n",
       "      <td>13.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>N</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32577</th>\n",
       "      <td>54</td>\n",
       "      <td>120000</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>A</td>\n",
       "      <td>17625</td>\n",
       "      <td>7.49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>N</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32578</th>\n",
       "      <td>65</td>\n",
       "      <td>76000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>HOMEIMPROVEMENT</td>\n",
       "      <td>B</td>\n",
       "      <td>35000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>N</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32579</th>\n",
       "      <td>56</td>\n",
       "      <td>150000</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>B</td>\n",
       "      <td>15000</td>\n",
       "      <td>11.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>N</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32580</th>\n",
       "      <td>66</td>\n",
       "      <td>42000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>B</td>\n",
       "      <td>6475</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>N</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32581 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
       "0              22          59000                  RENT              123.0   \n",
       "1              21           9600                   OWN                5.0   \n",
       "2              25           9600              MORTGAGE                1.0   \n",
       "3              23          65500                  RENT                4.0   \n",
       "4              24          54400                  RENT                8.0   \n",
       "...           ...            ...                   ...                ...   \n",
       "32576          57          53000              MORTGAGE                1.0   \n",
       "32577          54         120000              MORTGAGE                4.0   \n",
       "32578          65          76000                  RENT                3.0   \n",
       "32579          56         150000              MORTGAGE                5.0   \n",
       "32580          66          42000                  RENT                2.0   \n",
       "\n",
       "           loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
       "0             PERSONAL          D      35000          16.02            1   \n",
       "1            EDUCATION          B       1000          11.14            0   \n",
       "2              MEDICAL          C       5500          12.87            1   \n",
       "3              MEDICAL          C      35000          15.23            1   \n",
       "4              MEDICAL          C      35000          14.27            1   \n",
       "...                ...        ...        ...            ...          ...   \n",
       "32576         PERSONAL          C       5800          13.16            0   \n",
       "32577         PERSONAL          A      17625           7.49            0   \n",
       "32578  HOMEIMPROVEMENT          B      35000          10.99            1   \n",
       "32579         PERSONAL          B      15000          11.48            0   \n",
       "32580          MEDICAL          B       6475           9.99            0   \n",
       "\n",
       "       loan_percent_income cb_person_default_on_file  \\\n",
       "0                     0.59                         Y   \n",
       "1                     0.10                         N   \n",
       "2                     0.57                         N   \n",
       "3                     0.53                         N   \n",
       "4                     0.55                         Y   \n",
       "...                    ...                       ...   \n",
       "32576                 0.11                         N   \n",
       "32577                 0.15                         N   \n",
       "32578                 0.46                         N   \n",
       "32579                 0.10                         N   \n",
       "32580                 0.15                         N   \n",
       "\n",
       "       cb_person_cred_hist_length  \n",
       "0                               3  \n",
       "1                               2  \n",
       "2                               3  \n",
       "3                               2  \n",
       "4                               4  \n",
       "...                           ...  \n",
       "32576                          30  \n",
       "32577                          19  \n",
       "32578                          28  \n",
       "32579                          26  \n",
       "32580                          30  \n",
       "\n",
       "[32581 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_data = pd.read_csv(\"credit_risk_dataset.csv\")\n",
    "shape = cr_data.shape\n",
    "print(\"There are {} rows and {} features.\".format(shape[0], shape[1]))\n",
    "print(cr_data.dtypes)\n",
    "cr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "person_age                  0\n",
       "person_income               0\n",
       "person_home_ownership       0\n",
       "person_emp_length         895\n",
       "loan_intent                 0\n",
       "loan_grade                  0\n",
       "loan_amnt                   0\n",
       "loan_int_rate            3116\n",
       "loan_status                 0\n",
       "loan_percent_income         0\n",
       "default_hist                0\n",
       "cr_hist_len                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will shorten the last 2 feature names and address the null values\n",
    "cr_data = cr_data.rename(\n",
    "    columns = {\"cb_person_default_on_file\":\"default_hist\", \n",
    "               \"cb_person_cred_hist_length\": \"cr_hist_len\"})\n",
    "cr_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0956385623522912"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of null values from loan int rate col\n",
    "cr_data.loan_int_rate.isnull().sum() / cr_data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2 features which has null values. Since the  relative to our sample size, we will investigate their distributions and decide how to fill the NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Freq vs Interest Rate')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cr_data['person_emp_length'])\n",
    "plt.xlabel(\"Employment Length\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Freq vs Employment Length\")\n",
    "plt.show()\n",
    "\n",
    "plt.hist(cr_data['loan_int_rate'])\n",
    "plt.xlabel(\"Interest Rate\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Freq vs Interest Rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both features are not normally distributed. Therefore we will fill the NaNs with the median values for both the loan interest rate and employment length features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_len_null = cr_data[cr_data['person_emp_length'].isnull()].index\n",
    "int_rate_null = cr_data[cr_data['loan_int_rate'].isnull()].index\n",
    "\n",
    "cr_data['person_emp_length'].fillna((cr_data['person_emp_length'].median()), inplace=True)\n",
    "cr_data['loan_int_rate'].fillna((cr_data['loan_int_rate'].median()), inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's consider if there are outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Interest Rate vs Age')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check distribution of age and interest rate\n",
    "\n",
    "\n",
    "colors = [\"blue\",\"red\"]\n",
    "plt.scatter(cr_data['person_age'], cr_data['loan_int_rate'],\n",
    "            c = cr_data['loan_status'],\n",
    "            cmap = mpl.colors.ListedColormap(colors), alpha=0.5)\n",
    "plt.xlabel(\"Person Age\")\n",
    "plt.ylabel(\"Loan Interest Rate\")\n",
    "plt.title(\"Interest Rate vs Age\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are individuals who are above 120 years of age with loans and are unlikely to apply for new loans in the future. Therefore we will remove individuals who exceed 100 years of age. \n",
    "\n",
    "There is no outlier for loan interest rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_age</th>\n",
       "      <th>person_income</th>\n",
       "      <th>person_home_ownership</th>\n",
       "      <th>person_emp_length</th>\n",
       "      <th>loan_intent</th>\n",
       "      <th>loan_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_int_rate</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>loan_percent_income</th>\n",
       "      <th>default_hist</th>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>144</td>\n",
       "      <td>250000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>VENTURE</td>\n",
       "      <td>C</td>\n",
       "      <td>4800</td>\n",
       "      <td>13.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>183</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>B</td>\n",
       "      <td>6000</td>\n",
       "      <td>11.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>123</td>\n",
       "      <td>80004</td>\n",
       "      <td>RENT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>B</td>\n",
       "      <td>20400</td>\n",
       "      <td>10.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>123</td>\n",
       "      <td>78000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>7.0</td>\n",
       "      <td>VENTURE</td>\n",
       "      <td>B</td>\n",
       "      <td>20000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32297</th>\n",
       "      <td>144</td>\n",
       "      <td>6000000</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>12.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>C</td>\n",
       "      <td>5000</td>\n",
       "      <td>12.73</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>N</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
       "81            144         250000                  RENT                4.0   \n",
       "183           144         200000              MORTGAGE                4.0   \n",
       "575           123          80004                  RENT                2.0   \n",
       "747           123          78000                  RENT                7.0   \n",
       "32297         144        6000000              MORTGAGE               12.0   \n",
       "\n",
       "      loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
       "81        VENTURE          C       4800          13.57            0   \n",
       "183     EDUCATION          B       6000          11.86            0   \n",
       "575     EDUCATION          B      20400          10.25            0   \n",
       "747       VENTURE          B      20000          10.99            0   \n",
       "32297    PERSONAL          C       5000          12.73            0   \n",
       "\n",
       "       loan_percent_income default_hist  cr_hist_len  \n",
       "81                    0.02            N            3  \n",
       "183                   0.03            N            2  \n",
       "575                   0.25            N            3  \n",
       "747                   0.26            N            4  \n",
       "32297                 0.00            N           25  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean 1\n",
    "cr_clean1 = cr_data[cr_data['person_age']<=100]\n",
    "\n",
    "cr_data[cr_data['person_age']>100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entries shown above have been removed and created a cleaned dataset saved as `cr_clean1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>loan_grade</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
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       "    <tr>\n",
       "      <th>default_hist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>10777</td>\n",
       "      <td>10448</td>\n",
       "      <td>3200</td>\n",
       "      <td>1750</td>\n",
       "      <td>499</td>\n",
       "      <td>129</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3256</td>\n",
       "      <td>1876</td>\n",
       "      <td>465</td>\n",
       "      <td>112</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_grade        A      B     C     D    E    F   G\n",
       "default_hist                                        \n",
       "N             10777  10448  3200  1750  499  129  28\n",
       "Y                 0      0  3256  1876  465  112  36"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(cr_clean1['default_hist'], cr_clean1['loan_grade'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no surpise here as we see the lender focuses on issuing higher grade loans to clients with better credit history and less loans to those with worse credit history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>loan_status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_hist</th>\n",
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       "      <th>N</th>\n",
       "      <td>21895</td>\n",
       "      <td>4936</td>\n",
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       "      <th>Y</th>\n",
       "      <td>3573</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "loan_status       0     1\n",
       "default_hist             \n",
       "N             21895  4936\n",
       "Y              3573  2172"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note 0 is non default and 1 is default\n",
    "default_hist_status_tab = pd.crosstab(cr_clean1['default_hist'], cr_clean1['loan_status'])\n",
    "default_hist_status_tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Despite the measures taken, 18.4% of clients defaulted for the first time.\n",
      "And 37.81% of clients who had previously defaulted, defaulted again.\n"
     ]
    }
   ],
   "source": [
    "total1 = default_hist_status_tab.iloc[0].sum()\n",
    "defaulted1 = default_hist_status_tab.iloc[0,1]\n",
    "\n",
    "total2 = default_hist_status_tab.iloc[1].sum()\n",
    "defaulted2 = default_hist_status_tab.iloc[1,1]\n",
    "\n",
    "first_default = round(defaulted1 / total1 * 100, 2)\n",
    "second_default = round(defaulted2 / total2 * 100, 2)\n",
    "\n",
    "print(\"Despite the measures taken, {}% of clients defaulted for the first time.\".format(first_default))\n",
    "print(\"And {}% of clients who had previously defaulted, defaulted again.\".format(second_default))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>loan_intent</th>\n",
       "      <th>DEBTCONSOLIDATION</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>HOMEIMPROVEMENT</th>\n",
       "      <th>MEDICAL</th>\n",
       "      <th>PERSONAL</th>\n",
       "      <th>VENTURE</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>10.62</td>\n",
       "      <td>10.62</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>14.09</td>\n",
       "      <td>13.85</td>\n",
       "      <td>14.09</td>\n",
       "      <td>14.11</td>\n",
       "      <td>14.10</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_intent   DEBTCONSOLIDATION  EDUCATION  HOMEIMPROVEMENT  MEDICAL  \\\n",
       "default_hist                                                           \n",
       "N                         10.62      10.62            10.74    10.74   \n",
       "Y                         14.09      13.85            14.09    14.11   \n",
       "\n",
       "loan_intent   PERSONAL  VENTURE  \n",
       "default_hist                     \n",
       "N                10.65    10.65  \n",
       "Y                14.10    13.99  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(cr_clean1['default_hist'], cr_clean1['loan_intent'], \n",
    "            values = cr_clean1['loan_int_rate'], aggfunc = 'median')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those who had not previously defaulted has a median loan interest rate 4% less than those who have defaulted. Issing a loan to client who may default has negative outcomes not only for the lender but also long term negative consequences for the client. We will use machine learning algorithms to improve credit risk modelling to reduce risk for both the lender and client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.49</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
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       "      <th>32579</th>\n",
       "      <td>56</td>\n",
       "      <td>150000</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>B</td>\n",
       "      <td>15000</td>\n",
       "      <td>11.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>N</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32580</th>\n",
       "      <td>66</td>\n",
       "      <td>42000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>B</td>\n",
       "      <td>6475</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>N</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32576 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
       "0              22          59000                  RENT              123.0   \n",
       "1              21           9600                   OWN                5.0   \n",
       "2              25           9600              MORTGAGE                1.0   \n",
       "3              23          65500                  RENT                4.0   \n",
       "4              24          54400                  RENT                8.0   \n",
       "...           ...            ...                   ...                ...   \n",
       "32576          57          53000              MORTGAGE                1.0   \n",
       "32577          54         120000              MORTGAGE                4.0   \n",
       "32578          65          76000                  RENT                3.0   \n",
       "32579          56         150000              MORTGAGE                5.0   \n",
       "32580          66          42000                  RENT                2.0   \n",
       "\n",
       "           loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
       "0             PERSONAL          D      35000          16.02            1   \n",
       "1            EDUCATION          B       1000          11.14            0   \n",
       "2              MEDICAL          C       5500          12.87            1   \n",
       "3              MEDICAL          C      35000          15.23            1   \n",
       "4              MEDICAL          C      35000          14.27            1   \n",
       "...                ...        ...        ...            ...          ...   \n",
       "32576         PERSONAL          C       5800          13.16            0   \n",
       "32577         PERSONAL          A      17625           7.49            0   \n",
       "32578  HOMEIMPROVEMENT          B      35000          10.99            1   \n",
       "32579         PERSONAL          B      15000          11.48            0   \n",
       "32580          MEDICAL          B       6475           9.99            0   \n",
       "\n",
       "       loan_percent_income default_hist  cr_hist_len  \n",
       "0                     0.59            Y            3  \n",
       "1                     0.10            N            2  \n",
       "2                     0.57            N            3  \n",
       "3                     0.53            N            2  \n",
       "4                     0.55            Y            4  \n",
       "...                    ...          ...          ...  \n",
       "32576                 0.11            N           30  \n",
       "32577                 0.15            N           19  \n",
       "32578                 0.46            N           28  \n",
       "32579                 0.10            N           26  \n",
       "32580                 0.15            N           30  \n",
       "\n",
       "[32576 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_clean1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>person_age</th>\n",
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       "</table>\n",
       "<p>32576 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       person_age  person_income  person_emp_length  loan_amnt  loan_int_rate  \\\n",
       "0              22          59000              123.0      35000          16.02   \n",
       "1              21           9600                5.0       1000          11.14   \n",
       "2              25           9600                1.0       5500          12.87   \n",
       "3              23          65500                4.0      35000          15.23   \n",
       "4              24          54400                8.0      35000          14.27   \n",
       "...           ...            ...                ...        ...            ...   \n",
       "32576          57          53000                1.0       5800          13.16   \n",
       "32577          54         120000                4.0      17625           7.49   \n",
       "32578          65          76000                3.0      35000          10.99   \n",
       "32579          56         150000                5.0      15000          11.48   \n",
       "32580          66          42000                2.0       6475           9.99   \n",
       "\n",
       "       loan_status  loan_percent_income  cr_hist_len  \\\n",
       "0                1                 0.59            3   \n",
       "1                0                 0.10            2   \n",
       "2                1                 0.57            3   \n",
       "3                1                 0.53            2   \n",
       "4                1                 0.55            4   \n",
       "...            ...                  ...          ...   \n",
       "32576            0                 0.11           30   \n",
       "32577            0                 0.15           19   \n",
       "32578            1                 0.46           28   \n",
       "32579            0                 0.10           26   \n",
       "32580            0                 0.15           30   \n",
       "\n",
       "       person_home_ownership_MORTGAGE  person_home_ownership_OTHER  ...  \\\n",
       "0                               False                        False  ...   \n",
       "1                               False                        False  ...   \n",
       "2                                True                        False  ...   \n",
       "3                               False                        False  ...   \n",
       "4                               False                        False  ...   \n",
       "...                               ...                          ...  ...   \n",
       "32576                            True                        False  ...   \n",
       "32577                            True                        False  ...   \n",
       "32578                           False                        False  ...   \n",
       "32579                            True                        False  ...   \n",
       "32580                           False                        False  ...   \n",
       "\n",
       "       loan_intent_VENTURE  loan_grade_A  loan_grade_B  loan_grade_C  \\\n",
       "0                    False         False         False         False   \n",
       "1                    False         False          True         False   \n",
       "2                    False         False         False          True   \n",
       "3                    False         False         False          True   \n",
       "4                    False         False         False          True   \n",
       "...                    ...           ...           ...           ...   \n",
       "32576                False         False         False          True   \n",
       "32577                False          True         False         False   \n",
       "32578                False         False          True         False   \n",
       "32579                False         False          True         False   \n",
       "32580                False         False          True         False   \n",
       "\n",
       "       loan_grade_D  loan_grade_E  loan_grade_F  loan_grade_G  default_hist_N  \\\n",
       "0              True         False         False         False           False   \n",
       "1             False         False         False         False            True   \n",
       "2             False         False         False         False            True   \n",
       "3             False         False         False         False            True   \n",
       "4             False         False         False         False           False   \n",
       "...             ...           ...           ...           ...             ...   \n",
       "32576         False         False         False         False            True   \n",
       "32577         False         False         False         False            True   \n",
       "32578         False         False         False         False            True   \n",
       "32579         False         False         False         False            True   \n",
       "32580         False         False         False         False            True   \n",
       "\n",
       "       default_hist_Y  \n",
       "0                True  \n",
       "1               False  \n",
       "2               False  \n",
       "3               False  \n",
       "4                True  \n",
       "...               ...  \n",
       "32576           False  \n",
       "32577           False  \n",
       "32578           False  \n",
       "32579           False  \n",
       "32580           False  \n",
       "\n",
       "[32576 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding categorical variables\n",
    "num_col = cr_clean1.select_dtypes(exclude = 'object')\n",
    "char_col = cr_clean1.select_dtypes(include = 'object')\n",
    "\n",
    "encoded_char_col = pd.get_dummies(char_col)\n",
    "\n",
    "cr_clean2 = pd.concat([num_col, encoded_char_col], axis=1)\n",
    "cr_clean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split Train and Test Sets\n",
    "Y = cr_clean2['loan_status']\n",
    "X = cr_clean2.drop('loan_status',axis=1)\n",
    " \n",
    "\n",
    "\n",
    "x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, random_state=2020, test_size=.30)\n",
    "\n",
    "#Start of Classification Logistics Regression\n",
    "\n",
    "log_clf = linear_model.LogisticRegression()\n",
    "\n",
    "log_clf.fit(x_train, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>col_names</th>\n",
       "      <th>col_coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>person_age</td>\n",
       "      <td>-6.595057e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>person_income</td>\n",
       "      <td>-4.041520e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>person_emp_length</td>\n",
       "      <td>-2.328480e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>1.071947e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>loan_int_rate</td>\n",
       "      <td>7.954648e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>loan_percent_income</td>\n",
       "      <td>2.520093e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cr_hist_len</td>\n",
       "      <td>-1.243093e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>person_home_ownership_MORTGAGE</td>\n",
       "      <td>-6.484666e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>person_home_ownership_OTHER</td>\n",
       "      <td>2.063278e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>person_home_ownership_OWN</td>\n",
       "      <td>-4.212456e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>person_home_ownership_RENT</td>\n",
       "      <td>8.178127e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>loan_intent_DEBTCONSOLIDATION</td>\n",
       "      <td>2.535988e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>loan_intent_EDUCATION</td>\n",
       "      <td>-3.055606e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>loan_intent_HOMEIMPROVEMENT</td>\n",
       "      <td>1.770592e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>loan_intent_MEDICAL</td>\n",
       "      <td>1.383256e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>loan_intent_PERSONAL</td>\n",
       "      <td>-1.451700e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>loan_intent_VENTURE</td>\n",
       "      <td>-3.680892e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>loan_grade_A</td>\n",
       "      <td>-1.062200e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>loan_grade_B</td>\n",
       "      <td>-6.098216e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>loan_grade_C</td>\n",
       "      <td>-1.465050e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>loan_grade_D</td>\n",
       "      <td>1.072112e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>loan_grade_E</td>\n",
       "      <td>3.528367e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>loan_grade_F</td>\n",
       "      <td>9.824017e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>loan_grade_G</td>\n",
       "      <td>4.550201e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>default_hist_N</td>\n",
       "      <td>-9.467223e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>default_hist_Y</td>\n",
       "      <td>6.968861e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         col_names      col_coef\n",
       "0                       person_age -6.595057e-08\n",
       "1                    person_income -4.041520e-05\n",
       "2                person_emp_length -2.328480e-08\n",
       "3                        loan_amnt  1.071947e-04\n",
       "4                    loan_int_rate  7.954648e-08\n",
       "5              loan_percent_income  2.520093e-09\n",
       "6                      cr_hist_len -1.243093e-08\n",
       "7   person_home_ownership_MORTGAGE -6.484666e-09\n",
       "8      person_home_ownership_OTHER  2.063278e-11\n",
       "9        person_home_ownership_OWN -4.212456e-09\n",
       "10      person_home_ownership_RENT  8.178127e-09\n",
       "11   loan_intent_DEBTCONSOLIDATION  2.535988e-09\n",
       "12           loan_intent_EDUCATION -3.055606e-09\n",
       "13     loan_intent_HOMEIMPROVEMENT  1.770592e-09\n",
       "14             loan_intent_MEDICAL  1.383256e-09\n",
       "15            loan_intent_PERSONAL -1.451700e-09\n",
       "16             loan_intent_VENTURE -3.680892e-09\n",
       "17                    loan_grade_A -1.062200e-08\n",
       "18                    loan_grade_B -6.098216e-09\n",
       "19                    loan_grade_C -1.465050e-09\n",
       "20                    loan_grade_D  1.072112e-08\n",
       "21                    loan_grade_E  3.528367e-09\n",
       "22                    loan_grade_F  9.824017e-10\n",
       "23                    loan_grade_G  4.550201e-10\n",
       "24                  default_hist_N -9.467223e-09\n",
       "25                  default_hist_Y  6.968861e-09"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_effect = pd.DataFrame()\n",
    "col_effect['col_names'] = X.columns\n",
    "col_effect['col_coef'] = log_clf.coef_[0]\n",
    "col_effect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells  to deaultfor every one unit of increase in each column, the person is more likely when the coefficient is more positive and less likely when the coefficient is more negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The overall probablity of non default is -0.000%\n"
     ]
    }
   ],
   "source": [
    "int_val = float(log_clf.intercept_)\n",
    "print('The overall probablity of non default is {:.3%}'.format(int_val))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use he previous the intercept and coefficient values of calculate the probability of default ( P = 1 ) and non default ( P = 0 ).\n",
    "\n",
    "First we need the sum of the intercept and coefficients x column value. For example, int_coef_sum = intercept + [col_coef] X [col_values].\n",
    "\n",
    "Then we can calculate the probabilities of default and non default witht eh logistic regression formula.\n",
    "\n",
    "prob_default = 1/ (1 + np.exp(-int_coef_sum))\n",
    "\n",
    "prob_nondefault = 1 - prob_default "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    },
    {
     "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_status</th>\n",
       "      <th>prob_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.292610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.006268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.370780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.495510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.216253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9768</th>\n",
       "      <td>0</td>\n",
       "      <td>0.407430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9769</th>\n",
       "      <td>0</td>\n",
       "      <td>0.330124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9770</th>\n",
       "      <td>1</td>\n",
       "      <td>0.471306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9771</th>\n",
       "      <td>0</td>\n",
       "      <td>0.231141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9772</th>\n",
       "      <td>0</td>\n",
       "      <td>0.289141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9773 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      loan_status  prob_default\n",
       "0               0      0.292610\n",
       "1               0      0.006268\n",
       "2               0      0.370780\n",
       "3               0      0.495510\n",
       "4               0      0.216253\n",
       "...           ...           ...\n",
       "9768            0      0.407430\n",
       "9769            0      0.330124\n",
       "9770            1      0.471306\n",
       "9771            0      0.231141\n",
       "9772            0      0.289141\n",
       "\n",
       "[9773 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first column is the logistic regression value\n",
    "# second column is the predicted probability of default == 1\n",
    "predict_log = pd.DataFrame(log_clf.predict_proba(x_test)[:,1], columns=['prob_default'])\n",
    "\n",
    "pred_df = pd.concat([y_test.reset_index(drop=True), predict_log],axis=1)\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial accuracy is 0.81\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    }
   ],
   "source": [
    "# check the accuracy\n",
    "initial_accuracy = round(log_clf.score(x_test,  y_test),2)\n",
    "print(\"The initial accuracy is {}\".format(initial_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to whether there is a more suitable threshold to improve our accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "       0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresh = np.linspace(0,1,21)\n",
    "thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(pred_df.iloc[:,0],y_test, labels = [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "def find_opt_thresh(predict,thr =thresh, y_true = y_test):\n",
    "    data = predict\n",
    "    \n",
    "    def_recalls = []\n",
    "    nondef_recalls = []\n",
    "    accs =[]\n",
    "\n",
    "    \n",
    "    for threshold in thr:\n",
    "        # predicted values for each threshold\n",
    "        data['loan_status'] = data['prob_default'].apply(lambda x: 1 if x > threshold else 0 )\n",
    "        \n",
    "        accs.append(metrics.accuracy_score(y_true, data['loan_status']))\n",
    "        \n",
    "        stats = metrics.precision_recall_fscore_support(y_true, data['loan_status'])\n",
    "        \n",
    "        def_recalls.append(stats[1][1])\n",
    "        nondef_recalls.append(stats[1][0])\n",
    "        \n",
    "        \n",
    "    return accs, def_recalls, nondef_recalls\n",
    "\n",
    "accs, def_recalls, nondef_recalls= find_opt_thresh(pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(thresh,def_recalls)\n",
    "plt.plot(thresh,nondef_recalls)\n",
    "plt.plot(thresh,accs)\n",
    "plt.xlabel(\"Probability Threshold\")\n",
    "plt.xticks(thresh, rotation = 'vertical')\n",
    "plt.legend([\"Default Recall\",\"Non-default Recall\",\"Model Accuracy\"])\n",
    "#plt.axvline(x=0.45, color='pink')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum accuracy is 83%.\n",
      "Therefore we should have a threshold of 45%.\n"
     ]
    }
   ],
   "source": [
    "max_accuracy_index = accs.index(max(accs))\n",
    "\n",
    "print('The maximum accuracy is {:.0%}.'.format(accs[max_accuracy_index]))\n",
    "print('Therefore we should have a threshold of {:.0%}.'.format(thresh[max_accuracy_index]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further optimize the accuracy level with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_age</th>\n",
       "      <th>person_income</th>\n",
       "      <th>person_emp_length</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_int_rate</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>loan_percent_income</th>\n",
       "      <th>cr_hist_len</th>\n",
       "      <th>person_home_ownership_MORTGAGE</th>\n",
       "      <th>person_home_ownership_OTHER</th>\n",
       "      <th>...</th>\n",
       "      <th>loan_intent_VENTURE</th>\n",
       "      <th>loan_grade_A</th>\n",
       "      <th>loan_grade_B</th>\n",
       "      <th>loan_grade_C</th>\n",
       "      <th>loan_grade_D</th>\n",
       "      <th>loan_grade_E</th>\n",
       "      <th>loan_grade_F</th>\n",
       "      <th>loan_grade_G</th>\n",
       "      <th>default_hist_N</th>\n",
       "      <th>default_hist_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>59000</td>\n",
       "      <td>123.0</td>\n",
       "      <td>35000</td>\n",
       "      <td>16.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>9600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>11.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>9600</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5500</td>\n",
       "      <td>12.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>65500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35000</td>\n",
       "      <td>15.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>54400</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35000</td>\n",
       "      <td>14.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.55</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>32576</th>\n",
       "      <td>57</td>\n",
       "      <td>53000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5800</td>\n",
       "      <td>13.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32577</th>\n",
       "      <td>54</td>\n",
       "      <td>120000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17625</td>\n",
       "      <td>7.49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>19</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32578</th>\n",
       "      <td>65</td>\n",
       "      <td>76000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>35000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>28</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32579</th>\n",
       "      <td>56</td>\n",
       "      <td>150000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15000</td>\n",
       "      <td>11.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32580</th>\n",
       "      <td>66</td>\n",
       "      <td>42000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6475</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32576 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       person_age  person_income  person_emp_length  loan_amnt  loan_int_rate  \\\n",
       "0              22          59000              123.0      35000          16.02   \n",
       "1              21           9600                5.0       1000          11.14   \n",
       "2              25           9600                1.0       5500          12.87   \n",
       "3              23          65500                4.0      35000          15.23   \n",
       "4              24          54400                8.0      35000          14.27   \n",
       "...           ...            ...                ...        ...            ...   \n",
       "32576          57          53000                1.0       5800          13.16   \n",
       "32577          54         120000                4.0      17625           7.49   \n",
       "32578          65          76000                3.0      35000          10.99   \n",
       "32579          56         150000                5.0      15000          11.48   \n",
       "32580          66          42000                2.0       6475           9.99   \n",
       "\n",
       "       loan_status  loan_percent_income  cr_hist_len  \\\n",
       "0                1                 0.59            3   \n",
       "1                0                 0.10            2   \n",
       "2                1                 0.57            3   \n",
       "3                1                 0.53            2   \n",
       "4                1                 0.55            4   \n",
       "...            ...                  ...          ...   \n",
       "32576            0                 0.11           30   \n",
       "32577            0                 0.15           19   \n",
       "32578            1                 0.46           28   \n",
       "32579            0                 0.10           26   \n",
       "32580            0                 0.15           30   \n",
       "\n",
       "       person_home_ownership_MORTGAGE  person_home_ownership_OTHER  ...  \\\n",
       "0                               False                        False  ...   \n",
       "1                               False                        False  ...   \n",
       "2                                True                        False  ...   \n",
       "3                               False                        False  ...   \n",
       "4                               False                        False  ...   \n",
       "...                               ...                          ...  ...   \n",
       "32576                            True                        False  ...   \n",
       "32577                            True                        False  ...   \n",
       "32578                           False                        False  ...   \n",
       "32579                            True                        False  ...   \n",
       "32580                           False                        False  ...   \n",
       "\n",
       "       loan_intent_VENTURE  loan_grade_A  loan_grade_B  loan_grade_C  \\\n",
       "0                    False         False         False         False   \n",
       "1                    False         False          True         False   \n",
       "2                    False         False         False          True   \n",
       "3                    False         False         False          True   \n",
       "4                    False         False         False          True   \n",
       "...                    ...           ...           ...           ...   \n",
       "32576                False         False         False          True   \n",
       "32577                False          True         False         False   \n",
       "32578                False         False          True         False   \n",
       "32579                False         False          True         False   \n",
       "32580                False         False          True         False   \n",
       "\n",
       "       loan_grade_D  loan_grade_E  loan_grade_F  loan_grade_G  default_hist_N  \\\n",
       "0              True         False         False         False           False   \n",
       "1             False         False         False         False            True   \n",
       "2             False         False         False         False            True   \n",
       "3             False         False         False         False            True   \n",
       "4             False         False         False         False           False   \n",
       "...             ...           ...           ...           ...             ...   \n",
       "32576         False         False         False         False            True   \n",
       "32577         False         False         False         False            True   \n",
       "32578         False         False         False         False            True   \n",
       "32579         False         False         False         False            True   \n",
       "32580         False         False         False         False            True   \n",
       "\n",
       "       default_hist_Y  \n",
       "0                True  \n",
       "1               False  \n",
       "2               False  \n",
       "3               False  \n",
       "4                True  \n",
       "...               ...  \n",
       "32576           False  \n",
       "32577           False  \n",
       "32578           False  \n",
       "32579           False  \n",
       "32580           False  \n",
       "\n",
       "[32576 rows x 27 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_clean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=[0, 1])\n",
    "data_rescaled = scaler.fit_transform(cr_clean2)\n",
    "\n",
    "#Fitting the PCA algorithm with our Data\n",
    "pca = PCA().fit(data_rescaled)\n",
    "#Plotting the Cumulative Summation of the Explained Variance\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Variance (%)') #for each component\n",
    "plt.title('Hotel Booking Dataset Explained Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure above, I have identified 14 components would be optiminal number to have the most simplied model with the most amount of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    }
   ],
   "source": [
    "# normalize data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pie = cr_clean2.drop('loan_status',axis=1)\n",
    "\n",
    "data_scaled = pd.DataFrame(preprocessing.scale(pie),columns = pie.columns) \n",
    "\n",
    "# PCA\n",
    "pca = PCA(n_components=14)\n",
    "pca_val = pca.fit_transform(data_scaled)\n",
    "pca_dataset = pd.DataFrame(pca_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_variance_ratio = np.cumsum(pca.explained_variance_ratio_)\n",
    "optimal_components = np.argmax(cumulative_variance_ratio >= 0.95) + 1  # Choose components explaining 95% variance\n",
    "optimal_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.86"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = model_selection.train_test_split(pca_dataset, Y, random_state=2020, test_size=.32)\n",
    "\n",
    "#Start of Classification Logistics Regression\n",
    "\n",
    "log_clf = linear_model.LogisticRegression()\n",
    "\n",
    "log_clf.fit(x_train, np.ravel(y_train))\n",
    "\n",
    "# first column is the logistic regression value\n",
    "# second column is the predicted probability of default == 1\n",
    "pca_predict_log = pd.DataFrame(log_clf.predict_proba(x_test)[:,1], columns=['prob_default'])\n",
    "\n",
    "pca_pred_df = pd.concat([y_test.reset_index(drop=True), pca_predict_log],axis=1)\n",
    "pca_pred_df\n",
    "\n",
    "pca_accuracy = round(log_clf.score(x_test,  y_test),2)\n",
    "pca_accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have improved the accuracy of our model from ****81% to 86%**** by leveraging ****Principle Component Analysis*** and ****hyperparameter tuning****.\n",
    "\n",
    "The current process for credit assessment had a default rate of 22% as shown below. The new credit risk assessment algorithm which we had develop with principal component analysis and logistic regression had a reduced default rate from ****22% to 14%****  (1-0.86). \n",
    "\n",
    "The ****5% increase in accuracy resulted in the an 8% reduction of defaulted loans****, minimizing the lender's risk and improving their confidence to lend credit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Log Agent will be used as a feature for the XGB model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:757: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:595: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype):\n",
      "/Users/kennethleung/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:604: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
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       "       loan_status  prob_default\n",
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       "\n",
       "[10425 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_predict_log = pd.DataFrame(log_clf.predict_proba(x_test)[:,1], columns=['prob_default'])\n",
    "\n",
    "pca_pred_df = pd.concat([y_test.reset_index(drop=True), pca_predict_log],axis=1)\n",
    "pca_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.91      0.90      8195\n",
      "           1       0.66      0.61      0.63      2230\n",
      "\n",
      "    accuracy                           0.85     10425\n",
      "   macro avg       0.78      0.76      0.77     10425\n",
      "weighted avg       0.84      0.85      0.85     10425\n",
      "\n",
      "0.38\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.92      0.91      8195\n",
      "           1       0.68      0.58      0.63      2230\n",
      "\n",
      "    accuracy                           0.85     10425\n",
      "   macro avg       0.78      0.75      0.77     10425\n",
      "weighted avg       0.85      0.85      0.85     10425\n",
      "\n",
      "0.4\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.93      0.91      8195\n",
      "           1       0.70      0.57      0.63      2230\n",
      "\n",
      "    accuracy                           0.85     10425\n",
      "   macro avg       0.79      0.75      0.77     10425\n",
      "weighted avg       0.85      0.85      0.85     10425\n",
      "\n",
      "0.42\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.94      0.91      8195\n",
      "           1       0.71      0.55      0.62      2230\n",
      "\n",
      "    accuracy                           0.86     10425\n",
      "   macro avg       0.80      0.75      0.77     10425\n",
      "weighted avg       0.85      0.86      0.85     10425\n",
      "\n",
      "0.44\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.94      0.91      8195\n",
      "           1       0.72      0.54      0.62      2230\n",
      "\n",
      "    accuracy                           0.86     10425\n",
      "   macro avg       0.80      0.74      0.76     10425\n",
      "weighted avg       0.85      0.86      0.85     10425\n",
      "\n",
      "0.45\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.95      0.91      8195\n",
      "           1       0.73      0.53      0.61      2230\n",
      "\n",
      "    accuracy                           0.86     10425\n",
      "   macro avg       0.81      0.74      0.76     10425\n",
      "weighted avg       0.85      0.86      0.85     10425\n",
      "\n",
      "0.5\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.96      0.91      8195\n",
      "           1       0.77      0.48      0.59      2230\n",
      "\n",
      "    accuracy                           0.86     10425\n",
      "   macro avg       0.82      0.72      0.75     10425\n",
      "weighted avg       0.85      0.86      0.84     10425\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_thres = pca_pred_df['prob_default'].apply(lambda x: 1 if x > 0.45 else 0)\n",
    "\n",
    "for i in [0.36,0.38,0.40,0.42,0.44,0.45, 0.50]:\n",
    "    print(i)\n",
    "    y_pred_thres = pca_pred_df['prob_default'].apply(lambda x: 1 if x > i else 0)\n",
    "\n",
    "    print(classification_report(y_test, y_pred_thres))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this multi agent problem, we will set the threshold as 0.36 so that this model will have a recall of at least 60%."
   ]
  }
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