19. Credit Risk Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c46c810",
   "metadata": {},
   "source": [
    "# Credit Risk Prediction\n",
    "\n",
    "## Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f107eca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "fe5da808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_age</th>\n",
       "      <th>person_income</th>\n",
       "      <th>person_home_ownership</th>\n",
       "      <th>person_emp_length</th>\n",
       "      <th>loan_intent</th>\n",
       "      <th>loan_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_int_rate</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>loan_percent_income</th>\n",
       "      <th>cb_person_default_on_file</th>\n",
       "      <th>cb_person_cred_hist_length</th>\n",
       "    </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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   person_age  person_income person_home_ownership  person_emp_length  \\\n",
       "0          22          59000                  RENT              123.0   \n",
       "1          21           9600                   OWN                5.0   \n",
       "2          25           9600              MORTGAGE                1.0   \n",
       "3          23          65500                  RENT                4.0   \n",
       "4          24          54400                  RENT                8.0   \n",
       "\n",
       "  loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
       "0    PERSONAL          D      35000          16.02            1   \n",
       "1   EDUCATION          B       1000          11.14            0   \n",
       "2     MEDICAL          C       5500          12.87            1   \n",
       "3     MEDICAL          C      35000          15.23            1   \n",
       "4     MEDICAL          C      35000          14.27            1   \n",
       "\n",
       "   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n",
       "0                 0.59                         Y                           3  \n",
       "1                 0.10                         N                           2  \n",
       "2                 0.57                         N                           3  \n",
       "3                 0.53                         N                           2  \n",
       "4                 0.55                         Y                           4  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/proj19/credit_risk_dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e1c9fa4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5050110370>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.pie(df[\"cb_person_default_on_file\"].value_counts(), labels=[\"Yes\", \"No\"], explode=[0, 0.2], shadow=True)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b362ebb0",
   "metadata": {},
   "source": [
    "## Pre-Processing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f7e87301",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b81a5ba9",
   "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",
       "cb_person_default_on_file        0\n",
       "cb_person_cred_hist_length       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a3ff234d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32581 entries, 0 to 32580\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   person_age                  32581 non-null  int64  \n",
      " 1   person_income               32581 non-null  int64  \n",
      " 2   person_home_ownership       32581 non-null  object \n",
      " 3   person_emp_length           31686 non-null  float64\n",
      " 4   loan_intent                 32581 non-null  object \n",
      " 5   loan_grade                  32581 non-null  object \n",
      " 6   loan_amnt                   32581 non-null  int64  \n",
      " 7   loan_int_rate               29465 non-null  float64\n",
      " 8   loan_status                 32581 non-null  int64  \n",
      " 9   loan_percent_income         32581 non-null  float64\n",
      " 10  cb_person_default_on_file   32581 non-null  object \n",
      " 11  cb_person_cred_hist_length  32581 non-null  int64  \n",
      "dtypes: float64(3), int64(5), object(4)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "c67a9674",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df[\"person_emp_length\"] =  df[\"person_emp_length\"].fillna(np.mean(df[\"person_emp_length\"]))\n",
    "df[\"loan_int_rate\"] =  df[\"loan_int_rate\"].fillna(np.mean(df[\"loan_int_rate\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1a91ed35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "person_age                    0\n",
       "person_income                 0\n",
       "person_home_ownership         0\n",
       "person_emp_length             0\n",
       "loan_intent                   0\n",
       "loan_grade                    0\n",
       "loan_amnt                     0\n",
       "loan_int_rate                 0\n",
       "loan_status                   0\n",
       "loan_percent_income           0\n",
       "cb_person_default_on_file     0\n",
       "cb_person_cred_hist_length    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "262f27c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person_home_ownership\n",
      "----------\n",
      "['MORTGAGE' 'OTHER' 'OWN' 'RENT']\n",
      "----------\n",
      "loan_intent\n",
      "----------\n",
      "['DEBTCONSOLIDATION' 'EDUCATION' 'HOMEIMPROVEMENT' 'MEDICAL' 'PERSONAL'\n",
      " 'VENTURE']\n",
      "----------\n",
      "loan_grade\n",
      "----------\n",
      "['A' 'B' 'C' 'D' 'E' 'F' 'G']\n",
      "----------\n",
      "cb_person_default_on_file\n",
      "----------\n",
      "['N' 'Y']\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for stuff in df.keys():\n",
    "    if pd.api.types.is_object_dtype(df[stuff]) == True:\n",
    "        le = LabelEncoder()\n",
    "        df[stuff] = le.fit_transform(df[stuff])\n",
    "        print(stuff)\n",
    "        print(\"-\"*10)\n",
    "        print(le.classes_)\n",
    "        print(\"-\"*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6501283d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32581 entries, 0 to 32580\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   person_age                  32581 non-null  int64  \n",
      " 1   person_income               32581 non-null  int64  \n",
      " 2   person_home_ownership       32581 non-null  int64  \n",
      " 3   person_emp_length           32581 non-null  float64\n",
      " 4   loan_intent                 32581 non-null  int64  \n",
      " 5   loan_grade                  32581 non-null  int64  \n",
      " 6   loan_amnt                   32581 non-null  int64  \n",
      " 7   loan_int_rate               32581 non-null  float64\n",
      " 8   loan_status                 32581 non-null  int64  \n",
      " 9   loan_percent_income         32581 non-null  float64\n",
      " 10  cb_person_default_on_file   32581 non-null  int64  \n",
      " 11  cb_person_cred_hist_length  32581 non-null  int64  \n",
      "dtypes: float64(3), int64(9)\n",
      "memory usage: 3.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d576df2",
   "metadata": {},
   "source": [
    "## Preparing the data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6f0fad99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26064, 26064, 6517, 6517)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(\"cb_person_default_on_file\", axis=1)\n",
    "y = df[\"cb_person_default_on_file\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "len(X_train), len(y_train), len(X_test), len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1daa872c",
   "metadata": {},
   "source": [
    "## Creating an model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "30b060b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.34% accuracy\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knclf = KNeighborsClassifier()\n",
    "knclf.fit(X_train, y_train)\n",
    "model_score = knclf.score(X_test, y_test)\n",
    "print(str(round(model_score*100, 2)) + \"% accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "9ffcbb34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.73% accuracy\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rclf = RandomForestClassifier(random_state=42)\n",
    "rclf.fit(X_train, y_train)\n",
    "model_score = rclf.score(X_test, y_test)\n",
    "\n",
    "print(str(round(model_score*100, 2)) + \"% accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "26da5ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgclf = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a2264330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.34% accuracy\n"
     ]
    }
   ],
   "source": [
    "xgclf.fit(X_train, y_train)\n",
    "model_score = xgclf.score(X_test, y_test)\n",
    "print(str(round(model_score*100, 2)) + \"% accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "03b8d2bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/djoe/.local/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rclf.predict([[21, 30000, 2, 100.0, 4, 0, 20000, 10.3, 1, 0.43, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d9234553",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/djoe/.local/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knclf.predict([[21, 30000, 2, 100.0, 4, 0, 20000, 10.3, 1, 0.43, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "218d3309",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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