Loan default prediction using SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52716a45-cf04-470f-ae8a-f1160ab3bded",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff91873a-4bc4-4e5d-ba1d-50eadcadafde",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix \n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b26008c7-e238-49ae-97c3-41d925823dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e091bd2d-e6fe-4f94-abee-0926e37ba66f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l0/rswxq5r17tg_xh8mp8pj68mh0000gn/T/ipykernel_26922/2247868516.py:1: DtypeWarning: Columns (18) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\"HW9_LoansData.csv\")\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38098114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.39</td>\n",
       "      <td>336.64</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36805548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10400</td>\n",
       "      <td>10400</td>\n",
       "      <td>10400</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.99</td>\n",
       "      <td>321.08</td>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37842129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21425</td>\n",
       "      <td>21425</td>\n",
       "      <td>21425</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.59</td>\n",
       "      <td>516.36</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37612354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12800</td>\n",
       "      <td>12800</td>\n",
       "      <td>12800</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.14</td>\n",
       "      <td>319.08</td>\n",
       "      <td>D</td>\n",
       "      <td>D4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37662224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7650</td>\n",
       "      <td>7650</td>\n",
       "      <td>7650</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.66</td>\n",
       "      <td>260.20</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  38098114        NaN      15000        15000            15000   60 months   \n",
       "1  36805548        NaN      10400        10400            10400   36 months   \n",
       "2  37842129        NaN      21425        21425            21425   60 months   \n",
       "3  37612354        NaN      12800        12800            12800   60 months   \n",
       "4  37662224        NaN       7650         7650             7650   36 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade  ... hardship_payoff_balance_amount  \\\n",
       "0     12.39       336.64     C        C1  ...                            NaN   \n",
       "1      6.99       321.08     A        A3  ...                            NaN   \n",
       "2     15.59       516.36     D        D1  ...                            NaN   \n",
       "3     17.14       319.08     D        D4  ...                            NaN   \n",
       "4     13.66       260.20     C        C3  ...                            NaN   \n",
       "\n",
       "  hardship_last_payment_amount disbursement_method  debt_settlement_flag  \\\n",
       "0                          NaN                Cash                     N   \n",
       "1                          NaN                Cash                     N   \n",
       "2                          NaN                Cash                     N   \n",
       "3                          NaN                Cash                     N   \n",
       "4                          NaN                Cash                     N   \n",
       "\n",
       "  debt_settlement_flag_date settlement_status settlement_date  \\\n",
       "0                       NaN               NaN             NaN   \n",
       "1                       NaN               NaN             NaN   \n",
       "2                       NaN               NaN             NaN   \n",
       "3                       NaN               NaN             NaN   \n",
       "4                       NaN               NaN             NaN   \n",
       "\n",
       "  settlement_amount settlement_percentage settlement_term  \n",
       "0               NaN                   NaN             NaN  \n",
       "1               NaN                   NaN             NaN  \n",
       "2               NaN                   NaN             NaN  \n",
       "3               NaN                   NaN             NaN  \n",
       "4               NaN                   NaN             NaN  \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"HW9_LoansData.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "212f3fff-737a-462f-89c0-a359ef4add89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid            69982\n",
       "Charged Off           16156\n",
       "Current               12726\n",
       "Late (31-120 days)      730\n",
       "In Grace Period         264\n",
       "Late (16-30 days)       139\n",
       "Default                   3\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ef92ee2-abdf-4471-926a-5ccd039d48e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     0.812441\n",
       "Charged Off    0.187559\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.loc[df['loan_status'].isin(['Fully Paid', 'Charged Off'])]\n",
    "df['loan_status'].value_counts(dropna=False, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd3559ef-73d0-45cd-b0fa-a188203c07b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    86138.000000\n",
       "mean         0.187559\n",
       "std          0.390362\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: charged_off, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['charged_off']=df['loan_status'].map(lambda x: 1 if 'Charged Off' in x else 0)\n",
    "df['charged_off'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3cd51bc1-03b0-4c92-807f-a93dc1b26399",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('loan_status', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66ecafdd-27e5-4efc-ac6f-7ad21bede42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86138, 92)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_frac=df.isnull().mean().sort_values(ascending=False)\n",
    "drop_list=sorted(list(missing_frac[missing_frac>0.3].index))\n",
    "df.drop(labels=drop_list, axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de2814e5-0210-462f-bd20-bc6900b47e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>charged_off</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>installment</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>term</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>12.03</td>\n",
       "      <td>Aug-94</td>\n",
       "      <td>754</td>\n",
       "      <td>RENT</td>\n",
       "      <td>336.64</td>\n",
       "      <td>12.39</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15000</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>12017.81</td>\n",
       "      <td>29828</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10400</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>14.92</td>\n",
       "      <td>Sep-89</td>\n",
       "      <td>714</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>321.08</td>\n",
       "      <td>6.99</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10400</td>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "      <td>321.08</td>\n",
       "      <td>9536</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>21425</td>\n",
       "      <td>63800.0</td>\n",
       "      <td>18.49</td>\n",
       "      <td>Aug-03</td>\n",
       "      <td>689</td>\n",
       "      <td>RENT</td>\n",
       "      <td>516.36</td>\n",
       "      <td>15.59</td>\n",
       "      <td>60 months</td>\n",
       "      <td>21425</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>17813.19</td>\n",
       "      <td>4232</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7650</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>34.81</td>\n",
       "      <td>Aug-02</td>\n",
       "      <td>689</td>\n",
       "      <td>RENT</td>\n",
       "      <td>260.20</td>\n",
       "      <td>13.66</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7650</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>17.70</td>\n",
       "      <td>5857</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>9600</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>25.81</td>\n",
       "      <td>Nov-92</td>\n",
       "      <td>684</td>\n",
       "      <td>RENT</td>\n",
       "      <td>326.53</td>\n",
       "      <td>13.66</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9600</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>9338.58</td>\n",
       "      <td>3214</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   charged_off  funded_amnt  annual_inc    dti earliest_cr_line  \\\n",
       "0            0        15000     78000.0  12.03           Aug-94   \n",
       "1            1        10400     58000.0  14.92           Sep-89   \n",
       "2            0        21425     63800.0  18.49           Aug-03   \n",
       "4            1         7650     50000.0  34.81           Aug-02   \n",
       "5            0         9600     69000.0  25.81           Nov-92   \n",
       "\n",
       "   fico_range_high home_ownership  installment  int_rate        term  \\\n",
       "0              754           RENT       336.64     12.39   60 months   \n",
       "1              714       MORTGAGE       321.08      6.99   36 months   \n",
       "2              689           RENT       516.36     15.59   60 months   \n",
       "4              689           RENT       260.20     13.66   36 months   \n",
       "5              684           RENT       326.53     13.66   36 months   \n",
       "\n",
       "   loan_amnt grade sub_grade  last_pymnt_amnt  avg_cur_bal  \\\n",
       "0      15000     C        C1         12017.81        29828   \n",
       "1      10400     A        A3           321.08         9536   \n",
       "2      21425     D        D1         17813.19         4232   \n",
       "4       7650     C        C3            17.70         5857   \n",
       "5       9600     C        C3          9338.58         3214   \n",
       "\n",
       "   acc_open_past_24mths  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     4  \n",
       "4                     6  \n",
       "5                     8  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep_list=['charged_off', 'funded_amnt', 'annual_inc', 'dti', 'earliest_cr_line', \\\n",
    "          'fico_range_high', 'home_ownership', 'installment', 'int_rate', 'term', \\\n",
    "          'loan_amnt', 'grade', 'sub_grade', 'last_pymnt_amnt', 'avg_cur_bal', 'acc_open_past_24mths']\n",
    "df=df[keep_list]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd8a7d52-021a-4907-b15a-d6c27bb95a39",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>charged_off</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>installment</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>charged_off</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060818</td>\n",
       "      <td>-0.050349</td>\n",
       "      <td>0.123108</td>\n",
       "      <td>-0.121891</td>\n",
       "      <td>0.032847</td>\n",
       "      <td>0.282825</td>\n",
       "      <td>0.060818</td>\n",
       "      <td>-0.319081</td>\n",
       "      <td>-0.071977</td>\n",
       "      <td>0.105989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>0.060818</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375339</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>0.145427</td>\n",
       "      <td>0.956720</td>\n",
       "      <td>0.081992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483553</td>\n",
       "      <td>0.249609</td>\n",
       "      <td>-0.002055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>-0.050349</td>\n",
       "      <td>0.375339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.211716</td>\n",
       "      <td>0.103118</td>\n",
       "      <td>0.368550</td>\n",
       "      <td>-0.116058</td>\n",
       "      <td>0.375339</td>\n",
       "      <td>0.199016</td>\n",
       "      <td>0.372657</td>\n",
       "      <td>0.041771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0.123108</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>-0.211716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065497</td>\n",
       "      <td>0.005816</td>\n",
       "      <td>0.203275</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>-0.048400</td>\n",
       "      <td>-0.139358</td>\n",
       "      <td>0.165314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fico_range_high</th>\n",
       "      <td>-0.121891</td>\n",
       "      <td>0.145427</td>\n",
       "      <td>0.103118</td>\n",
       "      <td>-0.065497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105178</td>\n",
       "      <td>-0.387204</td>\n",
       "      <td>0.145427</td>\n",
       "      <td>0.103165</td>\n",
       "      <td>0.148058</td>\n",
       "      <td>-0.110641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0.032847</td>\n",
       "      <td>0.956720</td>\n",
       "      <td>0.368550</td>\n",
       "      <td>0.005816</td>\n",
       "      <td>0.105178</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.078197</td>\n",
       "      <td>0.956720</td>\n",
       "      <td>0.397907</td>\n",
       "      <td>0.226755</td>\n",
       "      <td>0.007955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.282825</td>\n",
       "      <td>0.081992</td>\n",
       "      <td>-0.116058</td>\n",
       "      <td>0.203275</td>\n",
       "      <td>-0.387204</td>\n",
       "      <td>0.078197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081992</td>\n",
       "      <td>0.099688</td>\n",
       "      <td>-0.097410</td>\n",
       "      <td>0.225447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>0.060818</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375339</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>0.145427</td>\n",
       "      <td>0.956720</td>\n",
       "      <td>0.081992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483553</td>\n",
       "      <td>0.249609</td>\n",
       "      <td>-0.002055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <td>-0.319081</td>\n",
       "      <td>0.483553</td>\n",
       "      <td>0.199016</td>\n",
       "      <td>-0.048400</td>\n",
       "      <td>0.103165</td>\n",
       "      <td>0.397907</td>\n",
       "      <td>0.099688</td>\n",
       "      <td>0.483553</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.178699</td>\n",
       "      <td>0.036069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <td>-0.071977</td>\n",
       "      <td>0.249609</td>\n",
       "      <td>0.372657</td>\n",
       "      <td>-0.139358</td>\n",
       "      <td>0.148058</td>\n",
       "      <td>0.226755</td>\n",
       "      <td>-0.097410</td>\n",
       "      <td>0.249609</td>\n",
       "      <td>0.178699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.100472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <td>0.105989</td>\n",
       "      <td>-0.002055</td>\n",
       "      <td>0.041771</td>\n",
       "      <td>0.165314</td>\n",
       "      <td>-0.110641</td>\n",
       "      <td>0.007955</td>\n",
       "      <td>0.225447</td>\n",
       "      <td>-0.002055</td>\n",
       "      <td>0.036069</td>\n",
       "      <td>-0.100472</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      charged_off  funded_amnt  annual_inc       dti  \\\n",
       "charged_off              1.000000     0.060818   -0.050349  0.123108   \n",
       "funded_amnt              0.060818     1.000000    0.375339  0.004018   \n",
       "annual_inc              -0.050349     0.375339    1.000000 -0.211716   \n",
       "dti                      0.123108     0.004018   -0.211716  1.000000   \n",
       "fico_range_high         -0.121891     0.145427    0.103118 -0.065497   \n",
       "installment              0.032847     0.956720    0.368550  0.005816   \n",
       "int_rate                 0.282825     0.081992   -0.116058  0.203275   \n",
       "loan_amnt                0.060818     1.000000    0.375339  0.004018   \n",
       "last_pymnt_amnt         -0.319081     0.483553    0.199016 -0.048400   \n",
       "avg_cur_bal             -0.071977     0.249609    0.372657 -0.139358   \n",
       "acc_open_past_24mths     0.105989    -0.002055    0.041771  0.165314   \n",
       "\n",
       "                      fico_range_high  installment  int_rate  loan_amnt  \\\n",
       "charged_off                 -0.121891     0.032847  0.282825   0.060818   \n",
       "funded_amnt                  0.145427     0.956720  0.081992   1.000000   \n",
       "annual_inc                   0.103118     0.368550 -0.116058   0.375339   \n",
       "dti                         -0.065497     0.005816  0.203275   0.004018   \n",
       "fico_range_high              1.000000     0.105178 -0.387204   0.145427   \n",
       "installment                  0.105178     1.000000  0.078197   0.956720   \n",
       "int_rate                    -0.387204     0.078197  1.000000   0.081992   \n",
       "loan_amnt                    0.145427     0.956720  0.081992   1.000000   \n",
       "last_pymnt_amnt              0.103165     0.397907  0.099688   0.483553   \n",
       "avg_cur_bal                  0.148058     0.226755 -0.097410   0.249609   \n",
       "acc_open_past_24mths        -0.110641     0.007955  0.225447  -0.002055   \n",
       "\n",
       "                      last_pymnt_amnt  avg_cur_bal  acc_open_past_24mths  \n",
       "charged_off                 -0.319081    -0.071977              0.105989  \n",
       "funded_amnt                  0.483553     0.249609             -0.002055  \n",
       "annual_inc                   0.199016     0.372657              0.041771  \n",
       "dti                         -0.048400    -0.139358              0.165314  \n",
       "fico_range_high              0.103165     0.148058             -0.110641  \n",
       "installment                  0.397907     0.226755              0.007955  \n",
       "int_rate                     0.099688    -0.097410              0.225447  \n",
       "loan_amnt                    0.483553     0.249609             -0.002055  \n",
       "last_pymnt_amnt              1.000000     0.178699              0.036069  \n",
       "avg_cur_bal                  0.178699     1.000000             -0.100472  \n",
       "acc_open_past_24mths         0.036069    -0.100472              1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c55fdf11-ddf6-405d-ae69-85f49b38d2e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['annual_inc', 'avg_cur_bal', 'funded_amnt', 'installment', 'loan_amnt']\n"
     ]
    }
   ],
   "source": [
    "corr_chagedoff=abs(corr['charged_off'])\n",
    "drop_list_corr=sorted(list(corr_chagedoff[corr_chagedoff<0.10].index))\n",
    "print(drop_list_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e132ab37-0b5a-4fb2-b7f4-cc7c9d9dab64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86138, 11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(labels=drop_list_corr, axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0531fcc6-0e25-4721-8fbc-a0cd0544160a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    67507\n",
       " 60 months    18631\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0ca94ea-b66d-4985-9a40-692bf8707680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term\n",
       "36    0.141526\n",
       "60    0.354356\n",
       "Name: charged_off, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['term']=df['term'].map(lambda x: 36 if '36 months' in x else 60)\n",
    "df.groupby('term')['charged_off'].value_counts(normalize=True).loc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "14c9ee2e-a015-412b-83ef-b25a0b942fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='grade'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chargeoff_rate_g=df.groupby('grade')['charged_off'].value_counts(normalize=True).loc[:,1]\n",
    "sns.barplot(x=chargeoff_rate_g.index, y=chargeoff_rate_g.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ce9235cb-f4ca-47f8-954e-62dfdcacb157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sub_grade'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chargeoff_rate_sg=df.groupby('sub_grade')['charged_off'].value_counts(normalize=True).loc[:,1]\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(x=chargeoff_rate_sg.index, y=chargeoff_rate_sg.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76e62f24-88f4-4e6e-9f0f-a9af3d92df2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86138, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['grade'], axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ef7c520f-e2c5-4bc0-81d5-042671cec2d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     86138\n",
       "unique       35\n",
       "top          C1\n",
       "freq       5217\n",
       "Name: sub_grade, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub_grade.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e89e80fb-1e9b-4006-b6cb-dad3a63f1cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['earliest_cr_line', 'home_ownership', 'sub_grade']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_mask=df.dtypes==object\n",
    "\n",
    "# Filter categorical columns using the mask and turn them into a list:\n",
    "cat_cols=df.columns[cat_mask].tolist()\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "79c5497b-73e4-45d8-98bf-dc162ae929f6",
   "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>charged_off</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>term</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>home_ownership_code</th>\n",
       "      <th>sub_grade_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "      <td>86138.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.187559</td>\n",
       "      <td>18.532747</td>\n",
       "      <td>696.463024</td>\n",
       "      <td>13.002360</td>\n",
       "      <td>41.191019</td>\n",
       "      <td>4757.453184</td>\n",
       "      <td>4.594732</td>\n",
       "      <td>1.925318</td>\n",
       "      <td>10.747127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.390362</td>\n",
       "      <td>8.538247</td>\n",
       "      <td>29.731848</td>\n",
       "      <td>4.397419</td>\n",
       "      <td>9.881240</td>\n",
       "      <td>6466.767327</td>\n",
       "      <td>3.070996</td>\n",
       "      <td>0.943402</td>\n",
       "      <td>6.678662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.070000</td>\n",
       "      <td>674.000000</td>\n",
       "      <td>9.490000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>358.522500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>12.990000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1241.230000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>709.000000</td>\n",
       "      <td>15.610000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>7303.205000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.990000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>26.060000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>36234.440000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        charged_off           dti  fico_range_high      int_rate  \\\n",
       "count  86138.000000  86138.000000     86138.000000  86138.000000   \n",
       "mean       0.187559     18.532747       696.463024     13.002360   \n",
       "std        0.390362      8.538247        29.731848      4.397419   \n",
       "min        0.000000      0.000000       664.000000      6.000000   \n",
       "25%        0.000000     12.070000       674.000000      9.490000   \n",
       "50%        0.000000     17.950000       689.000000     12.990000   \n",
       "75%        0.000000     24.500000       709.000000     15.610000   \n",
       "max        1.000000     39.990000       850.000000     26.060000   \n",
       "\n",
       "               term  last_pymnt_amnt  acc_open_past_24mths  \\\n",
       "count  86138.000000     86138.000000          86138.000000   \n",
       "mean      41.191019      4757.453184              4.594732   \n",
       "std        9.881240      6466.767327              3.070996   \n",
       "min       36.000000         0.000000              0.000000   \n",
       "25%       36.000000       358.522500              2.000000   \n",
       "50%       36.000000      1241.230000              4.000000   \n",
       "75%       36.000000      7303.205000              6.000000   \n",
       "max       60.000000     36234.440000             53.000000   \n",
       "\n",
       "       home_ownership_code  sub_grade_code  \n",
       "count         86138.000000    86138.000000  \n",
       "mean              1.925318       10.747127  \n",
       "std               0.943402        6.678662  \n",
       "min               0.000000        0.000000  \n",
       "25%               1.000000        6.000000  \n",
       "50%               2.000000       10.000000  \n",
       "75%               3.000000       15.000000  \n",
       "max               3.000000       34.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['home_ownership_code']=LabelEncoder().fit_transform(df.home_ownership)\n",
    "df['sub_grade_code']=LabelEncoder().fit_transform(df.sub_grade)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "643f1823-99ed-43a7-9e04-e1e1f4603185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86138, 9)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(cat_cols, axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "34c1f9d6-557a-4623-b654-a42de4857332",
   "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>charged_off</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>term</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>home_ownership_code</th>\n",
       "      <th>sub_grade_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12.03</td>\n",
       "      <td>754</td>\n",
       "      <td>12.39</td>\n",
       "      <td>60</td>\n",
       "      <td>12017.81</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14.92</td>\n",
       "      <td>714</td>\n",
       "      <td>6.99</td>\n",
       "      <td>36</td>\n",
       "      <td>321.08</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>18.49</td>\n",
       "      <td>689</td>\n",
       "      <td>15.59</td>\n",
       "      <td>60</td>\n",
       "      <td>17813.19</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>34.81</td>\n",
       "      <td>689</td>\n",
       "      <td>13.66</td>\n",
       "      <td>36</td>\n",
       "      <td>17.70</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>25.81</td>\n",
       "      <td>684</td>\n",
       "      <td>13.66</td>\n",
       "      <td>36</td>\n",
       "      <td>9338.58</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   charged_off    dti  fico_range_high  int_rate  term  last_pymnt_amnt  \\\n",
       "0            0  12.03              754     12.39    60         12017.81   \n",
       "1            1  14.92              714      6.99    36           321.08   \n",
       "2            0  18.49              689     15.59    60         17813.19   \n",
       "4            1  34.81              689     13.66    36            17.70   \n",
       "5            0  25.81              684     13.66    36          9338.58   \n",
       "\n",
       "   acc_open_past_24mths  home_ownership_code  sub_grade_code  \n",
       "0                     5                    3              10  \n",
       "1                     7                    1               2  \n",
       "2                     4                    3              15  \n",
       "4                     6                    3              12  \n",
       "5                     8                    3              12  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a036846f-2b21-4d03-b521-17b34a63e476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1681     0\n",
       "55818    0\n",
       "21667    1\n",
       "Name: charged_off, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['charged_off']\n",
    "x = df.loc[:, df.columns != 'charged_off']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=666)\n",
    "y_test.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d26b0c01-ec94-43c8-ae16-3fa82ad14b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, class_weight='balanced', random_state=666)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC = SVC(kernel='rbf', C=10, class_weight='balanced', random_state=666) \n",
    "SVC.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7c8776cc-7b9b-4100-97bd-e51294563f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fa9405bb0a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_estimator(SVC, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "771ac279-77e6-4f08-a8f7-cbf5a694097e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    69982\n",
       "1    16156\n",
       "Name: charged_off, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['charged_off'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1f4ab5c-1a22-4a08-8d5e-71141fc0b303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 9)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0 = df[df['charged_off']==0]\n",
    "y1 = df[df['charged_off']==1]\n",
    "subset_y0 = y0.sample(n=15000)\n",
    "subset_y1 = y1.sample(n=15000)\n",
    "df2 = pd.concat([subset_y0, subset_y1])\n",
    "df2 = df2.sample(frac=1).reset_index(drop=True)\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6d9bf9d-48d6-4bf9-a237-f439287b8183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21149    1\n",
       "9916     1\n",
       "25042    1\n",
       "Name: charged_off, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the balanced sample:\n",
    "y = df2['charged_off']\n",
    "x = df2.loc[:, df2.columns != 'charged_off']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=666)\n",
    "y_test.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "361cb9a4-c8ea-4474-a5d2-6fa3125fd5f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB Accuracy Score = 0.8574666666666667\n",
      "GB Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.82      0.85      3682\n",
      "           1       0.84      0.89      0.86      3818\n",
      "\n",
      "    accuracy                           0.86      7500\n",
      "   macro avg       0.86      0.86      0.86      7500\n",
      "weighted avg       0.86      0.86      0.86      7500\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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/QY5Dtmrp2PZARfWurNM1zijN9dY7v75M7bpXacBzebI0+b80mNVXn7fRZXFlTvsu61Cu/NzzLxcMsVarXWSlTp043UY7sDdUNTUWXZ304wTCNm0rFdepRHu/aN0gccPzjB9WGVzsZrTwhKDJWgbh4eHy9vY+qxqQl5d3VtXgDH9/f/n7k427I+OFMHW4qVytomtVU+alA++10rFtgfrFa8dksUg9xxfqs1fayNqxRtaONfpsURv5BBrqfFuJJKn4Ox8dWBei2BvKFRBmU9lxH+16tbW8Awx16H96MmLZcW+9++vL1CqmVsm/PaHKUz9WHILaMbEQjWvt3zvqT69laMzd3+rTD6PU5aoiDbs9WwtmXyVJCgis1V2TDug/H0Xq1Al/RUZXaPxD36i40FdbN57+WVRe5qsP3mmv+1K/VkmRr0qKfDUhdZ+OfBuirO3hrt4elxCeduhakyUEfn5+SkxM1IYNG3T77bc79m/YsEGjRo1qqrBavIoTPvroN5Eqy/ORX4hNba+s1i9eO6bYG06XT6+eVKjaKi99Oqudqoq8FNGrSiOWHnPcg8Db31DOzgB9scyqqmJvBbatVcy1lbp99VEFtT39ZZ+9OUhFR/xUdMRPK26Md3r/B/cfaNwPDNPb/1VrPfOba3T3Q/v0q/sO6PixQL06t5s2pp+eP2C3WxTXqUS33Pq9gkNqVHDCX19kttVzT1ytivIff0QumddNdpuXfjs7S34BNn2+o62efjqRexCgxbAYRtNNiVm9erVSUlL0yiuvKDk5Wa+++qqWLFmiPXv2KC4u7oLnFxcXy2q1asWuBAWFeF9wPNAcLel/U1OHADSYWnuVPjy2WEVFRQoNDW2Q9zjzXXH7hnvkG3zxt0utKavWmsFLGzTWptSkyw7Hjh2rkydP6g9/+INycnLUo0cPrVu3rk7JAAAA9UHLwLUmv3Xx5MmTNXny5KYOAwAAU2vyhAAAgMbAswxcIyEAAJgCLQPXWB0OAACoEAAAzIEKgWskBAAAUyAhcI2WAQAAoEIAADAHKgSukRAAAEzBkHtLB1v6k65pGQAATMGtRx9fRHUhLS1N1157rUJCQhQREaHRo0dr3759TmPuvvtuWSwWp61v375OY6qqqjRlyhSFh4crODhYI0eO1NGjR53GFBQUKCUlRVarVVarVSkpKSosLKxXvCQEAAA0gE2bNumhhx5SRkaGNmzYoNraWg0ZMkRlZc6P4x42bJhycnIc27p165yOp6amas2aNVq1apU2b96s0tJSjRgxQjbbj0+PHTdunLKyspSenq709HRlZWUpJSWlXvHSMgAAmEJjzyFIT093er106VJFREQoMzNTN93040PL/P39FRUVdc5rFBUV6bXXXtOKFSs0aNAgSdLrr7+u2NhYffjhhxo6dKj27t2r9PR0ZWRkKCkpSZK0ZMkSJScna9++feratWud4qVCAAAwBU+1DIqLi522qqqqOr1/UVGRJCksLMxp/8aNGxUREaEuXbpo4sSJysvLcxzLzMxUTU2NhgwZ4tgXExOjHj16aMuWLZKkrVu3ymq1OpIBSerbt6+sVqtjTF2QEAAAUA+xsbGOXr3ValVaWtoFzzEMQ9OmTdMNN9ygHj16OPYPHz5cK1eu1EcffaQXXnhBO3bs0C233OJIMnJzc+Xn56c2bdo4XS8yMlK5ubmOMREREWe9Z0REhGNMXdAyAACYgqdaBtnZ2QoNDXXs9/f3v+C5Dz/8sL744gtt3rzZaf/YsWMdf+7Ro4f69OmjuLg4vffee7rjjjvOez3DMGSx/PhZfvrn8425ECoEAABTMAyL25skhYaGOm0XSgimTJmid999Vx9//LHat2/vcmx0dLTi4uK0f/9+SVJUVJSqq6tVUFDgNC4vL0+RkZGOMcePHz/rWvn5+Y4xdUFCAABAAzAMQw8//LDefvttffTRR4qPj7/gOSdPnlR2draio6MlSYmJifL19dWGDRscY3JycrR7927169dPkpScnKyioiJt377dMWbbtm0qKipyjKkLWgYAAFOwy+LWjYnqe+5DDz2kN954Q++8845CQkIc/Xyr1arAwECVlpZq1qxZ+uUvf6no6GgdPnxYTzzxhMLDw3X77bc7xk6YMEHTp09X27ZtFRYWphkzZighIcGx6qBbt24aNmyYJk6cqMWLF0uSJk2apBEjRtR5hYFEQgAAMInGXna4aNEiSVL//v2d9i9dulR33323vL299eWXX+pvf/ubCgsLFR0drQEDBmj16tUKCQlxjJ83b558fHw0ZswYVVRUaODAgVq2bJm8vb0dY1auXKmpU6c6ViOMHDlSCxcurFe8JAQAADQAw3B9s+PAwECtX7/+gtcJCAjQggULtGDBgvOOCQsL0+uvv17vGH+KhAAAYAo/nRh4see3ZCQEAABT4GmHrpEQAABMgQqBayw7BAAAVAgAAOZguNkyaOkVAhICAIApGJIuMPH/gue3ZLQMAAAAFQIAgDnYZZGlEe9U2NyQEAAATIFVBq7RMgAAAFQIAADmYDcssnBjovMiIQAAmIJhuLnKoIUvM6BlAAAAqBAAAMyBSYWukRAAAEyBhMA1EgIAgCkwqdA15hAAAAAqBAAAc2CVgWskBAAAUzidELgzh8CDwVyCaBkAAAAqBAAAc2CVgWskBAAAUzB+2Nw5vyWjZQAAAKgQAADMgZaBayQEAABzoGfgEgkBAMAc3KwQqIVXCJhDAAAAqBAAAMyBOxW6RkIAADAFJhW6RssAAABQIQAAmIRhcW9iYAuvEJAQAABMgTkErtEyAAAAVAgAACbBjYlcIiEAAJgCqwxcq1NC8NJLL9X5glOnTr3oYAAAQNOoU0Iwb968Ol3MYrGQEAAALl0tvOzvjjolBIcOHWroOAAAaFC0DFy76FUG1dXV2rdvn2praz0ZDwAADcPwwNaC1TshKC8v14QJExQUFKSrrrpK3333naTTcweee+45jwcIAAAaXr0Tgscff1yff/65Nm7cqICAAMf+QYMGafXq1R4NDgAAz7F4YGu56r3scO3atVq9erX69u0ri+XHv5zu3bvr22+/9WhwAAB4DPchcKneFYL8/HxFRESctb+srMwpQQAAAM1HvROCa6+9Vu+9957j9ZkkYMmSJUpOTvZcZAAAeBKTCl2qd8sgLS1Nw4YN01dffaXa2lq9+OKL2rNnj7Zu3apNmzY1RIwAALiPpx26VO8KQb9+/fSf//xH5eXl6tSpkz744ANFRkZq69atSkxMbIgYAQBAA7uoZxkkJCRo+fLlno4FAIAGw+OPXbuohMBms2nNmjXau3evLBaLunXrplGjRsnHh2clAQAuUawycKne3+C7d+/WqFGjlJubq65du0qSvvnmG7Vr107vvvuuEhISPB4kAABoWPWeQ3Dffffpqquu0tGjR/XZZ5/ps88+U3Z2tnr27KlJkyY1RIwAALjvzKRCd7YWrN4Vgs8//1w7d+5UmzZtHPvatGmjZ599Vtdee61HgwMAwFMsxunNnfNbsnpXCLp27arjx4+ftT8vL09XXHGFR4ICAMDjGvk+BGlpabr22msVEhKiiIgIjR49Wvv27XMOyTA0a9YsxcTEKDAwUP3799eePXucxlRVVWnKlCkKDw9XcHCwRo4cqaNHjzqNKSgoUEpKiqxWq6xWq1JSUlRYWFiveOuUEBQXFzu22bNna+rUqfrHP/6ho0eP6ujRo/rHP/6h1NRUzZkzp15vDgBAS7Vp0yY99NBDysjI0IYNG1RbW6shQ4aorKzMMeb555/X3LlztXDhQu3YsUNRUVEaPHiwSkpKHGNSU1O1Zs0arVq1Sps3b1ZpaalGjBghm83mGDNu3DhlZWUpPT1d6enpysrKUkpKSr3itRjGhRdSeHl5Od2W+MwpZ/b99PVPA2xoxcXFslqtWrErQUEh3o32vkBjWtL/pqYOAWgwtfYqfXhssYqKihQaGtog73HmuyJ23h/lFRhw4RPOw15Rqez/+f1Fx3rm1v+bNm3STTfdJMMwFBMTo9TUVD322GOSTlcDIiMjNWfOHN1///0qKipSu3bttGLFCo0dO1aSdOzYMcXGxmrdunUaOnSo9u7dq+7duysjI0NJSUmSpIyMDCUnJ+vrr792LAC4kDrNIfj444/r/cEBALikeGjZYXFxsdNuf39/+fv7X/D0oqIiSVJYWJgk6dChQ8rNzdWQIUOcrnXzzTdry5Ytuv/++5WZmamamhqnMTExMerRo4e2bNmioUOHauvWrbJarY5kQJL69u0rq9WqLVu2eDYhuPnmm+t0MQAAWrrY2Fin10899ZRmzZrl8hzDMDRt2jTdcMMN6tGjhyQpNzdXkhQZGek0NjIyUkeOHHGM8fPzc5rIf2bMmfNzc3PP+dDBiIgIx5i6uOg7CZWXl+u7775TdXW10/6ePXte7CUBAGg4HqoQZGdnO7UM6lIdePjhh/XFF19o8+bNZx37+ZOCDcO44NODfz7mXOPrcp2fqndCkJ+fr3vuuUfvv//+OY835hwCAADqzEMJQWhoaL3mEEyZMkXvvvuuPvnkE7Vv396xPyoqStLp3/Cjo6Md+/Py8hxVg6ioKFVXV6ugoMCpSpCXl6d+/fo5xpxr9V9+fv5Z1QdX6r3sMDU1VQUFBcrIyFBgYKDS09O1fPlyde7cWe+++259LwcAQItkGIYefvhhvf322/roo48UHx/vdDw+Pl5RUVHasGGDY191dbU2bdrk+LJPTEyUr6+v05icnBzt3r3bMSY5OVlFRUXavn27Y8y2bdtUVFTkGFMX9a4QfPTRR3rnnXd07bXXysvLS3FxcRo8eLBCQ0OVlpamX/ziF/W9JAAADa+RH3/80EMP6Y033tA777yjkJAQRz/farUqMDBQFotFqampmj17tjp37qzOnTtr9uzZCgoK0rhx4xxjJ0yYoOnTp6tt27YKCwvTjBkzlJCQoEGDBkmSunXrpmHDhmnixIlavHixJGnSpEkaMWJEnScUSheREJSVlTkmL4SFhSk/P19dunRRQkKCPvvss/peDgCARtHYdypctGiRJKl///5O+5cuXaq7775bkvToo4+qoqJCkydPVkFBgZKSkvTBBx8oJCTEMX7evHny8fHRmDFjVFFRoYEDB2rZsmXy9v5xuf3KlSs1depUx2qEkSNHauHChfWKt94JQdeuXbVv3z517NhRV199tRYvXqyOHTvqlVdeceqBAABgZnW4zY8sFotmzZrlcpVCQECAFixYoAULFpx3TFhYmF5//fWLCdOh3glBamqqcnJyJJ1eajF06FCtXLlSfn5+WrZsmVvBAADQYHj8sUv1Tgjuuusux5979+6tw4cP6+uvv1aHDh0UHh7u0eAAAEDjuOj7EJwRFBSka665xhOxAADQYCxycw6BxyK5NNUpIZg2bVqdLzh37tyLDgYAADSNOiUEu3btqtPF6nNHJE96rffl8rH4Nsl7Aw1t/bF1TR0C0GCKS+xq06WR3qyRlx02NzzcCABgDkwqdKnedyoEAAAtj9uTCgEAaBaoELhEQgAAMIXGvlNhc0PLAAAAUCEAAJgELQOXLqpCsGLFCl1//fWKiYnRkSNHJEnz58/XO++849HgAADwGMMDWwtW74Rg0aJFmjZtmm699VYVFhbKZrNJklq3bq358+d7Oj4AANAI6p0QLFiwQEuWLNHMmTOdHr3Yp08fffnllx4NDgAATzkzqdCdrSWr9xyCQ4cOqXfv3mft9/f3V1lZmUeCAgDA47hToUv1rhDEx8crKyvrrP3vv/++unfv7omYAADwPOYQuFTvCsFvfvMbPfTQQ6qsrJRhGNq+fbv+/ve/Ky0tTX/5y18aIkYAANDA6p0Q3HPPPaqtrdWjjz6q8vJyjRs3TpdddplefPFF3XnnnQ0RIwAAbuPGRK5d1H0IJk6cqIkTJ+rEiROy2+2KiIjwdFwAAHgW9yFwya0bE4WHh3sqDgAA0ITqnRDEx8fLYjn/TMuDBw+6FRAAAA3C3aWDVAicpaamOr2uqanRrl27lJ6ert/85jeeigsAAM+iZeBSvROCRx555Jz7//znP2vnzp1uBwQAABqfx552OHz4cL311lueuhwAAJ7FfQhc8tjTDv/xj38oLCzMU5cDAMCjWHboWr0Tgt69eztNKjQMQ7m5ucrPz9fLL7/s0eAAAEDjqHdCMHr0aKfXXl5eateunfr3768rr7zSU3EBAIBGVK+EoLa2Vh07dtTQoUMVFRXVUDEBAOB5rDJwqV6TCn18fPTggw+qqqqqoeIBAKBB8Phj1+q9yiApKUm7du1qiFgAAEATqfccgsmTJ2v69Ok6evSoEhMTFRwc7HS8Z8+eHgsOAACPauG/5bujzgnBvffeq/nz52vs2LGSpKlTpzqOWSwWGYYhi8Uim83m+SgBAHAXcwhcqnNCsHz5cj333HM6dOhQQ8YDAACaQJ0TAsM4nRrFxcU1WDAAADQUbkzkWr3mELh6yiEAAJc0WgYu1Ssh6NKlywWTglOnTrkVEAAAaHz1SgiefvppWa3WhooFAIAGQ8vAtXolBHfeeaciIiIaKhYAABoOLQOX6nxjIuYPAADQctV7lQEAAM0SFQKX6pwQ2O32howDAIAGxRwC1+p962IAAJolKgQu1fvhRgAAoOWhQgAAMAcqBC6REAAATIE5BK7RMgAAAFQIAAAmQcvAJRICAIAp0DJwjZYBAACgQgAAMAlaBi6REAAAzIGEwCVaBgAANIBPPvlEt912m2JiYmSxWLR27Vqn43fffbcsFovT1rdvX6cxVVVVmjJlisLDwxUcHKyRI0fq6NGjTmMKCgqUkpIiq9Uqq9WqlJQUFRYW1jteEgIAgClYPLDVR1lZmXr16qWFCxeed8ywYcOUk5Pj2NatW+d0PDU1VWvWrNGqVau0efNmlZaWasSIEbLZbI4x48aNU1ZWltLT05Wenq6srCylpKTUM1paBgAAs2jklsHw4cM1fPhwl2P8/f0VFRV1zmNFRUV67bXXtGLFCg0aNEiS9Prrrys2NlYffvihhg4dqr179yo9PV0ZGRlKSkqSJC1ZskTJycnat2+funbtWud4qRAAAEzhzLJDdzZJKi4udtqqqqouOqaNGzcqIiJCXbp00cSJE5WXl+c4lpmZqZqaGg0ZMsSxLyYmRj169NCWLVskSVu3bpXVanUkA5LUt29fWa1Wx5i6IiEAAKAeYmNjHf16q9WqtLS0i7rO8OHDtXLlSn300Ud64YUXtGPHDt1yyy2OBCM3N1d+fn5q06aN03mRkZHKzc11jImIiDjr2hEREY4xdUXLAABgDh5qGWRnZys0NNSx29/f/6IuN3bsWMefe/TooT59+iguLk7vvfee7rjjjvOHYRiyWH6c0fDTP59vTF1QIQAAmIfhxvaD0NBQp+1iE4Kfi46OVlxcnPbv3y9JioqKUnV1tQoKCpzG5eXlKTIy0jHm+PHjZ10rPz/fMaauSAgAALgEnDx5UtnZ2YqOjpYkJSYmytfXVxs2bHCMycnJ0e7du9WvXz9JUnJysoqKirR9+3bHmG3btqmoqMgxpq5oGQAATKGxn2VQWlqqAwcOOF4fOnRIWVlZCgsLU1hYmGbNmqVf/vKXio6O1uHDh/XEE08oPDxct99+uyTJarVqwoQJmj59utq2bauwsDDNmDFDCQkJjlUH3bp107BhwzRx4kQtXrxYkjRp0iSNGDGiXisMJBICAIBZNPKyw507d2rAgAGO19OmTZMkjR8/XosWLdKXX36pv/3tbyosLFR0dLQGDBig1atXKyQkxHHOvHnz5OPjozFjxqiiokIDBw7UsmXL5O3t7RizcuVKTZ061bEaYeTIkS7vfXA+FsMwmu3NGIuLi2W1WtVfo+Rj8W3qcIAGsf5YVlOHADSY4hK72nQ5qKKiIqeJeh59jx++K3pMnC1vv4CLvo6tulK7lzzRoLE2JSoEAABT4PHHrpEQAADMgYcbucQqAwAAQIUAAGAOtAxcIyEAAJgDLQOXSAgAAOZAQuAScwgAAAAVAgCAOTCHwDUSAgCAOdAycImWAQAAoEIAADAHi2HI4sbd+t05tzkgIQAAmAMtA5doGQAAACoEAABzYJWBayQEAABzoGXgEi0DAABAhQAAYA60DFwjIQAAmAMtA5dICAAApkCFwDXmEAAAACoEAACToGXgEgkBAMA0WnrZ3x20DAAAABUCAIBJGMbpzZ3zWzASAgCAKbDKwDVaBgAAgAoBAMAkWGXgEgkBAMAULPbTmzvnt2S0DAAAABUCOBv78HHd+0Su1iwJ1ytPXSZJun54oW5NOanOPStkDbPpwcFddHBPoNN50XFVmvjkMV11XZl8/QxlfhyiP//uMhWe8G2KjwET++fytnrvb+E6nu0nSYrrWqm7/idX195SIkla8acobXyntfKP+crXz9AVCRW657c5uvKacsc1jh3205I/xGjP9laqqbYocUCxHnrme7VpV+sYs/+LQL32bIy++TxIXt6Gbri1UPfPOqbA4Bb+a2RzRsvAJSoEcOjSq1y3/vqUDu4JcNofEGTXVzuC9dfZ0ec8zz/Qptl/PyjDsOix/+qkaaOukI+foT8sPyRLS5+Wi0tOu+ga3fvEMS14/xsteP8b9bq+RLPuidfhfaf/XV92eaUeevaoFn+0Ty+sPaCo2Go9/qtOKjzpLUmqLPfSE7/qJItFmvN/BzT3nf2qrfbSk+PjZf/hu/5kro9+e2cnxcRX6cV/faNnV36rI/sC9KfUDk31sVEHZ1YZuLO1ZE2aEHzyySe67bbbFBMTI4vForVr1zZlOKYWEGTTYwuPaP5v2qukyNvp2L/fCtPKeVHa9UnIOc+96rpyRcZW64XUWB3+OlCHvw7UC/8Tq669K3T1DaWNET7g0HdIsa4bWKL2narUvlOV7vltrgKC7fo6M0iSdMsdhbrmplJFx1WrY9dKTZr1vcpLvHXoq9NVrz3bg3U820/T53+n+G6Viu9WqenzvtM3WcHK2txKkrTtQ6t8fAw9PPuoYq+oUterK/Tw7O+1+b3W+v6QX5N9dlzAmfsQuLO1YE2aEJSVlalXr15auHBhU4YBSQ/P/l7b/x2qXZ+e+0vfFV8/u2RINdUWx77qKi/ZbNJV15V5MkygXmw2aePa1qoq91K3Pmf/W6yptmjd620VHGrT5d0rHPtkkXz9fvzh7+dvl5eXoT3bTycENVUW+fga8vrJT1C/gNPlgzNjgOamSecQDB8+XMOHD6/z+KqqKlVVVTleFxcXN0RYpnPzqAJdkVChKbd2vqjzv84MVmW5lybMzNHS56IlGbrvdzny9pbCImo8GyxQB4f2Bij1ts6qrvJSYLBdT752SHFdfvzZkbEhVGkPxqmqwkthkTVKW3VA1rY2SdKViWUKCLLrtWdjdM9vj0my6C/PRMtut+hU3ukfmb1uKNXipy/T/73cTqPvO6HKcq8f/u3LMQaXHm5M5FqzmkOQlpYmq9Xq2GJjY5s6pGavXUy1HvzDMT0/pYNqqi7un0PRKR89c39HJQ0u1tr9X2rNvt0KCrFr/xeBstssF74A4GHtO1Xp5Q379OK/vtGI/z6hPz0SpyPf+DuOX319qV7esE/z3t2vPv1L9Oz9HVV44vQXeeu2Nv1u8WFt2xCq0Z176vauCSov8dYVCeXy+qGb1rFrpWbMP6K3FkdoZKee+tXVVym6Q7XatKtxqhrgEmN4YGvBmlUq+/jjj2vatGmO18XFxSQFbrqiZ4XatKvVwvRvHPu8faSEvmUaec8JjejYU3b7hb/UP9sUonv6dVNoWK1stRaVFXvr71l7lJtNPxWNz9fP0GXx1ZKkLr0qtC8rSGv/0k6PPH9U0umJspfFV+uy+Gp1SyzXPdd3U/rfw3TnlDxJUmL/Ei3buldFJ73l7SO1stp0Z6+rFBX7Y5XhljsKdcsdhSrI91FAkF0Wi/T2q+0U1aHq7ICAZqBZJQT+/v7y9/e/8EDUWdanrTRpQBenfdPnZSv7QIDe/HO7OiUDP1V86oeS6vUlah1eq4wPQj0WK+COmurz/+puGDpnhexMGyFrcysVnvBR3yFntynPLEVc//cw+frbdc1NTKS9VNEycK1ZJQTwvIoybx3Z53xPgcpyL5UU/Lg/pHWt2l1Wo7aRp+cDxHaqlCQV5PmoIP/0fQaGjD2l7/b7q+ikj7olluvBP3yvNa+209FvnZcwAg3tr2nRuvaWYrWLqVFFqZc2vtNaX2xppWdWfqvKci+98WKkkocUKSyyRsWnfPSv5eE6keOrG28rdFxj/aowdehcKWvbWu3NDNaiJy/T7ZPyFXvFj7/9v/PXcHXvU6bAYLs++yREf/ljjO594phaWW1N8KlRJzzt0CUSAlxQ3yHFmjE/2/H6iVe+kySteCFSr78QJUlq36lS9zyeo5DWNh3P9tXfX4rU26+GN0m8MLfCfB/975Q4ncrzUVCITfHdKvXMym+VeHOpqistOnrAX3/8v44qPuWjkDY2delVrhfW7FfHrpWOaxz91l9L06JVUuityNhq/Wrqcd0xKd/pffZlBWnFC1GqLPNS+yuqNPX5bA36fwWN/XEBj7EYRtOlPKWlpTpw4IAkqXfv3po7d64GDBigsLAwdehw4Rt8FBcXy2q1qr9GycfCHfHQMq0/ltXUIQANprjErjZdDqqoqEihoQ3TYjzzXZE8/A/y8b34qmVtTaW2vv9kg8balJq0QrBz504NGDDA8frMhMHx48dr2bJlTRQVAKBF4tbFLjVpQtC/f381YYECAAD8gDkEAABTYJWBayQEAABzsBunN3fOb8FICAAA5sAcApe4ySYAAKBCAAAwB4vcnEPgsUguTSQEAABz4E6FLtEyAAAAVAgAAObAskPXSAgAAObAKgOXaBkAANAAPvnkE912222KiYmRxWLR2rVrnY4bhqFZs2YpJiZGgYGB6t+/v/bs2eM0pqqqSlOmTFF4eLiCg4M1cuRIHT161GlMQUGBUlJSZLVaZbValZKSosLCwnrHS0IAADAFi2G4vdVHWVmZevXqpYULF57z+PPPP6+5c+dq4cKF2rFjh6KiojR48GCVlJQ4xqSmpmrNmjVatWqVNm/erNLSUo0YMUI224+P2R43bpyysrKUnp6u9PR0ZWVlKSUlpd5/P7QMAADmYP9hc+f8ehg+fLiGDx9+zmOGYWj+/PmaOXOm7rjjDknS8uXLFRkZqTfeeEP333+/ioqK9Nprr2nFihUaNGiQJOn1119XbGysPvzwQw0dOlR79+5Venq6MjIylJSUJElasmSJkpOTtW/fPnXt2rXO8VIhAACgHoqLi522qqqqel/j0KFDys3N1ZAhQxz7/P39dfPNN2vLli2SpMzMTNXU1DiNiYmJUY8ePRxjtm7dKqvV6kgGJKlv376yWq2OMXVFQgAAMAVPtQxiY2Md/Xqr1aq0tLR6x5KbmytJioyMdNofGRnpOJabmys/Pz+1adPG5ZiIiIizrh8REeEYU1e0DAAA5uChVQbZ2dkKDQ117Pb397/oS1oszvc/NAzjrH1nhfGzMecaX5fr/BwVAgCAOZy5U6E7m6TQ0FCn7WISgqioKEk667f4vLw8R9UgKipK1dXVKigocDnm+PHjZ10/Pz//rOrDhZAQAADQyOLj4xUVFaUNGzY49lVXV2vTpk3q16+fJCkxMVG+vr5OY3JycrR7927HmOTkZBUVFWn79u2OMdu2bVNRUZFjTF3RMgAAmEJj36mwtLRUBw4ccLw+dOiQsrKyFBYWpg4dOig1NVWzZ89W586d1blzZ82ePVtBQUEaN26cJMlqtWrChAmaPn262rZtq7CwMM2YMUMJCQmOVQfdunXTsGHDNHHiRC1evFiSNGnSJI0YMaJeKwwkEgIAgFk08sONdu7cqQEDBjheT5s2TZI0fvx4LVu2TI8++qgqKio0efJkFRQUKCkpSR988IFCQkIc58ybN08+Pj4aM2aMKioqNHDgQC1btkze3t6OMStXrtTUqVMdqxFGjhx53nsfuGIxjOb7+Kbi4mJZrVb11yj5WHybOhygQaw/ltXUIQANprjErjZdDqqoqMhpop5H3+OH74qbk38nH5+Ai75ObW2lNm19pkFjbUpUCAAApmCxn97cOb8lIyEAAJhDI7cMmhtWGQAAACoEAACT4PHHLpEQAABM4WKeWPjz81syWgYAAIAKAQDAJJhU6BIJAQDAHAxJ7iwdbNn5AAkBAMAcmEPgGnMIAAAAFQIAgEkYcnMOgcciuSSREAAAzIFJhS7RMgAAAFQIAAAmYZdkcfP8FoyEAABgCqwycI2WAQAAoEIAADAJJhW6REIAADAHEgKXaBkAAAAqBAAAk6BC4BIJAQDAHFh26BIJAQDAFFh26BpzCAAAABUCAIBJMIfAJRICAIA52A3J4saXur1lJwS0DAAAABUCAIBJ0DJwiYQAAGASbiYEatkJAS0DAABAhQAAYBK0DFwiIQAAmIPdkFtlf1YZAACAlo4KAQDAHAz76c2d81swEgIAgDkwh8AlEgIAgDkwh8Al5hAAAAAqBAAAk6Bl4BIJAQDAHAy5mRB4LJJLEi0DAABAhQAAYBK0DFwiIQAAmIPdLsmNewnYW/Z9CGgZAAAAKgQAAJOgZeASCQEAwBxICFyiZQAAAKgQAABMglsXu0RCAAAwBcOwy3DjiYXunNsckBAAAMzBMNz7LZ85BAAAoKWjQgAAMAfDzTkELbxCQEIAADAHu12yuDEPoIXPIaBlAABAA5g1a5YsFovTFhUV5ThuGIZmzZqlmJgYBQYGqn///tqzZ4/TNaqqqjRlyhSFh4crODhYI0eO1NGjRxskXhICAIA5nLkxkTtbPV111VXKyclxbF9++aXj2PPPP6+5c+dq4cKF2rFjh6KiojR48GCVlJQ4xqSmpmrNmjVatWqVNm/erNLSUo0YMUI2m80jfyU/RcsAAGAKht0uw42WwcUsO/Tx8XGqCvx4LUPz58/XzJkzdccdd0iSli9frsjISL3xxhu6//77VVRUpNdee00rVqzQoEGDJEmvv/66YmNj9eGHH2ro0KEX/VnOhQoBAAD1UFxc7LRVVVWdd+z+/fsVExOj+Ph43XnnnTp48KAk6dChQ8rNzdWQIUMcY/39/XXzzTdry5YtkqTMzEzV1NQ4jYmJiVGPHj0cYzyJhAAAYA4eahnExsbKarU6trS0tHO+XVJSkv72t79p/fr1WrJkiXJzc9WvXz+dPHlSubm5kqTIyEincyIjIx3HcnNz5efnpzZt2px3jCfRMgAAmIPdkCzuLzvMzs5WaGioY7e/v/85hw8fPtzx54SEBCUnJ6tTp05avny5+vbtK0myWCw/ewvjrH1nh3HhMReDCgEAAPUQGhrqtJ0vIfi54OBgJSQkaP/+/Y55BT//TT8vL89RNYiKilJ1dbUKCgrOO8aTSAgAAOZgGKfvJXDRm3s3JqqqqtLevXsVHR2t+Ph4RUVFacOGDY7j1dXV2rRpk/r16ydJSkxMlK+vr9OYnJwc7d692zHGk2gZAABMwbAbMtxoGRj1TAhmzJih2267TR06dFBeXp6eeeYZFRcXa/z48bJYLEpNTdXs2bPVuXNnde7cWbNnz1ZQUJDGjRsnSbJarZowYYKmT5+utm3bKiwsTDNmzFBCQoJj1YEnkRAAAMzBsEtqvDsVHj16VL/61a904sQJtWvXTn379lVGRobi4uIkSY8++qgqKio0efJkFRQUKCkpSR988IFCQkIc15g3b558fHw0ZswYVVRUaODAgVq2bJm8vb0v/nOch8Wob8pzCSkuLpbValV/jZKPxbepwwEaxPpjWU0dAtBgikvsatPloIqKipwm6nn0PX74rhjgfYdb3xW1Ro0+tr3doLE2JSoEAABTaOyWQXNDQgAAMIdGbhk0N806ITiTrdWqxq0nWgKXsuKSlv1DCOZWXHr633dj/Pbt7ndFrWo8F8wlqFknBGceALFZ65o4EqDhtOnS1BEADa+kpERWq7VBru3n56eoqChtznX/uyIqKkp+fn4eiOrS06wnFdrtdh07dkwhISENctcmnK24uFixsbFn3akLaAn49934DMNQSUmJYmJi5OXVcLfGqaysVHV1tdvX8fPzU0BAgAciuvQ06wqBl5eX2rdv39RhmNKZO3QBLRH/vhtXQ1UGfiogIKDFfpF7CncqBAAAJAQAAICEAPXk7++vp556qs4P8wCaE/59w8ya9aRCAADgGVQIAAAACQEAACAhAAAAIiEAAAAiIUA9vPzyy4qPj1dAQIASExP16aefNnVIgEd88sknuu222xQTEyOLxaK1a9c2dUhAoyMhQJ2sXr1aqampmjlzpnbt2qUbb7xRw4cP13fffdfUoQFuKysrU69evbRw4cKmDgVoMiw7RJ0kJSXpmmuu0aJFixz7unXrptGjRystLa0JIwM8y2KxaM2aNRo9enRThwI0KioEuKDq6mplZmZqyJAhTvuHDBmiLVu2NFFUAABPIiHABZ04cUI2m02RkZFO+yMjI5Wbm9tEUQEAPImEAHX280dMG4bBY6cBoIUgIcAFhYeHy9vb+6xqQF5e3llVAwBA80RCgAvy8/NTYmKiNmzY4LR/w4YN6tevXxNFBQDwJJ+mDgDNw7Rp05SSkqI+ffooOTlZr776qr777js98MADTR0a4LbS0lIdOHDA8frQoUPKyspSWFiYOnTo0ISRAY2HZYeos5dfflnPP/+8cnJy1KNHD82bN0833XRTU4cFuG3jxo0aMGDAWfvHjx+vZcuWNX5AQBMgIQAAAMwhAAAAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAAAQCQEAABAJAQAAEAkBIDbZs2apauvvtrx+u6779bo0aMbPY7Dhw/LYrEoKyvrvGM6duyo+fPn1/may5YtU+vWrd2OzWKxaO3atW5fB0DDISFAi3T33XfLYrHIYrHI19dXl19+uWbMmKGysrIGf+8XX3yxzre7rcuXOAA0Bh5uhBZr2LBhWrp0qWpqavTpp5/qvvvuU1lZmRYtWnTW2JqaGvn6+nrkfa1Wq0euAwCNiQoBWix/f39FRUUpNjZW48aN01133eUoW58p8//1r3/V5ZdfLn9/fxmGoaKiIk2aNEkREREKDQ3VLbfcos8//9zpus8995wiIyMVEhKiCRMmqLKy0un4z1sGdrtdc+bM0RVXXCF/f3916NBBzz77rCQpPj5ektS7d29ZLBb179/fcd7SpUvVrVs3BQQE6Morr9TLL7/s9D7bt29X7969FRAQoD59+mjXrl31/juaO3euEhISFBwcrNjYWE2ePFmlpaVnjVu7dq26dOmigIAADR48WNnZ2U7H//nPfyoxMVEBAQG6/PLL9fTTT6u2trbe8QBoOiQEMI3AwEDV1NQ4Xh84cEBvvvmm3nrrLUfJ/he/+IVyc3O1bt06ZWZm6pprrtHAgQN16tQpSdKbb76pp556Ss8++6x27typ6Ojos76of+7xxx/XnDlz9Pvf/15fffWV3njjDUVGRko6/aUuSR9++KFycnL09ttvS5KWLFmimTNn6tlnn9XevXs1e/Zs/f73v9fy5cslSWVlZRoxYoS6du2qzMxMzZo1SzNmzKj334mXl5deeukl7d69W8uXL9dHH32kRx991GlMeXm5nn32WS1fvlz/+c9/VFxcrDvvvNNxfP369fr1r3+tqVOn6quvvtLixYu1bNkyR9IDoJkwgBZo/PjxxqhRoxyvt23bZrRt29YYM2aMYRiG8dRTTxm+vr5GXl6eY8y///1vIzQ01KisrHS6VqdOnYzFixcbhmEYycnJxgMPPOB0PCkpyejVq9c537u4uNjw9/c3lixZcs44Dx06ZEgydu3a5bQ/NjbWeOONN5z2/fGPfzSSk5MNwzCMxYsXG2FhYUZZWZnj+KJFi855rZ+Ki4sz5s2bd97jb775ptG2bVvH66VLlxqSjIyMDMe+vXv3GpKMbdu2GYZhGDfeeKMxe/Zsp+usWLHCiI6OdryWZKxZs+a87wug6TGHAC3Wv/71L7Vq1Uq1tbWqqanRqFGjtGDBAsfxuLg4tWvXzvE6MzNTpaWlatu2rdN1Kioq9O2330qS9u7dqwceeMDpeHJysj7++ONzxrB3715VVVVp4MCBdY47Pz9f2dnZmjBhgiZOnOjYX1tb65ifsHfvXvXq1UtBQUFOcdTXxx9/rNmzZ+urr75ScXGxamtrVVlZqbKyMgUHB0uSfHx81KdPH8c5V155pVq3bq29e/fquuuuU2Zmpnbs2OFUEbDZbKqsrFR5eblTjAAuXSQEaLEGDBigRYsWydfXVzExMWdNGjzzhXeG3W5XdHS0Nm7ceNa1LnbpXWBgYL3Psdvtkk63DZKSkpyOeXt7S5IMw7ioeH7qyJEjuvXWW/XAAw/oj3/8o8LCwrR582ZNmDDBqbUinV42+HNn9tntdj399NO64447zhoTEBDgdpwAGgcJAVqs4OBgXXHFFXUef8011yg3N1c+Pj7q2LHjOcd069ZNGRkZ+u///m/HvoyMjPNes3PnzgoMDNS///1v3XfffWcd9/Pzk3T6N+ozIiMjddlll+ngwYO66667znnd7t27a8WKFaqoqHAkHa7iOJedO3eqtrZWL7zwgry8Tk8nevPNN88aV1tbq507d+q6666TJO3bt0+FhYW68sorJZ3+e9u3b1+9/q4BXHpICIAfDBo0SMnJyRo9erTmzJmjrl276tixY1q3bp1Gjx6tPn366JFHHtH48ePVp08f3XDDDVq5cqX27Nmjyy+//JzXDAgI0GOPPaZHH31Ufn5+uv7665Wfn689e/ZowoQJioiIUGBgoNLT09W+fXsFBATIarVq1qxZmjp1qkJDQzV8+HBVVVVp586dKigo0LRp0zRu3DjNnDlTEyZM0O9+9zsdPnxYf/rTn+r1eTt16qTa2lotWLBAt912m/7zn//olVdeOWucr6+vpkyZopdeekm+vr56+OGH1bdvX0eC8OSTT2rEiBGKjY3Vf/3Xf8nLy0tffPGFvvzySz3zzDP1/z8CQJNglQHwA4vFonXr1ummm27Svffeqy5duujOO+/U4cOHHasCxo4dqyeffFKPPfaYEhMTdeTIET344IMur/v73/9e06dP15NPPqlu3bpp7NixysvLk3S6P//SSy9p8eLFiomJ0ahRoyRJ9913n/7yl79o2bJlSkhI0M0336xly5Y5lim2atVK//znP/XVV1+pd+/emjlzpubMmVOvz3v11Vdr7ty5mjNnjnr06KGVK1cqLS3trHFBQUF67LHHNG7cOCUnJyswMFCrVq1yHB86dKj+9a9/acOGDbr22mvVt29fzZ07V3FxcfWKB0DTshieaEYCAIBmjQoBAAAgIQAAACQEAABAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAAAQNL/By28VnufdNCGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(random_state=666, n_estimators=200, max_depth=8, learning_rate=.5)\n",
    "gbc.fit(x_train, y_train)\n",
    "\n",
    "# Confusion matrix:\n",
    "# plot_confusion_matrix(gbc, x_test, y_test) #old command\n",
    "ConfusionMatrixDisplay.from_estimator(gbc, x_test, y_test)\n",
    "\n",
    "# Evaluation metrics:\n",
    "y_pred = gbc.predict(x_test)\n",
    "gbc_score = accuracy_score(y_test, y_pred)\n",
    "print(f'GB Accuracy Score = {gbc_score}')\n",
    "print('GB Classification Report:')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2dfcfdea-0577-443f-8daa-97f00abd09a4",
   "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>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>FNLWGT</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationLevel</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>NativeCountry</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>68268</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Private</td>\n",
       "      <td>215990</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>Private</td>\n",
       "      <td>185405</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Private</td>\n",
       "      <td>258006</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>39388</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age          Workclass  FNLWGT      Education  EducationLevel  \\\n",
       "0   62          Local-gov   68268        HS-grad               9   \n",
       "1   50            Private  215990        HS-grad               9   \n",
       "2   36            Private  185405        HS-grad               9   \n",
       "3   64            Private  258006   Some-college              10   \n",
       "4   28   Self-emp-not-inc   39388      Assoc-voc              11   \n",
       "\n",
       "         MaritalStatus         Occupation    Relationship    Race      Sex  \\\n",
       "0   Married-civ-spouse   Transport-moving         Husband   White     Male   \n",
       "1   Married-civ-spouse       Craft-repair         Husband   White     Male   \n",
       "2   Married-civ-spouse       Adm-clerical         Husband   White     Male   \n",
       "3              Widowed       Adm-clerical   Not-in-family   White   Female   \n",
       "4   Married-civ-spouse    Farming-fishing         Husband   White     Male   \n",
       "\n",
       "   CapitalGain  CapitalLoss  HoursPerWeek   NativeCountry  Salary  \n",
       "0            0            0            40   United-States   <=50K  \n",
       "1            0            0            40   United-States    >50K  \n",
       "2            0            0            50   United-States    >50K  \n",
       "3            0            0            40            Cuba   <=50K  \n",
       "4            0            0            60   United-States   <=50K  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('HW9_income.csv')\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c7a01a40-cbac-4ebe-8e18-1c3d0b168c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3084\n",
       "1     916\n",
       "Name: HighIncome, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HighIncome'] = df['Salary'].map(lambda x : 1 if '>50K' in x else 0)\n",
    "df['HighIncome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3dd2fe94-241a-4bff-9279-e1e11457f04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2690\n",
       "0    1310\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'] = df['Sex'].map(lambda x : 1 if 'Male' in x else 0)\n",
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f0857e-fea4-4542-b82f-343b0db2b113",
   "metadata": {},
   "source": [
    "## Feature selection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5b40f43-b19d-4aa8-9d5b-c98cdabc2a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 17)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminating variables with many missing values:\n",
    "missing_frac=df.isnull().mean().sort_values(ascending=False)\n",
    "drop_list=sorted(list(missing_frac[missing_frac>0.3].index))\n",
    "df.drop(labels=drop_list, axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "af1967b1-ab4c-4dd8-b3c5-0ba154ea772e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 10)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminating variables based on intuition: (this is subjective)\n",
    "keep_list=['Gender', 'EducationLevel', 'HoursPerWeek', 'CapitalGain','Age', 'HighIncome', 'Occupation',\n",
    "           'Workclass', 'MaritalStatus', 'Relationship']\n",
    "df=df[keep_list]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4a5327d5-a8b7-4e59-ace1-d2b0575720a1",
   "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>Gender</th>\n",
       "      <th>EducationLevel</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>Age</th>\n",
       "      <th>HighIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012809</td>\n",
       "      <td>0.207148</td>\n",
       "      <td>0.067635</td>\n",
       "      <td>0.094933</td>\n",
       "      <td>0.233259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducationLevel</th>\n",
       "      <td>0.012809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.165150</td>\n",
       "      <td>0.128875</td>\n",
       "      <td>0.035994</td>\n",
       "      <td>0.327435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <td>0.207148</td>\n",
       "      <td>0.165150</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095176</td>\n",
       "      <td>0.069024</td>\n",
       "      <td>0.225311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CapitalGain</th>\n",
       "      <td>0.067635</td>\n",
       "      <td>0.128875</td>\n",
       "      <td>0.095176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056872</td>\n",
       "      <td>0.229374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.094933</td>\n",
       "      <td>0.035994</td>\n",
       "      <td>0.069024</td>\n",
       "      <td>0.056872</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.217113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HighIncome</th>\n",
       "      <td>0.233259</td>\n",
       "      <td>0.327435</td>\n",
       "      <td>0.225311</td>\n",
       "      <td>0.229374</td>\n",
       "      <td>0.217113</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Gender  EducationLevel  HoursPerWeek  CapitalGain       Age  \\\n",
       "Gender          1.000000        0.012809      0.207148     0.067635  0.094933   \n",
       "EducationLevel  0.012809        1.000000      0.165150     0.128875  0.035994   \n",
       "HoursPerWeek    0.207148        0.165150      1.000000     0.095176  0.069024   \n",
       "CapitalGain     0.067635        0.128875      0.095176     1.000000  0.056872   \n",
       "Age             0.094933        0.035994      0.069024     0.056872  1.000000   \n",
       "HighIncome      0.233259        0.327435      0.225311     0.229374  0.217113   \n",
       "\n",
       "                HighIncome  \n",
       "Gender            0.233259  \n",
       "EducationLevel    0.327435  \n",
       "HoursPerWeek      0.225311  \n",
       "CapitalGain       0.229374  \n",
       "Age               0.217113  \n",
       "HighIncome        1.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminating features based on correlations with the output variable: Keep if correlation is above 10%\n",
    "# NOTE: this applies only to numeric featues\n",
    "corr=df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ed05fa6f-2f62-46af-b719-36046067c37f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "corr_chagedoff=abs(corr['HighIncome'])\n",
    "drop_list_corr=sorted(list(corr_chagedoff[corr_chagedoff<0.10].index))\n",
    "corr_chagedoff_reset = corr_chagedoff.reset_index(drop=True)\n",
    "print(drop_list_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7816a2d4-9fd9-43c5-9b89-f04761b4cd2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 10)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(labels=drop_list_corr, axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "beef8baf-e51f-4426-aed0-ac3cc4edd623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Private             2756\n",
       " Self-emp-not-inc     328\n",
       " Local-gov            262\n",
       " ?                    229\n",
       " State-gov            157\n",
       " Self-emp-inc         148\n",
       " Federal-gov          117\n",
       " Without-pay            2\n",
       " Never-worked           1\n",
       "Name: Workclass, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fb0b3b73-3f7f-4033-8672-25fec16ebf8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Husband           1618\n",
       " Not-in-family     1019\n",
       " Own-child          647\n",
       " Unmarried          429\n",
       " Wife               173\n",
       " Other-relative     114\n",
       "Name: Relationship, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9c3f570-e814-43c0-9be0-524c598f086f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Occupation', 'Workclass', 'MaritalStatus', 'Relationship']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a categorical boolean mask:\n",
    "cat_mask=df.dtypes==object\n",
    "\n",
    "# Filter categorical columns using the mask and turn them into a list:\n",
    "cat_cols=df.columns[cat_mask].tolist()\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "17e52af1-d5bb-4904-afb6-abb8bfd5862c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>EducationLevel</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>Age</th>\n",
       "      <th>HighIncome</th>\n",
       "      <th>Occupation_code</th>\n",
       "      <th>Workclass_code</th>\n",
       "      <th>MaritalStatus_code</th>\n",
       "      <th>Relationship_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4000.00000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.67250</td>\n",
       "      <td>10.057750</td>\n",
       "      <td>40.413000</td>\n",
       "      <td>1087.994500</td>\n",
       "      <td>38.463500</td>\n",
       "      <td>0.229000</td>\n",
       "      <td>6.573500</td>\n",
       "      <td>3.872750</td>\n",
       "      <td>2.611250</td>\n",
       "      <td>1.442250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.46936</td>\n",
       "      <td>2.564383</td>\n",
       "      <td>12.427693</td>\n",
       "      <td>7633.716387</td>\n",
       "      <td>13.778775</td>\n",
       "      <td>0.420242</td>\n",
       "      <td>4.252366</td>\n",
       "      <td>1.467002</td>\n",
       "      <td>1.510031</td>\n",
       "      <td>1.594459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender  EducationLevel  HoursPerWeek   CapitalGain          Age  \\\n",
       "count  4000.00000     4000.000000   4000.000000   4000.000000  4000.000000   \n",
       "mean      0.67250       10.057750     40.413000   1087.994500    38.463500   \n",
       "std       0.46936        2.564383     12.427693   7633.716387    13.778775   \n",
       "min       0.00000        1.000000      1.000000      0.000000    17.000000   \n",
       "25%       0.00000        9.000000     40.000000      0.000000    27.000000   \n",
       "50%       1.00000       10.000000     40.000000      0.000000    37.000000   \n",
       "75%       1.00000       12.000000     45.000000      0.000000    47.000000   \n",
       "max       1.00000       16.000000     99.000000  99999.000000    90.000000   \n",
       "\n",
       "        HighIncome  Occupation_code  Workclass_code  MaritalStatus_code  \\\n",
       "count  4000.000000      4000.000000     4000.000000         4000.000000   \n",
       "mean      0.229000         6.573500        3.872750            2.611250   \n",
       "std       0.420242         4.252366        1.467002            1.510031   \n",
       "min       0.000000         0.000000        0.000000            0.000000   \n",
       "25%       0.000000         3.000000        4.000000            2.000000   \n",
       "50%       0.000000         7.000000        4.000000            2.000000   \n",
       "75%       0.000000        10.000000        4.000000            4.000000   \n",
       "max       1.000000        14.000000        8.000000            6.000000   \n",
       "\n",
       "       Relationship_code  \n",
       "count        4000.000000  \n",
       "mean            1.442250  \n",
       "std             1.594459  \n",
       "min             0.000000  \n",
       "25%             0.000000  \n",
       "50%             1.000000  \n",
       "75%             3.000000  \n",
       "max             5.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Occupation_code']=LabelEncoder().fit_transform(df.Occupation)\n",
    "df['Workclass_code']=LabelEncoder().fit_transform(df.Workclass)\n",
    "df['MaritalStatus_code']=LabelEncoder().fit_transform(df.MaritalStatus)\n",
    "df['Relationship_code']=LabelEncoder().fit_transform(df.Relationship)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0d635a95-2918-4ab0-beaf-abf62a43f4e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 10)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(cat_cols, axis=1, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9b5a92e6-83df-4b50-be25-e9038ab6039a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>EducationLevel</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>Age</th>\n",
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       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  EducationLevel  HoursPerWeek  CapitalGain  Age  HighIncome  \\\n",
       "0       1               9            40            0   62           0   \n",
       "1       1               9            40            0   50           1   \n",
       "2       1               9            50            0   36           1   \n",
       "3       0              10            40            0   64           0   \n",
       "4       1              11            60            0   28           0   \n",
       "\n",
       "   Occupation_code  Workclass_code  MaritalStatus_code  Relationship_code  \n",
       "0               14               2                   2                  0  \n",
       "1                3               4                   2                  0  \n",
       "2                1               4                   2                  0  \n",
       "3                1               4                   6                  1  \n",
       "4                5               6                   2                  0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f842cb-5336-405e-9f87-f16341a9578c",
   "metadata": {},
   "source": [
    "## Splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e94a341d-6c55-437e-b0e7-52229e5e46a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2433    0\n",
       "2919    0\n",
       "1916    0\n",
       "Name: HighIncome, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['HighIncome']\n",
    "x = df.loc[:, df.columns != 'HighIncome']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=800)\n",
    "y_test.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5d32ab10-c21c-4267-91e8-d86d2afe05e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CART Accuracy Score = 0.812\n",
      "CART Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.88       783\n",
      "           1       0.56      0.59      0.57       217\n",
      "\n",
      "    accuracy                           0.81      1000\n",
      "   macro avg       0.72      0.73      0.73      1000\n",
      "weighted avg       0.81      0.81      0.81      1000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(random_state=666) #max_depth; class_weight='balanced'\n",
    "dtc.fit(x_train, y_train)\n",
    "\n",
    "# Confusion matrix:\n",
    "# plot_confusion_matrix(dtc, x_test, y_test) #old command\n",
    "ConfusionMatrixDisplay.from_estimator(dtc, x_test, y_test)\n",
    "\n",
    "# Evaluation metrics:\n",
    "y_pred = dtc.predict(x_test)\n",
    "dtc_score = accuracy_score(y_test, y_pred)\n",
    "print(f'CART Accuracy Score = {dtc_score}')\n",
    "print('CART Classification Report:')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bcdd725b-1a3a-40cb-b7e2-52cc08fbd31b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00300611, 0.14871269, 0.1379384 , 0.13561478, 0.21033969,\n",
       "       0.08802351, 0.05666844, 0.01250253, 0.20719385])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2360be1b-a05e-42bb-b378-10122685d69c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender importance = 0.30%\n",
      "EducationLevel importance = 14.87%\n",
      "HoursPerWeek importance = 13.79%\n",
      "CapitalGain importance = 13.56%\n",
      "Age importance = 21.03%\n",
      "Occupation_code importance = 8.80%\n",
      "Workclass_code importance = 5.67%\n",
      "MaritalStatus_code importance = 1.25%\n",
      "Relationship_code importance = 20.72%\n"
     ]
    }
   ],
   "source": [
    "#Display feature importances:\n",
    "for name, val in zip(x_train.columns, dtc.feature_importances_):\n",
    "    print(f'{name} importance = {val:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6dd4953d-d1e5-4fbc-a2aa-64ce7dec6147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting feature importances:\n",
    "feat_imp=pd.Series(dtc.feature_importances_, index=x_train.columns)\n",
    "feat_imp.nlargest(5).sort_values().plot(kind='barh') #nlargest(5) to see the 5 most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "13f18b5a-ff86-41c0-9663-e0dc67fd8310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(random_state=666, max_depth=3) #max_depth; class_weight='balanced'\n",
    "dtc.fit(x_train, y_train)\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plot_tree(dtc, feature_names=x_train.columns, class_names=['0','1'], fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1f091d-285f-4db6-8c98-072b9f07d870",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "131b5e2a-0fa9-452a-a3be-ab33368d7957",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF Accuracy Score = 0.852\n",
      "RF Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.93      0.91       783\n",
      "           1       0.69      0.57      0.62       217\n",
      "\n",
      "    accuracy                           0.85      1000\n",
      "   macro avg       0.79      0.75      0.77      1000\n",
      "weighted avg       0.84      0.85      0.85      1000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=666, n_estimators=100, max_features=3)\n",
    "rfc.fit(x_train, y_train)\n",
    "\n",
    "# Confusion matrix:\n",
    "# plot_confusion_matrix(rfc, x_test, y_test) #old command\n",
    "ConfusionMatrixDisplay.from_estimator(rfc, x_test, y_test)\n",
    "\n",
    "# Evaluation metrics:\n",
    "y_pred = rfc.predict(x_test)\n",
    "rfc_score = accuracy_score(y_test, y_pred)\n",
    "print(f'RF Accuracy Score = {rfc_score}')\n",
    "print('RF Classification Report:')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ca559c35-c133-451c-944b-ffbf0ac0c655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting feature importances:\n",
    "feat_imp=pd.Series(rfc.feature_importances_, index=x_train.columns)\n",
    "feat_imp.nlargest(9).sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3646a1c-ab5e-43e2-9ab3-441e0961e897",
   "metadata": {},
   "source": [
    "## Extra Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7fa157d6-d33b-429d-8f75-0359baea6f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ET Accuracy Score = 0.822\n",
      "ET Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.90      0.89       783\n",
      "           1       0.60      0.54      0.57       217\n",
      "\n",
      "    accuracy                           0.82      1000\n",
      "   macro avg       0.74      0.72      0.73      1000\n",
      "weighted avg       0.82      0.82      0.82      1000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "etc = ExtraTreesClassifier(random_state=666, n_estimators=5) #in this example, lower n_estimator (number of trees) performs better.\n",
    "etc.fit(x_train, y_train)\n",
    "\n",
    "# Confusion matrix:\n",
    "# plot_confusion_matrix(etc, x_test, y_test) #old command\n",
    "ConfusionMatrixDisplay.from_estimator(etc, x_test, y_test)\n",
    "\n",
    "# Evaluation metrics:\n",
    "y_pred = etc.predict(x_test)\n",
    "etc_score = accuracy_score(y_test, y_pred)\n",
    "print(f'ET Accuracy Score = {etc_score}')\n",
    "print('ET Classification Report:')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbc9515-043a-4a12-9a64-fa9c37417a98",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bca1e859-0fac-49ec-b117-374398460404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB Accuracy Score = 0.83\n",
      "GB Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.91      0.89       783\n",
      "           1       0.62      0.55      0.58       217\n",
      "\n",
      "    accuracy                           0.83      1000\n",
      "   macro avg       0.75      0.73      0.74      1000\n",
      "weighted avg       0.82      0.83      0.83      1000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(random_state=666, n_estimators=200, max_depth=8, learning_rate=.5)\n",
    "gbc.fit(x_train, y_train)\n",
    "\n",
    "# Confusion matrix:\n",
    "# plot_confusion_matrix(gbc, x_test, y_test) #old command\n",
    "ConfusionMatrixDisplay.from_estimator(gbc, x_test, y_test)\n",
    "\n",
    "# Evaluation metrics:\n",
    "y_pred = gbc.predict(x_test)\n",
    "gbc_score = accuracy_score(y_test, y_pred)\n",
    "print(f'GB Accuracy Score = {gbc_score}')\n",
    "print('GB Classification Report:')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}