Final_Project_Loan_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "41ce9f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "c3b70dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0    LP001002    Male      No          0      Graduate            No   \n",
       "1    LP001003    Male     Yes          1      Graduate            No   \n",
       "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
       "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
       "4    LP001008    Male      No          0      Graduate            No   \n",
       "..        ...     ...     ...        ...           ...           ...   \n",
       "609  LP002978  Female      No          0      Graduate            No   \n",
       "610  LP002979    Male     Yes         3+      Graduate            No   \n",
       "611  LP002983    Male     Yes          1      Graduate            No   \n",
       "612  LP002984    Male     Yes          2      Graduate            No   \n",
       "613  LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0         NaN             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "0               1.0         Urban           Y  \n",
       "1               1.0         Rural           N  \n",
       "2               1.0         Urban           Y  \n",
       "3               1.0         Urban           Y  \n",
       "4               1.0         Urban           Y  \n",
       "..              ...           ...         ...  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  \n",
       "\n",
       "[614 rows x 13 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "X = df.iloc[:, 1:12]\n",
    "y = df.iloc[:, 12]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3ea0b6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              False\n",
       "Gender                True\n",
       "Married               True\n",
       "Dependents            True\n",
       "Education            False\n",
       "Self_Employed         True\n",
       "ApplicantIncome      False\n",
       "CoapplicantIncome    False\n",
       "LoanAmount            True\n",
       "Loan_Amount_Term      True\n",
       "Credit_History        True\n",
       "Property_Area        False\n",
       "Loan_Status          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Null Values\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "befdc7a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking class balance\n",
    "df['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7847a3",
   "metadata": {},
   "source": [
    "# Missing Values Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "a21d8798",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Cleaning\n",
    "# Imputed missing values with their respective mode values\n",
    "df['Gender'] = X['Gender'].fillna( df['Gender'].dropna().mode().values[0] )\n",
    "df['Married'] = df['Married'].fillna( df['Married'].dropna().mode().values[0] )\n",
    "df['Dependents'] = df['Dependents'].fillna( df['Dependents'].dropna().mode().values[0] )\n",
    "df['Self_Employed'] = df['Self_Employed'].fillna( df['Self_Employed'].dropna().mode().values[0] )\n",
    "df['LoanAmount'] = df['LoanAmount'].fillna( df['LoanAmount'].dropna().mean() )\n",
    "df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna( df['Loan_Amount_Term'].dropna().mode().values[0] )\n",
    "df['Credit_History'] = df['Credit_History'].fillna( df['Credit_History'].dropna().mode().values[0] )\n",
    "df['Dependents'] = df['Dependents'].str.rstrip('+')\n",
    "df['Gender'] = df['Gender'].map({'Female':0,'Male':1}).astype(np.int)\n",
    "df['Married'] = df['Married'].map({'No':0, 'Yes':1}).astype(np.int)\n",
    "df['Education'] = df['Education'].map({'Not Graduate':0, 'Graduate':1}).astype(np.int)\n",
    "df['Self_Employed'] = df['Self_Employed'].map({'No':0, 'Yes':1}).astype(np.int)\n",
    "df['Loan_Status'] = df['Loan_Status'].map({'N':0, 'Y':1}).astype(np.int)\n",
    "df['Dependents'] = df['Dependents'].astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fec60a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                 int32\n",
       "Married                int32\n",
       "Dependents             int32\n",
       "Education              int32\n",
       "Self_Employed          int32\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status            int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "204221cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              False\n",
       "Gender               False\n",
       "Married              False\n",
       "Dependents           False\n",
       "Education            False\n",
       "Self_Employed        False\n",
       "ApplicantIncome      False\n",
       "CoapplicantIncome    False\n",
       "LoanAmount           False\n",
       "Loan_Amount_Term     False\n",
       "Credit_History       False\n",
       "Property_Area        False\n",
       "Loan_Status          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa4cc0e",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "87c841fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29ff2624ee0>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x316.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Gender', col='Married', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "65115a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29ff2d21b20>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x316.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Gender', col='Education', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "d8107670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29ff2b930a0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 514.88x316.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Married', col='Education', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "60553b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29ff2ff2f40>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1482.56x460.8 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Married', col='Dependents', size=3.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "579d6486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram and normal probability plot\n",
    "sns.distplot(df['ApplicantIncome'], fit=norm);\n",
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "74bebed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flg, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (14,6))\n",
    "\n",
    "sns.distplot(df['ApplicantIncome'], ax = axes[0]).set_title('ApplicantIncome Distribution')\n",
    "axes[0].set_ylabel('ApplicantIncomee Count')\n",
    "\n",
    "sns.distplot(df['CoapplicantIncome'], color = \"r\", ax = axes[1]).set_title('CoapplicantIncome Distribution')\n",
    "axes[1].set_ylabel('CoapplicantIncome Count')\n",
    "\n",
    "sns.distplot(df['LoanAmount'],color = \"g\", ax = axes[2]).set_title('LoanAmount Distribution')\n",
    "axes[2].set_ylabel('LoanAmount Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.gcf().clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "8ef64f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29fea969340>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 476.875x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"Gender\", size=6) \\\n",
    "   .map(sns.kdeplot, \"ApplicantIncome\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "67822e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29feb237ac0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 478.25x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"Married\", size=6) \\\n",
    "   .map(sns.kdeplot, \"ApplicantIncome\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "c8449a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "1078c5e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3,figsize=(12,6))\n",
    "\n",
    "g = sns.countplot(df[\"Gender\"], ax=axes[0])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df[\"Married\"], ax=axes[1])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df[\"Dependents\"], ax=axes[2])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.gcf().clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "9af2ded8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3,figsize=(12,6))\n",
    "\n",
    "g = sns.countplot(df[\"Education\"], ax=axes[0])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df[\"Self_Employed\"], ax=axes[1])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df[\"Property_Area\"], ax=axes[2])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.gcf().clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "dde359aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_name = \"CoapplicantIncome\"\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.regplot(x=var_name, y='ApplicantIncome', data=df, scatter_kws={'alpha':0.5, 's':30})\n",
    "plt.xlabel(var_name, fontsize=12)\n",
    "plt.ylabel('ApplicantIncome', fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var_name, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "370e3eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_name = \"LoanAmount\"\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.regplot(x=var_name, y='ApplicantIncome', data=df, scatter_kws={'alpha':0.5, 's':30})\n",
    "plt.xlabel(var_name, fontsize=12)\n",
    "plt.ylabel('ApplicantIncome', fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var_name, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8655f2",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "1e9a030d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 1:12]\n",
    "y = df.iloc[:, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1ab1ea59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X[['ApplicantIncome','CoapplicantIncome','LoanAmount']] = sc.fit_transform(X[['ApplicantIncome','CoapplicantIncome','LoanAmount']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "cb75ffe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(614, 11)\n"
     ]
    }
   ],
   "source": [
    "# Categorical data Encoding\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "enc = OrdinalEncoder()\n",
    "X.iloc[:,10:11] = enc.fit_transform(X.iloc[:,10:11])\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "177e58d2",
   "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>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.072991</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.134412</td>\n",
       "      <td>-0.038732</td>\n",
       "      <td>-0.219273</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.393747</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>-0.957641</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.462062</td>\n",
       "      <td>0.251980</td>\n",
       "      <td>-0.314547</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.097728</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>-0.064454</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.410130</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>-0.898095</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.212557</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>-1.267279</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.437174</td>\n",
       "      <td>-0.472404</td>\n",
       "      <td>1.269371</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.357064</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>0.483367</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.134412</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>-0.159728</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0         1        0           0          1              0         0.072991   \n",
       "1         1        1           1          1              0        -0.134412   \n",
       "2         1        1           0          1              1        -0.393747   \n",
       "3         1        1           0          0              0        -0.462062   \n",
       "4         1        0           0          1              0         0.097728   \n",
       "..      ...      ...         ...        ...            ...              ...   \n",
       "609       0        0           0          1              0        -0.410130   \n",
       "610       1        1           3          1              0        -0.212557   \n",
       "611       1        1           1          1              0         0.437174   \n",
       "612       1        1           2          1              0         0.357064   \n",
       "613       0        0           0          1              1        -0.134412   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0            -0.554487    0.000000             360.0             1.0   \n",
       "1            -0.038732   -0.219273             360.0             1.0   \n",
       "2            -0.554487   -0.957641             360.0             1.0   \n",
       "3             0.251980   -0.314547             360.0             1.0   \n",
       "4            -0.554487   -0.064454             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609          -0.554487   -0.898095             360.0             1.0   \n",
       "610          -0.554487   -1.267279             180.0             1.0   \n",
       "611          -0.472404    1.269371             360.0             1.0   \n",
       "612          -0.554487    0.483367             360.0             1.0   \n",
       "613          -0.554487   -0.159728             360.0             0.0   \n",
       "\n",
       "     Property_Area  \n",
       "0              2.0  \n",
       "1              0.0  \n",
       "2              2.0  \n",
       "3              2.0  \n",
       "4              2.0  \n",
       "..             ...  \n",
       "609            0.0  \n",
       "610            0.0  \n",
       "611            2.0  \n",
       "612            2.0  \n",
       "613            1.0  \n",
       "\n",
       "[614 rows x 11 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "de5fb527",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set  \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 22,shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "ea4e19c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
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       "      <th>Credit_History</th>\n",
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       "      <th>385</th>\n",
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       "    <tr>\n",
       "      <th>523</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.011543</td>\n",
       "      <td>-0.167670</td>\n",
       "      <td>0.471458</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.088213</td>\n",
       "      <td>1.368994</td>\n",
       "      <td>0.733459</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.393747</td>\n",
       "      <td>0.015307</td>\n",
       "      <td>-0.552730</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.479933</td>\n",
       "      <td>0.528668</td>\n",
       "      <td>0.221365</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1</td>\n",
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       "      <td>-0.554487</td>\n",
       "      <td>-0.910004</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>491 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "306       0        0           0          1              0        -0.268912   \n",
       "122       0        0           0          1              0        -0.535129   \n",
       "548       0        0           0          1              0        -0.066097   \n",
       "385       1        0           1          1              0        -0.284476   \n",
       "523       1        1           2          1              1         0.416860   \n",
       "..      ...      ...         ...        ...            ...              ...   \n",
       "491       1        1           1          0              0        -0.011543   \n",
       "502       1        1           2          1              0        -0.088213   \n",
       "358       1        1           0          0              0        -0.393747   \n",
       "356       1        1           2          1              0         0.479933   \n",
       "132       1        0           0          1              0        -0.439946   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "306           0.015307   -0.135909             360.0             1.0   \n",
       "122           2.516790   -0.112091             360.0             0.0   \n",
       "548          -0.554487   -0.517002             360.0             0.0   \n",
       "385          -0.554487   -0.397911             180.0             1.0   \n",
       "523           1.896379    3.972750             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "491          -0.167670    0.471458             360.0             1.0   \n",
       "502           1.368994    0.733459             360.0             1.0   \n",
       "358           0.015307   -0.552730             480.0             0.0   \n",
       "356           0.528668    0.221365             360.0             1.0   \n",
       "132          -0.554487   -0.910004             360.0             1.0   \n",
       "\n",
       "     Property_Area  \n",
       "306            0.0  \n",
       "122            1.0  \n",
       "548            1.0  \n",
       "385            2.0  \n",
       "523            0.0  \n",
       "..             ...  \n",
       "491            2.0  \n",
       "502            1.0  \n",
       "358            2.0  \n",
       "356            0.0  \n",
       "132            1.0  \n",
       "\n",
       "[491 rows x 11 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978e8acf",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "ff5a5e23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.51      0.62        39\n",
      "           1       0.80      0.93      0.86        84\n",
      "\n",
      "    accuracy                           0.80       123\n",
      "   macro avg       0.79      0.72      0.74       123\n",
      "weighted avg       0.79      0.80      0.78       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zKoXjpqWZZaaoyhOxjcCcZNnJHsDciHhA0pPAXEkzgDXAZZUKciIzs2yqNPtFRCwH3tvG+a1knA/RiczMMvNYSzMrvLwNUXIiM7PsXCMzs0Ir6ErjZmYHcyIzsyJrfSA2T5zIzCwzteQrkzmRmVk2XkWpexkwuJnr/2Et407cSwR844ZjWbFkQL3DsjJrV/fhlk+Ne+v41TW9+YvPv8ppZ+3ktllj2Le3Bw09g8/83TpOfO/u+gWaM93m8QtJ3wc+DGyKiCMa0V50V9/8CosXDeJvZo6jZ68W+vTL2X9jxrHj3+SO/1gJQHMzXDnpZM6+ZDvfvPFYPnHDq7zvAzt4umkQd/7N0XztvtV1jjZHcvZXuZaDxn9AaSR7t9R/YDOnnrmLh+4eDsCB/T3Y9UZDnaOy9ix7fBCN73qT0WP2I8GuHaXf1643Ghg+en+do8uXas9+0VE1q5FFxGOSxtWq/Lz7vXft4/WtDXzuH9dy/Ml7WLW8P3f8n6N5c4+TWV4tmjeU86ZtB+BTN7/CF654N/9889FEwD/OX1Xf4PIkgOoMGq+auk/jI2lm6+yR+3mz3uFUTUNDMP7UPTzww6P49EUnsHd3D/78MxWnVbI62b9P/PLhIZz7ke0APDBnBFd95RXuWvICV315Pd+4YWx9A8wZtaTbOkvdE1lEzI6IyRExuRd96h1O1WzZ0IvNG3qx8tlS5/4TDwxh/Kl76hyVHc4zjwxi/Km7GTbyAAAL7x3OH33odQDO/ch2frOsfz3Dy5VaTKzYUXVPZF3Va5t7sWV9b8a8ey8AE8/ZyZpVfesclR3Oon8b9lazEuCo0ftZ/uRAAJY9MZCjj+s6rYUOi0i/dRI/flFDt3/xGP7q22vo2St4dU1vvn79sfUOydqwd7dY+vggrvv7tW+d++zX1nLHTcfQ3Cx692nhs19b204J3U+3ebJf0j3AeZRWUVkHfCki7qzV9fLopef7cc0l76l3GFZB3/7BT55/7qBzp5yxi9sX/KZOERVAd0lkEXFFrco2s/rqNjUyM+uiAmjOVyZzIjOzzFwjM7Pi8wOxZlZ01XiOTNKxkh6VtELS85KuS84Pl7RQ0qrkdVileJzIzCybtIvzVq60HQA+FxG/D5wJfFrSScAsoCkiJgBNyXG7nMjMLBMBao5UW3siYkNELE32dwArgGOAqcCc5GNzgGmVYnIfmZllVqWVxt8urzTBxHuBp4DREbEBSslO0qhK33ciM7Nsss0QO0LS4rLj2RExu/wDkgYC9wGfjYg3JGUOyYnMzDLKNI5yS0RMPtybknpRSmJ3RcRPk9MbJTUmtbFGoOK0Me4jM7PMqnTXUsCdwIqI+EbZW/OB6cn+dGBepXhcIzOz7KrTR3Y28BfAryUtS859AbgVmCtpBrAGuKxSQU5kZpZNUPGOZKpiIp6gdBO0LRdkKcuJzMyyy9eD/U5kZpZdtR+/6CgnMjPLzonMzAotgO6yQK+ZdU0i3LQ0sy6gJV9VMicyM8vGTUsz6wrctDSz4nMiM7Ni69zFd9NwIjOzbLyKkpl1Be4jM7PicyIzs0ILoMWJzMwKzZ39ZtYVOJGZWaEF0JyvR/udyMwso4BwIjOzonPT0swKzXctzaxLcI3MzAovZ4nMC/SaWTYR0NycbqtA0vclbZL0XNm54ZIWSlqVvA6rVI4TmZllF5Fuq+wHwJRDzs0CmiJiAtCUHLfLiczMsqtSIouIx4Bth5yeCsxJ9ucA0yqV4z4yM8sosty1HCFpcdnx7IiYXeE7oyNiA0BEbJA0qtJFnMjMLJuASP9A7JaImFzLcMCJzMyORG2HKG2U1JjUxhqBTZW+4D4yM8smorQcXJrtyMwHpif704F5lb7gRGZm2VWps1/SPcCTwAmS1kmaAdwKXChpFXBhctwuNy3NLLOo0gK9EXHFYd66IEs5TmRmlpEnVjSzovOgcTMrugAixfCjzuREZmbZhCdWNLMuINy0NLPCy1mNTJGjuw+SNgO/q3ccNTAC2FLvICyTrvo7e1dEjOxIAZIeovTnk8aWiDh0douqy1Ui66okLe6M8WZWPf6dFYuf7DezwnMiM7PCcyLrHJXmX7L88e+sQNxHZmaF5xqZmRWeE5mZFZ4TWQ1JmiJppaTVkiquBGP119byZJZ/TmQ1IqkBuB24BDgJuELSSfWNylL4Ae9cnsxyzomsdk4HVkfESxGxD/gxpWWuLMcOszyZ5ZwTWe0cA6wtO16XnDOzKnMiqx21cc7PupjVgBNZ7awDji07HgOsr1MsZl2aE1ntPANMkHScpN7A5ZSWuTKzKnMiq5GIOAB8BlgArADmRsTz9Y3KKjnM8mSWcx6iZGaF5xqZmRWeE5mZFZ4TmZkVnhOZmRWeE5mZFZ4TWYFIapa0TNJzku6V1L8DZf1A0seS/e+1N6Bd0nmS3n8E13hZ0jtW2znc+UM+szPjtb4s6casMVrX4ERWLHsiYmJEnALsAz5V/mYy40ZmEfE/I+KFdj5yHpA5kZl1Fiey4nocGJ/Ulh6VdDfwa0kNkr4m6RlJyyVdBaCSb0t6QdKDwKjWgiQtkjQ52Z8iaamkX0lqkjSOUsK8PqkNniNppKT7kms8I+ns5LtHSXpY0rOS/om2x5seRNK/SVoi6XlJMw957+tJLE2SRibn3i3poeQ7j0s6sSp/mlZsEeGtIBuwM3ntCcwDrqZUW9oFHJe8NxP4YrLfB1gMHAd8FFgINABHA9uBjyWfWwRMBkZSmrGjtazhyeuXgRvL4rgb+KNkfyywItm/Dbgp2f8TSoPkR7Txc7zcer7sGv2A54CjkuMArkz2bwK+new3AROS/TOAR9qK0Vv32noeWfqzOuknaVmy/zhwJ6Um39MR8d/J+YuAP2jt/wKGABOAc4F7IqIZWC/pkTbKPxN4rLWsiDjcvFwfBE6S3qpwDZY0KLnGR5PvPijptRQ/07WS/jTZPzaJdSvQAvxrcv5HwE8lDUx+3nvLrt0nxTWsi3MiK5Y9ETGx/ETyD3pX+SngmohYcMjnPkTlaYSU4jNQ6pI4KyL2tBFL6jFvks6jlBTPiojdkhYBfQ/z8Uiuu/3QPwMz95F1PQuAqyX1ApD0HkkDgMeAy5M+tEbg/Da++yTwx5KOS747PDm/AxhU9rmHKQ2IJ/ncxGT3MeDK5NwlwLAKsQ4BXkuS2ImUaoStegCttcqPA09ExBvAf0u6LLmGJJ1W4RrWDTiRdT3fA14AliYLaPwTpZr3/cAq4NfAHcB/HvrFiNhMqY/tp5J+xdtNu58Bf9ra2Q9cC0xObia8wNt3T78CnCtpKaUm7poKsT4E9JS0HPgq8Muy93YBJ0taAnwAuDk5fyUwI4nveTx9uOHZL8ysC3CNzMwKz4nMzArPiczMCs+JzMwKz4nMzArPiczMCs+JzMwK7/8DOH5s+KqFEnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtclf = DecisionTreeClassifier(max_depth=4,min_samples_split=5,criterion=\"entropy\",random_state=42)\n",
    "dtclf = dtclf.fit(X_train,y_train)\n",
    "y_pred = dtclf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Decision Tree Classifier')\n",
    "display.plot()  # doctest: +SKIP\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "14a33c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize=(15,10))\n",
    "# _ = tree.plot_tree(dtclf,\n",
    "#                    feature_names=X.columns,\n",
    "#                    filled=True)\n",
    "plt.figure(figsize=(15, 10))\n",
    "tree.plot_tree(dtclf, filled=True, rounded=True, \n",
    "           feature_names=X.columns);\n",
    "fig.savefig('DT.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93482f48",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "15baa29f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        39\n",
      "           1       0.68      1.00      0.81        84\n",
      "\n",
      "    accuracy                           0.68       123\n",
      "   macro avg       0.34      0.50      0.41       123\n",
      "weighted avg       0.47      0.68      0.55       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svcclf = LinearSVC(random_state = 12)\n",
    "svcclf = svcclf.fit(X_train,y_train)\n",
    "y_pred = svcclf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='SVM Classifier')\n",
    "display.plot() \n",
    "plt.plot([0, 1], [0, 1],'r--')# doctest: +SKIP\n",
    "plt.show()  \n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b19766",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "3185e7d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.46      0.62        39\n",
      "           1       0.80      0.99      0.88        84\n",
      "\n",
      "    accuracy                           0.82       123\n",
      "   macro avg       0.87      0.72      0.75       123\n",
      "weighted avg       0.85      0.82      0.80       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATIAAAEGCAYAAADmLRl+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYJElEQVR4nO3de7QdZXnH8e8vJ4GQhEsOuco1agoilRgjF2kxXFRQl0EXCIo2tdgAoqgt1JRaaaXtwrbUO+oRkLRAJAiYeCkhRiJhFSEJBAyBlIsQArmQkJRAQi7nPP1j5pidw8nZM8m+zJzz+6w1a8/Mnj3zJFl51vu+M/M+igjMzMqsX7MDMDPbU05kZlZ6TmRmVnpOZGZWek5kZlZ6/ZsdQKUBew2OgQOHNjsMyyFamh2B5bFl03q2bXlVe3KO9508ONa91J7p2EWPbJkdEafvyfWyKFQiGzhwKO84/rPNDsNy2D7YmaxMFs/75h6fY+1L7dw/++BMxw4Y/dSwPb5gBoVKZGZWBkF7dDQ7iJ04kZlZLgF0UKwH6Z3IzCy3DorVIvNdSzPLJQi2RUempRpJX5T0qKQlkqZLGiipVdIcSU+kn1XvADqRmVkuAbQTmZaeSDoIuASYEBFHAy3AucBUYG5EjAXmpts9ciIzs9w6iExLBv2BfST1BwYBLwCTgGnp99OAM7OcxMwsswDas8+aM0zSworttohoA4iI5yX9O7Ac2AzcFRF3SRoZESvTY1ZKGlHtIk5kZpZbjqH+tRExobsv0rGvScAYYANwq6RP7E48TmRmlktkGP/K6DTg9xHxIoCk24F3AasljU5bY6OBNdVO5DEyM8slArZlXKpYDhwvaZAkAacCjwGzgMnpMZOBmdVO5BaZmeUk2tmj1zUBiIj7Jf0EeBDYDjwEtAFDgBmSzidJdmdXO5cTmZnlEkBHjR7sj4grgCu67N5C0jrLzInMzHKrRYuslpzIzCyX5IFYJzIzK7EAtkWx7hM6kZlZLoFoL9gDD05kZpZbR7hraWYl5jEyM+sFRLvHyMyszJIZYp3IzKzEIsTWgpXPciIzs9w6PEZmZmWWDPa7a2lmpebBfjMrOQ/2m1mv0O4HYs2szAKxLYqVOooVjZkVngf7zaz0ArlraWblV7TB/mJFY2aFFwHt0S/T0hNJR0haXLG8LOkLklolzZH0RPo5tFpMTmRmlksy2N+SaenxPBHLImJcRIwD3gFsAu4ApgJzI2IsMDfd7pETmZnl1k6/TEsOpwJPRcSzJEV7p6X7pwFnVvuxx8jMLJdAeSZWHCZpYcV2W0S0dXPcucD0dH1kRKwESIv0jqh2EScyM8stR2trbURM6OkASXsBHwL+dnfjcSIzs1ySupY1HZU6A3gwIlan26sljU5bY6OBNdVO4DEyM8spqTSeZcnoY+zoVgLMAian65OBmdVO4BaZmeWSlIOrzcSKkgYB7wEuqNh9FTBD0vnAcuDsaudxIjOzXCJUs65lRGwCDuyybx3JXczMnMjMLDfPR2ZmpZbMR+Z3Lc2s1DxDrJmVXPL4hVtkZlZine9aFokTmZnlVrRpfJzIzCyXZBofdy3NrOQ8RmZmpZbMfuGupZmVWPKKkhNZr3bZp+dz/Ljn2PDyQM6//CMAvOnQdXzxz/+HvQa0094hvjntXTz+9PAmR2oAI4a+wuWT53HgfpvpCPjZvW/hJ3cfzcTxT/OpDyzisFEbuOBrZ7Jsuf+9dihei6yu0Ug6XdIySU9KqjpdbW8we/5Ypv7be3fad8E5C/jPn45jyt+fyQ23jWfKOQuaFJ111d7ej2tuO55PfvVsLvzXSXz43Y9y2Kj1/P6FoXy57T08/OToZodYSB0o09IodWuRSWoBvkvyZvsKYIGkWRGxtF7XLIJHlo1i5LCNO+0LxKB9tgEweNBW1m0Y1IzQrBvrXh7EupeTf4/NW/bi2VVDGX7Aqyx8/OAmR1Zcfe2u5bHAkxHxNICkH5PMxd2rE1l3vnvTcXztstlceO4C+in43JUfbHZI1o1RrRsZe8halj5TdWblPq8vdS0PAp6r2F6R7tuJpCmSFkpauG3bq3UMp3k+dMrjXHPTcZz7xXP47s3Hcumn5zc7JOtin723ceUFv+Lbt57Aptf2anY4hdY5Z3+WpVHqmci6+1PE63ZEtEXEhIiYMGDA4DqG0zzv/ZMnmL/wMAB+88AYjnzj2iZHZJVa+nVw5ZQ5zHngTdyzeEyzwym8ALZHv0xLo9TzSiuAQyq2DwZeqOP1CmvdhkEcc+QqAN5+1EqeX7VfkyOyHYIvffI3PLtqKDPmvq3ZwZRGR/TLtDRKPcfIFgBjJY0Bnicp9/TxOl6vEL580d0c85ZV7D/kNW75xo+54fbxXH39iXz2vPtpaelg67YWrv7Ric0O01J//KbVnH78kzy1opXrLr8NgB/OfCcD+rfz+XPu44Ahm/naxbN5ckUrl377/U2OtiBq2G2UdABwLXA0SWPvL4BlwC3A4cAzwEcjYn1P56lbIouI7ZI+C8wGWoDrI+LRel2vKP7peyd3u//CKyY1OBLL4ndPjeKki/6y2+/mP+xuZndqPLHiN4E7I+KstCzcIOBykkrjV6WPbU0FvtTTSer6QGxE/BL4ZT2vYWaNV4sWmaT9gJOAPweIiK3AVkmTgInpYdOAeVRJZMW6h2pmhdc5sWLGu5bDOp9KSJcpFad6I/Ai8CNJD0m6VtJgulQaB1xp3MxqKxDbO2pSabw/MB74XETcL+mbJN3I3NwiM7PcavSK0gpgRUTcn27/hCSxrU4rjONK42ZWH5Gra7nr00SsAp6TdES661SSN39cadzM6qvGxUc+B9yU3rF8GvgUSQPLlcbNrL5qlcgiYjHQ3RiaK42bWf0Eoj37YH9DOJGZWW6uNG5mpRbh4iNm1guEE5mZlVtj5xrLwonMzHJzi8zMSi0C2jucyMys5HzX0sxKLXDX0sxKz4P9ZtYLxOvKCDWXE5mZ5eaupZmVWnLX0u9amlnJuWtpZqXnrqWZlVogJzIzK7+C9SydyMwsp4DwK0pmVna16lpKegbYCLQD2yNigqRW4BbgcOAZ4KMRsb6n8xTrHqqZlUJEtiWjkyNiXEX9y6nA3IgYC8wlQ63LXbbIJH2bHrrCEXFJ5jDNrNdowLuWk4CJ6fo0YB7wpZ5+0FPXcmFNQjKz3iWA7IlsmKTKXNIWEW1dznaXpAB+kH43MiJWAkTESkkjql1kl4ksIqZVbksaHBGvZo3ezHqvHN3GtRVdxu6cGBEvpMlqjqTHdyeeqmNkkk6QtBR4LN0+RtI1u3MxM+sNRHRkW6qJiBfSzzXAHcCxwGpJowHSzzXVzpNlsP8bwPuAdekFHwZOyvA7M+utIuPSA0mDJe3buQ68F1gCzAImp4dNBmZWCyfT4xcR8Zy0U3Ztz/I7M+uFomaD/SOBO9Lc0h+4OSLulLQAmCHpfGA5cHa1E2VJZM9JehcQkvYCLiHtZppZH1WDR/sj4mngmG72rwNOzXOuLF3LC4GLgYOA54Fx6baZ9VnKuDRG1RZZRKwFzmtALGZWFh3NDmBnWe5avlHSzyS9KGmNpJmS3tiI4MysgDqfI8uyNEiWruXNwAxgNPAG4FZgej2DMrNiq/ErSnssSyJTRPxXRGxPlxsp3iweZtZINXj8opZ6eteyNV29W9JU4MckoZ0D/KIBsZlZUZVoYsVFJImrM+ILKr4L4Mp6BWVmxaaC9cl6etdyTCMDMbOSCEEZJ1aUdDRwFDCwc19E/Ge9gjKzgitLi6yTpCtI5gY6CvglcAZwL+BEZtZXFSyRZblreRbJ6wKrIuJTJK8U7F3XqMys2Mpy17LC5ojokLRd0n4kU2r4gVizvirfxIoNkSWRLZR0APBDkjuZrwAP1DMoMyu20ty17BQRn0lXvy/pTmC/iHikvmGZWaGVJZFJGt/TdxHxYH1CMrOiK1OL7OoevgvglBrHgjZuYsCvFtX6tFZHv35hcbNDsByOfd/a2pyoLGNkEXFyIwMxs5Jo8B3JLFyg18zyq+HjF5JaJD0k6efpdqukOZKeSD+HVjuHE5mZ5aaObEtGn2fn6fNzVxp3IjOz/GrUIpN0MPAB4NqK3ZNIKoyTfp5Z7TxZZoiVpE9I+kq6faikY6uHaGa9kSL7QlppvGKZ0uV03wD+hp0nz96p0jiw+5XGK1yTXuQU4KvARuA24J0ZfmtmvVH2u5a7rDQu6YPAmohYJGninoSTJZEdFxHjJT0EEBHr07JwZtZX1eau5YnAhyS9n2Rmnf0k3UhaaTwiVtay0vg2SS2koUsaTuFqqJhZI+XoWu5SRPxtRBwcEYcD5wK/johPsBuVxrMksm8BdwAjJP0zyRQ+/5Lhd2bWG0XN71p2dRXwHklPAO9Jt3uU5V3LmyQtIpnKR8CZEeFK42Z9WY0fiI2IecC8dD13pfEsEyseCmwCfla5LyKW57mQmfUiBXuyP8tg/y/YUYRkIDAGWAa8tY5xmVmBlemlcQAi4o8rt9NZMS7YxeFmZg2XqfhIpYh4UJKfITPry8rWIpP0VxWb/YDxwIt1i8jMii326I5kXWRpke1bsb6dZMzstvqEY2alUKYWWfog7JCIuKxB8ZhZwYkSDfZL6h8R23ua8trM+qiyJDKSSknjgcWSZgG3Aq92fhkRt9c5NjMrogyvHzValjGyVmAdyewXnc+TBeBEZtZXlWiwf0R6x3IJOxJYp4LlYzNrpDK1yFqAIeycwDoV7I9hZg1VsAzQUyJbGRFfbVgkZlYOBayi1FMiK1bhOjMrjDJ1LXNNo2FmfUhZEllEvNTIQMysPMr4ipKZ2Q4lGyMzM3sdUbwBdBfoNbP8alCgV9JASQ9IeljSo5L+Md3fKmmOpCfSz6HVwnEiM7PcalFFCdgCnBIRxwDjgNMlHQ9MBeZGxFhgbrrdIycyM8uvBi2ySLySbg5IlwAmAdPS/dOAM6uF40RmZvnkKwc3TNLCimVK5akktUhaTFKEd05E3A+MjIiVAOnniGohebDfzPLLftdybURM2OVpItqBcZIOAO6QdPTuhOMWmZnlVqMxsj+IiA0kdS1PB1ZLGg2Qfq6p9nsnMjPLrzZ3LYenLTEk7QOcBjwOzAImp4dNBmZWC8ddSzPLrUbvWo4GpqVT6vcDZkTEzyXdB8yQdD6wHDi72omcyMwsn6AmEytGxCPA27vZv46c73o7kZlZLqUqPmJmtktOZGZWdopiZTInMjPLx7NfmFlv4DEyMys9T6xoZuXnFpmZlVpJK42bme3MiczMyswPxJpZr6COYmUyJzIzy8fPkfUtf/UfyznutI1sWNufC045otnh2C7c3jac/765FQnGHPkaf/315Uz/1kjum70/EhwwbBuXfmM5B47a3uxQC6Noj1/UbT4ySddLWiNpSb2uUXR33dLK3503ptlhWA/WrhzAT68bxnf++39pu3sZ7R0wb+ZQzrpoDd+fu4zv/WoZx532Mjd+fVSzQy2WGsxHVkv1nFjxBpLZHvusJfcPYeN6N3qLrn272PJaP9q3w5bN/Thw5DYG77ujyfHa5n6oaIUcm6zWM8Tuqbr9L4uIeyQdXq/zm9XCsNHbOOuiNXzynUex98Bg/Ltf5h0TNwLwo6tG8atbWxm8Xzv/+pMnmxxpgQRQsJfGmz7VtaQpnRVWtrGl2eFYH7NxQwv3zd6fafcv5eaHlvDaphbm3pbUg/3U1FXctGgpp3xkPbOuH97kSIslRxWlhmh6IouItoiYEBETBrB3s8OxPuah+UMYdchWDjiwnf4D4MT3b2DpwsE7HXPyh9dz7y/3b1KExdP5HNmedi0lHSLpbkmPpZXGP5/ud6VxszxGHLSNxx4cxGubRAQsvndfDn3zazz/9F5/OOa3s/fnkDe7t/AHEdmXnm0H/joi3gIcD1ws6Sh2o9K4R6LraOo1z/K2E15h/9bt3LhwKf919UhmTz+w2WFZhSPHb+JPP/B/XPy+I2jpH7z56M2c8Yl1XHXxYax4am/69YMRB23lkq+taHaohVKLgfy0+G5nId6Nkh4DDiKpND4xPWwaSZm4L/V0rrolMknT02CGSVoBXBER19XrekV01WcOa3YIlsGfXbaKP7ts1U77vnLtM80JpiyyJ7JhkhZWbLdFRFvXg9Ibg28HXldpXFLzKo1HxMfqdW4za64cLbIeK40DSBoC3AZ8ISJe1m486+KupZnlE0B7bR6/kDSAJIndFBG3p7tXSxqdtsZcadzM6qNGdy0FXAc8FhH/UfGVK42bWQPU5oHYE4FPAr+TtDjddzlwFa40bmb1VqO7lveSPJbWHVcaN7M68jQ+ZlZ2AlSjwf5acSIzs9xcadzMys1dSzMrv0zvUTaUE5mZ5eYqSmZWfm6RmVmphe9amllvUKw85kRmZvn58QszKz8nMjMrtQAKVqDXiczMchHhrqWZ9QIdxWqSOZGZWT7uWppZb+CupZmVnxOZmZVb8V4ad/ERM8uns4pSlqUKSddLWiNpScW+VklzJD2Rfg6tdh4nMjPLTRGZlgxuAE7vsm8qMDcixgJz0+0eOZGZWX4R2Zaqp4l7gJe67J4ETEvXpwFnVjuPx8jMLJ8AOjKPkQ2TtLBiuy0i2qr8ZmRErARIi/SOqHYRJzIzyynXYP/aiJhQz2jAXUsz2x016lruwmpJowHSzzXVfuBEZmb5BNDekW3ZPbOAyen6ZGBmtR+4a2lmOQVEbd5RkjQdmEgylrYCuAK4Cpgh6XxgOXB2tfM4kZlZfjV6IDYiPraLr07Ncx4nMjPLJ99dy4ZwIjOz/Ar2ipITmZnl50RmZqUWAe3tzY5iJ05kZpafW2RmVnpOZGZWbuG7lmZWcgFRowdia8WJzMzy2/3Xj+rCiczM8olwOTgz6wU82G9mZRdukZlZuRWvipITmZnl45fGzazsAgi/omRmpRa1m1ixVpzIzCy3cNfSzEqvYC0yRYHuPkh6EXi22XHUwTBgbbODsFx667/ZYRExfE9OIOlOkr+fLNZGRNdK4jVXqETWW0la2IjaflY7/jcrF5eDM7PScyIzs9JzImuMtmYHYLn536xEPEZmZqXnFpmZlZ4TmZmVnhNZHUk6XdIySU9KmtrseKw6SddLWiNpSbNjseycyOpEUgvwXeAM4CjgY5KOam5UlsENQN0f4LTaciKrn2OBJyPi6YjYCvwYmNTkmKyKiLgHeKnZcVg+TmT1cxDwXMX2inSfmdWYE1n9qJt9ftbFrA6cyOpnBXBIxfbBwAtNisWsV3Miq58FwFhJYyTtBZwLzGpyTGa9khNZnUTEduCzwGzgMWBGRDza3KisGknTgfuAIyStkHR+s2Oy6vyKkpmVnltkZlZ6TmRmVnpOZGZWek5kZlZ6TmRmVnpOZCUiqV3SYklLJN0qadAenOsGSWel69f29EK7pImS3rUb13hG0uuq7exqf5djXsl5rX+QdGneGK13cCIrl80RMS4ijga2AhdWfpnOuJFbRHw6Ipb2cMhEIHciM2sUJ7Lymg+8OW0t3S3pZuB3klok/ZukBZIekXQBgBLfkbRU0i+AEZ0nkjRP0oR0/XRJD0p6WNJcSYeTJMwvpq3BP5U0XNJt6TUWSDox/e2Bku6S9JCkH9D9+6Y7kfRTSYskPSppSpfvrk5jmStpeLrvTZLuTH8zX9KRNfnbtHKLCC8lWYBX0s/+wEzgIpLW0qvAmPS7KcCX0/W9gYXAGOAjwBygBXgDsAE4Kz1uHjABGE4yY0fnuVrTz38ALq2I42bgT9L1Q4HH0vVvAV9J1z9A8pL8sG7+HM907q+4xj7AEuDAdDuA89L1rwDfSdfnAmPT9eOAX3cXo5e+tfTfvfRnTbKPpMXp+nzgOpIu3wMR8ft0/3uBt3WOfwH7A2OBk4DpEdEOvCDp192c/3jgns5zRcSu5uU6DThK+kODaz9J+6bX+Ej6219IWp/hz3SJpA+n64eksa4DOoBb0v03ArdLGpL+eW+tuPbeGa5hvZwTWblsjohxlTvS/9CvVu4CPhcRs7sc936qTyOkDMdAMiRxQkRs7iaWzO+8SZpIkhRPiIhNkuYBA3dxeKTX3dD178DMY2S9z2zgIkkDACT9kaTBwD3AuekY2mjg5G5+ex/wbklj0t+2pvs3AvtWHHcXyQvxpMeNS1fvAc5L950BDK0S6/7A+jSJHUnSIuzUD+hsVX4cuDciXgZ+L+ns9BqSdEyVa1gf4ETW+1wLLAUeTAto/ICk5X0H8ATwO+B7wG+6/jAiXiQZY7td0sPs6Nr9DPhw52A/cAkwIb2ZsJQdd0//EThJ0oMkXdzlVWK9E+gv6RHgSuC3Fd+9CrxV0iLgFOCr6f7zgPPT+B7F04cbnv3CzHoBt8jMrPScyMys9JzIzKz0nMjMrPScyMys9JzIzKz0nMjMrPT+H+j5yrl0ArUIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgclf = LogisticRegression(random_state = 12)\n",
    "lgcclf = lgclf.fit(X_train,y_train)\n",
    "y_pred_proba = lgclf.predict_proba(X_test)\n",
    "y_pred = lgclf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred_proba[:,1],drop_intermediate=True)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Logistic Regression Classifier')\n",
    "display.plot()  # doctest: +SKIP\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8d9a16",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "b5aef0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.46      0.60        39\n",
      "           1       0.79      0.96      0.87        84\n",
      "\n",
      "    accuracy                           0.80       123\n",
      "   macro avg       0.83      0.71      0.74       123\n",
      "weighted avg       0.81      0.80      0.79       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb=gnb.fit(X_train, y_train)\n",
    "y_pred = gnb.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Gaussian Naive Bayes Classifier')\n",
    "display.plot()  # doctest: +SKIP\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2af0f6",
   "metadata": {},
   "source": [
    "# Artificial Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "ec6359ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "c1b12197",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann = tf.keras.models.Sequential()\n",
    "ann.add(tf.keras.layers.Dense(units = 6, activation = 'relu'))\n",
    "ann.add(tf.keras.layers.Dense(units = 6, activation = 'relu'))\n",
    "ann.add(tf.keras.layers.Dense(units = 1, activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "8173792a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "50/50 [==============================] - 1s 3ms/step - loss: 0.5450 - accuracy: 0.7678\n",
      "Epoch 2/50\n",
      "50/50 [==============================] - 0s 2ms/step - loss: 0.5452 - accuracy: 0.7271\n",
      "Epoch 3/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5467 - accuracy: 0.7536\n",
      "Epoch 4/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5375 - accuracy: 0.7760\n",
      "Epoch 5/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5450 - accuracy: 0.7658\n",
      "Epoch 6/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5510 - accuracy: 0.7597\n",
      "Epoch 7/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5669 - accuracy: 0.7047\n",
      "Epoch 8/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5448 - accuracy: 0.7230\n",
      "Epoch 9/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5468 - accuracy: 0.7475\n",
      "Epoch 10/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5558 - accuracy: 0.7556\n",
      "Epoch 11/50\n",
      "50/50 [==============================] - 0s 2ms/step - loss: 0.5280 - accuracy: 0.7862\n",
      "Epoch 12/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5208 - accuracy: 0.7556\n",
      "Epoch 13/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5445 - accuracy: 0.7760\n",
      "Epoch 14/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5223 - accuracy: 0.7658\n",
      "Epoch 15/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5586 - accuracy: 0.7475\n",
      "Epoch 16/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5209 - accuracy: 0.7963\n",
      "Epoch 17/50\n",
      "50/50 [==============================] - 0s 2ms/step - loss: 0.5239 - accuracy: 0.7800\n",
      "Epoch 18/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5229 - accuracy: 0.7739\n",
      "Epoch 19/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5377 - accuracy: 0.7699\n",
      "Epoch 20/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5222 - accuracy: 0.7963\n",
      "Epoch 21/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5235 - accuracy: 0.7821\n",
      "Epoch 22/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5374 - accuracy: 0.7699\n",
      "Epoch 23/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5287 - accuracy: 0.7841\n",
      "Epoch 24/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5470 - accuracy: 0.7475\n",
      "Epoch 25/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5219 - accuracy: 0.8024\n",
      "Epoch 26/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5239 - accuracy: 0.7739\n",
      "Epoch 27/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5160 - accuracy: 0.7943\n",
      "Epoch 28/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5342 - accuracy: 0.7719\n",
      "Epoch 29/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5306 - accuracy: 0.7882\n",
      "Epoch 30/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5233 - accuracy: 0.7841\n",
      "Epoch 31/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5381 - accuracy: 0.7617\n",
      "Epoch 32/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5342 - accuracy: 0.7841\n",
      "Epoch 33/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5151 - accuracy: 0.7943\n",
      "Epoch 34/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5202 - accuracy: 0.7882\n",
      "Epoch 35/50\n",
      "50/50 [==============================] - 0s 4ms/step - loss: 0.5031 - accuracy: 0.8106\n",
      "Epoch 36/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5224 - accuracy: 0.7841\n",
      "Epoch 37/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5043 - accuracy: 0.8065\n",
      "Epoch 38/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5180 - accuracy: 0.7963\n",
      "Epoch 39/50\n",
      "50/50 [==============================] - 0s 4ms/step - loss: 0.4977 - accuracy: 0.8045\n",
      "Epoch 40/50\n",
      "50/50 [==============================] - 0s 4ms/step - loss: 0.4993 - accuracy: 0.8086\n",
      "Epoch 41/50\n",
      "50/50 [==============================] - 0s 4ms/step - loss: 0.5341 - accuracy: 0.7882\n",
      "Epoch 42/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5175 - accuracy: 0.7800\n",
      "Epoch 43/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5111 - accuracy: 0.7862\n",
      "Epoch 44/50\n",
      "50/50 [==============================] - 0s 2ms/step - loss: 0.5110 - accuracy: 0.7719\n",
      "Epoch 45/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5256 - accuracy: 0.7882\n",
      "Epoch 46/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5379 - accuracy: 0.7800\n",
      "Epoch 47/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5180 - accuracy: 0.8106\n",
      "Epoch 48/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5163 - accuracy: 0.8045\n",
      "Epoch 49/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5071 - accuracy: 0.8045\n",
      "Epoch 50/50\n",
      "50/50 [==============================] - 0s 3ms/step - loss: 0.5268 - accuracy: 0.7556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x29ffe100be0>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "ann.fit(X_train, y_train, batch_size = 10, epochs = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "ae073cb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred_prob = ann.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "08002a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7034219"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_prob.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "68cff703",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.56      0.59        39\n",
      "           1       0.80      0.83      0.82        84\n",
      "\n",
      "    accuracy                           0.75       123\n",
      "   macro avg       0.71      0.70      0.70       123\n",
      "weighted avg       0.74      0.75      0.75       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = (y_pred_prob > 0.7)\n",
    "#Confusion Matrix\n",
    "print(classification_report(y_test, y_pred))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Artificial Neural Network Classifier')\n",
    "display.plot()  # doctest: +SKIP\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d7a3840",
   "metadata": {},
   "source": [
    "# Ensemble Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "b51fe475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboostNote: you may need to restart the kernel to use updated packages.\n",
      "  Downloading xgboost-1.6.2-py3-none-win_amd64.whl (125.4 MB)\n",
      "Requirement already satisfied: scipy in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from xgboost) (1.7.3)\n",
      "Requirement already satisfied: numpy in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from xgboost) (1.21.5)\n",
      "Installing collected packages: xgboost\n",
      "\n",
      "Successfully installed xgboost-1.6.2\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3722f338",
   "metadata": {},
   "source": [
    "# XGboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "37fd1ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "xgbc = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "40372011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "283e3580",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation score: 0.77\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(xgbc, X_train, y_train, cv=5)\n",
    "print(\"Mean cross-validation score: %.2f\" % scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "33eb9973",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=10, shuffle=True)\n",
    "kf_cv_scores = cross_val_score(xgbc, X_train, y_train, cv=kfold )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "0690fe30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-fold CV average score: 0.76\n"
     ]
    }
   ],
   "source": [
    "print(\"K-fold CV average score: %.2f\" % kf_cv_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "775b21d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[22 17]\n",
      " [16 68]]\n"
     ]
    }
   ],
   "source": [
    "ypred = xgbc.predict(X_test)\n",
    "cm = confusion_matrix(y_test,ypred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "b63f7412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.56      0.57        39\n",
      "           1       0.80      0.81      0.80        84\n",
      "\n",
      "    accuracy                           0.73       123\n",
      "   macro avg       0.69      0.69      0.69       123\n",
      "weighted avg       0.73      0.73      0.73       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, ypred))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,ypred,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='XGBClassifier')\n",
    "display.plot()  \n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, ypred)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b76210",
   "metadata": {},
   "source": [
    "# Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "608cdae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8130081300813008\n",
      "[[17 22]\n",
      " [ 1 83]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "bag_model=BaggingClassifier(base_estimator=dtclf, n_estimators=100, bootstrap=True)\n",
    "bag_model=bag_model.fit(X_train,y_train)\n",
    "ytest_pred=bag_model.predict(X_test)\n",
    "print(bag_model.score(X_test, y_test))\n",
    "print(confusion_matrix(y_test, ytest_pred)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "519d9a67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.44      0.60        39\n",
      "           1       0.79      0.99      0.88        84\n",
      "\n",
      "    accuracy                           0.81       123\n",
      "   macro avg       0.87      0.71      0.74       123\n",
      "weighted avg       0.84      0.81      0.79       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, ytest_pred))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,ytest_pred,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Bagging_Classifier')\n",
    "display.plot()  \n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, ytest_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a28c39",
   "metadata": {},
   "source": [
    "# RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "a6745266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7642276422764228\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred_randomforest=clf.predict(X_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred_randomforest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "b0c31b96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.54      0.59        39\n",
      "           1       0.80      0.87      0.83        84\n",
      "\n",
      "    accuracy                           0.76       123\n",
      "   macro avg       0.73      0.70      0.71       123\n",
      "weighted avg       0.76      0.76      0.76       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_randomforest))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred_randomforest,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Random_Forest')\n",
    "display.plot()  \n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, y_pred_randomforest)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f520c00",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "0ad43bbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7886178861788617\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model = GradientBoostingClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "y_pred_grad_boost=model.predict(X_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred_grad_boost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "48e76e08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.49      0.59        39\n",
      "           1       0.80      0.93      0.86        84\n",
      "\n",
      "    accuracy                           0.79       123\n",
      "   macro avg       0.78      0.71      0.73       123\n",
      "weighted avg       0.78      0.79      0.77       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_grad_boost))\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred_grad_boost,drop_intermediate=False)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Grad Boost')\n",
    "display.plot()  \n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.show() \n",
    "cm = confusion_matrix(y_test, y_pred_grad_boost)\n",
    "cm_display = ConfusionMatrixDisplay(cm,display_labels=[0,1]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d6fcd5",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "6f47764f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC of Logistic regression classifier : 0.71\n",
      "AUC of Decision Tree classifier : 0.72\n",
      "AUC of GNB classifier : 0.71\n",
      "AUC of SVM classifier : 0.50\n",
      "AUC of ANN classifier : 0.70\n",
      "AUC of XGBoost classifier : 0.69\n",
      "AUC of Bagging classifier : 0.71\n",
      "AUC of Random Forest classifier : 0.70\n",
      "AUC of Gradient Boosting classifier : 0.71\n"
     ]
    }
   ],
   "source": [
    "print('AUC of Logistic regression classifier : {:.2f}'\n",
    "     .format(0.71))\n",
    "print('AUC of Decision Tree classifier : {:.2f}'\n",
    "     .format(0.72))\n",
    "print('AUC of GNB classifier : {:.2f}'\n",
    "     .format(0.71))\n",
    "print('AUC of SVM classifier : {:.2f}'\n",
    "     .format(0.50))\n",
    "print('AUC of ANN classifier : {:.2f}'\n",
    "     .format(0.70))\n",
    "print('AUC of XGBoost classifier : {:.2f}'\n",
    "     .format(0.69))\n",
    "print('AUC of Bagging classifier : {:.2f}'\n",
    "     .format(0.71))\n",
    "print('AUC of Random Forest classifier : {:.2f}'\n",
    "     .format(0.70))\n",
    "print('AUC of Gradient Boosting classifier : {:.2f}'\n",
    "     .format(0.71))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88e1e1b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}