LoanPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "  </tbody>\n",
       "</table>\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",
       "   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",
       "   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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      "Loan_ID              614 non-null object\n",
      "Gender               601 non-null object\n",
      "Married              611 non-null object\n",
      "Dependents           599 non-null object\n",
      "Education            614 non-null object\n",
      "Self_Employed        582 non-null object\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           592 non-null float64\n",
      "Loan_Amount_Term     600 non-null float64\n",
      "Credit_History       564 non-null float64\n",
      "Property_Area        614 non-null object\n",
      "Loan_Status          614 non-null object\n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Data Cleaning and filling missing values **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: sum(x.isnull()),axis=0) # checking missing values in each column of train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Gender = data.Gender.fillna('Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    398\n",
       "No     213\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Married'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Married = data.Married.fillna('Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     345\n",
       "1     102\n",
       "2     101\n",
       "3+     51\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Dependents'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Dependents = data.Dependents.fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Self_Employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Self_Employed = data.Self_Employed.fillna('No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.LoanAmount = data.LoanAmount.fillna(data.LoanAmount.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "84.0       4\n",
       "240.0      4\n",
       "120.0      3\n",
       "36.0       2\n",
       "60.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Loan_Amount_Term = data.Loan_Amount_Term.fillna(360.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    475\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Credit_History'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Credit_History = data.Credit_History.fillna(1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: sum(x.isnull()),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>146.412162</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>128.000000</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>66.000000</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>120.000000</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>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             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  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting traing data\n",
    "X = data.iloc[:, 1: 12].values\n",
    "y = data.iloc[:, 12].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Male', 'No', '0', ..., 360.0, 1.0, 'Urban'],\n",
       "       ['Male', 'Yes', '1', ..., 360.0, 1.0, 'Rural'],\n",
       "       ['Male', 'Yes', '0', ..., 360.0, 1.0, 'Urban'],\n",
       "       ...,\n",
       "       ['Male', 'Yes', '1', ..., 360.0, 1.0, 'Urban'],\n",
       "       ['Male', 'Yes', '2', ..., 360.0, 1.0, 'Urban'],\n",
       "       ['Female', 'No', '0', ..., 360.0, 0.0, 'Semiurban']], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'N', 'N', 'N', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N',\n",
       "       'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'N', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N',\n",
       "       'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N',\n",
       "       'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N', 'N',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N',\n",
       "       'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'N', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'N', 'N', 'N',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'N',\n",
       "       'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 = 1/3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Male', 'Yes', '3+', ..., 360.0, 1.0, 'Rural'],\n",
       "       ['Male', 'Yes', '0', ..., 360.0, 1.0, 'Rural'],\n",
       "       ['Male', 'Yes', '3+', ..., 180.0, 1.0, 'Rural'],\n",
       "       ...,\n",
       "       ['Male', 'Yes', '3+', ..., 360.0, 1.0, 'Semiurban'],\n",
       "       ['Male', 'Yes', '0', ..., 360.0, 1.0, 'Urban'],\n",
       "       ['Female', 'Yes', '0', ..., 360.0, 1.0, 'Semiurban']], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding categorical data\n",
    "# Encoding the Independent Variable\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0, 5):\n",
    "    X_train[:,i] = labelencoder_X.fit_transform(X_train[:,i])\n",
    "\n",
    "X_train[:,10] = labelencoder_X.fit_transform(X_train[:,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding the Dependent Variable\n",
    "labelencoder_y = LabelEncoder()\n",
    "y_train = labelencoder_y.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 3, ..., 360.0, 1.0, 0],\n",
       "       [1, 1, 0, ..., 360.0, 1.0, 0],\n",
       "       [1, 1, 3, ..., 180.0, 1.0, 0],\n",
       "       ...,\n",
       "       [1, 1, 3, ..., 360.0, 1.0, 1],\n",
       "       [1, 1, 0, ..., 360.0, 1.0, 2],\n",
       "       [0, 1, 0, ..., 360.0, 1.0, 1]], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding categorical data\n",
    "# Encoding the Independent Variable\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "for i in range(0, 5):\n",
    "    X_test[:,i] = labelencoder_X.fit_transform(X_test[:,i])\n",
    "X_test[:,10] = labelencoder_X.fit_transform(X_test[:,10])\n",
    "# Encoding the Dependent Variable\n",
    "labelencoder_y = LabelEncoder()\n",
    "y_test = labelencoder_y.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, ..., 360.0, 1.0, 1],\n",
       "       [0, 0, 0, ..., 360.0, 1.0, 1],\n",
       "       [1, 1, 0, ..., 360.0, 1.0, 2],\n",
       "       ...,\n",
       "       [1, 1, 0, ..., 180.0, 1.0, 0],\n",
       "       [1, 1, 2, ..., 180.0, 0.0, 2],\n",
       "       [1, 1, 0, ..., 360.0, 1.0, 0]], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shrikant/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying PCA\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components = 2)\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.fit_transform(X_test)\n",
    "explained_variance = pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Logistic Regression to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic Regression is:  0.7073170731707317\n"
     ]
    }
   ],
   "source": [
    "# Measuring Accuracy\n",
    "from sklearn import metrics\n",
    "print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,  60],\n",
       "       [  0, 145]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9354e72d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Logistic Regression (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93492a1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting K-NN to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of KNN is:  0.6292682926829268\n"
     ]
    }
   ],
   "source": [
    "# Measuring Accuracy\n",
    "from sklearn import metrics\n",
    "print('The accuracy of KNN is: ', metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 11  49]\n",
      " [ 27 118]]\n"
     ]
    }
   ],
   "source": [
    "# Making confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93492b7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('KNN (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93492a16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('KNN (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting SVM to the Training set\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of SVM is:  0.7073170731707317\n"
     ]
    }
   ],
   "source": [
    "# Measuring Accuracy\n",
    "from sklearn import metrics\n",
    "print('The accuracy of SVM is: ', metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  60]\n",
      " [  0 145]]\n"
     ]
    }
   ],
   "source": [
    "# Making confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f934a469a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('SVM (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f934a3aa3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('SVM (Test set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Naive Bayes to the Training set\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Naive Bayes is:  0.7121951219512195\n"
     ]
    }
   ],
   "source": [
    "# Measuring Accuracy\n",
    "from sklearn import metrics\n",
    "print('The accuracy of Naive Bayes is: ', metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  3  57]\n",
      " [  2 143]]\n"
     ]
    }
   ],
   "source": [
    "# Making confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f934a378048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Naive Bayes (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f934a378ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Naive Bayes (Test set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Decision Tree Classification to the Training set\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Decision Tree Classifier is:  0.5365853658536586\n"
     ]
    }
   ],
   "source": [
    "# Measuring Accuracy\n",
    "from sklearn import metrics\n",
    "print('The accuracy of Decision Tree Classifier is: ', metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20 40]\n",
      " [55 90]]\n"
     ]
    }
   ],
   "source": [
    "# Making confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f934a2e0f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Decision Tree Classifier (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f934a31fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Decision Tree Classifier (Test set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Random Forest Classification to the Training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Random Forest Classification is:  0.5853658536585366\n"
     ]
    }
   ],
   "source": [
    "# Measuring Accuracy\n",
    "from sklearn import metrics\n",
    "print('The accuracy of Random Forest Classification is: ', metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[22 38]\n",
      " [47 98]]\n"
     ]
    }
   ],
   "source": [
    "# Making confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0OwvPiS8uoXjqVN4/eDBBLRSE+LDsBOJl1EOZPbfHCIBg+pVA2PrWr1n69PU0tBoZTw+eqGLp09cDxC0UBKEnUVxYLAJAiJl+pTJa/efb/MLAoqG1gdV/ji/9tSAIQl+gXwmEQyec01y7LRcEQehP9CuBUDDY2WXPbbkgCEJ/ol8JhA0X3U52WmD66+y0bDZcFF/666998xtMufhLHHjnbfI/dQYP/s/Dce1PEITEUlFZQemWUubfNZ/SLaUSte1CvzIqW4bjRHsZPfY/v0pE84Qks7GgPOH7WXEoupToGSqDptSmhLVB6MBK5WFFb1upPICEGt77wr3rVwIBDKEgHkWCG9FEAkf7e29L9Lap4anDITWuQwsudEcqj3ifm55Cv1IZCYLQ/5BUHtHTJwSC1jrZTQhLT2+fIPRlJJVH9PR6gTAARe2xuh7b6WqtqT1WxwCiSFMpCELCkVQe0ZN0G4JSKhXYB1RrrS+L9ff57Wl4a2r5uKYm8Y1LEANQ5LenITJBELofSeURPUkXCMAKoBIY3Jkfp6sUTtcZkTdMNiIMBCFpSCqP6EiqykgplQ/MAf5fMtshCIIgJH+GcA9wI+Bx20AptRRYClAwYmQ3NUsQ+i5Sb1lwI2kzBKXUZcBHWuv94bbTWm/WWhdprYuG5+R2U+sEoW8i9ZaFcCRTZXQh8BWl1PvAr4FpSqlHk9geQejzhAvSEoSkCQSt9U1a63yt9Vjgq8AerfXXk9UeQegPSJCWEI5eH4cgCEL0SJCWEI5kG5UB0Fr/L/C/kbb7KONE0hJIRZukLFas87Hv/97R5ege5qYafN276nr0NXpawrOSqSUBid5AgrSEDnqEQIiW9JT0pCSRiiVJWaLpCUmzgtuQzOvRm+gJ9y4YCdISwtGrBIIgCPEjQVqCG2JDEARBEAARCIIgCIKJCARBEAQBEIEgCIIgmIhAEARBEAARCIIgCIKJCARBEAQBEIEgCIIgmIhAEARBEAARCIIgCIKJCARBEAQBEIEgCIIgmIhAEARBEAARCIIgCIKJCARBEAQBkHoIfYqKyoqQwifjzhyX7GYJgtBLkBlCH6GisoJNuzdR46tBo6nx1bBp9yb2HdiX7KYJgtBLEIHQRyjbWxZQJxegubWZXS/tSlKLBEHobYjKqI9Q66t1XF7nqwtZ5qRakpKKgiDIDCEZPLkTZk+DSYUsPncdZ+2IX60zzDPMcXmuJzfg+74D+xxVSxWVFXG3QRCE3k2vmiG0tLfgbfEmuxnx8eROWH8rnDoFwGBvHdNXboNbzoXZc0M2j3S++en5AJRMLWHT7k0BaqOMtAzmTJkTsP2ul3Y5qpbK9pa5zhJ6/TVPEtFeN+seCkKy6VUC4RPNg1lxaGaymxEf99/tFwYW6Y0txnJTICyviu4cNxaU423xkp+e7+/M7aqgmVNmUjS+COjonJxUSNChcvJ3Ygry0/L937vtuj+507gWHxyB00bADascBWVXEW8nHvz7SNdtY0E5AB+3fUxTe1Onjyskl74yaOpVAqFP8MGR2JaHYcVbxWw8p0PVU1xYHDDK97Z4QzqkdZ5cR6FgVznlnxyMd9CJjuN0pzCwzZ44ctj4Dt0iFKI9T6sTj3c/dpp1M5ltqZRWz+j0cYXk0pMHqyuj3E5sCN3NaSNiW55g5kyZQ0ZaRsCyjLQMSqaWdMvxw+Iwe+LUKWO5IAhdjgiE7uaGVTBgQOCyAQOM5V2Jacj+1YWP8sDuDE5L9aBQ5HnyKJ1R2jO8jBI4ewrAZsRn9jTjuyA4UFFZQemWUubfNZ/SLaX9ztlCVEbdjaX6MPXkvlE57L11DrPOj10lYlcXhcWmilHAdX+q5xuvZLD/vmV4FxT7X4JaXy05nhwWTZrHuHM/HXN74ua0EYaayGl5Z0myGipaMlQGTalNohZKIlZwp+V0YXngAVENmPrCvVNa62S3IWqKzpmg95X9JtnNSCjWQ2TXP947uhytovt9OCOjt9ULGhafu47B3lC7wcnRedy8I9Q7KT0tne/O+C7FhcXda1QO7rzBmD3dur7znffsac5CZsRIeHJPwKJYXmi3617dWo39nXK7blurdrK68m4ONh4mz5MnsSA9gNItpdT4akKW53ny2LRkUxJalDjmDZ63X2tdFGk7mSEkEXsH5CQYAN57971OB5FZXkKeamfPomxvrWOEc0tri98NNT/d2EdwZ2m1077cqfOLtD6AoNlTQryMYlRDxevFMyptlP+z03UDIxZk22vbaGltAWIfiQpdg1twp9vyvogIhCRjdbjBZKZkUvl2ZVxTWGv/Dfl5DKyqYetEWH0JHBoCBcfhtr8NjOolCO4kg9vrdg7Rrg9g9tzEqnK6Qg0VJW7C5Y6X7vALA4tIsSBC1zPMM8xxhuAW9NkXEaNyD8YtP1HZ3rKY9vPGmhIemZTK0rlwMAe0Mv5fP+0UgwYMcvxNn3kJkmXED4OMRHsmJVNLeq4HXjchAqEHk6iOw7ugmBuvzKYh8FnnlG5Fa93lL0H+9goWn7suOV4+s+caNogRI0Ep4388NokE4CZs+4wQ7qUUFxZTOqOUPE9ez/PA6yZEZdRDaWpvIseT4xhEluPJ8atgIum8re0+bKt3XH+y6STLZi0La6eIpO4Jt/6sHfuYtHKbEY0NyfHySbQaKkrcrsvMKTPZtmdbgNqov41EeyrBwZ3RIpHKQkLI317BjLWPMLj6mGlEbWG52XnlfaqFpX+/lYa2Dq+b7NQB3Pepm1l4aGZMXjHh9KPRvARuBuFIhuJZa/dAY6C+3B9s1oPcPrsKx+uTNZOZnzlXvIz6GH0hUlkEQhI5a8c+Jq/cTlqjaScIGj0vHG10mKsr7+ZQ4xEKskawoXCVfzngz2Vkx2m0UjK1hAd2PxBxVOqWy6jTuHn5HDlsBoz1faHgxMLRxv3dWFAu+YmEHoMIhCRy4fpdHcLAImj0bHUcTgTnMnLcxpxJFBcWc7TtKLte2sUx37GwLqwJzWXk5uUDPTJALFlIcjuhJ5A0gaCUGg38CjgNaAc2a603Jqs9ycAtPiDuVA0uFI0vomh8Ufd2KjesCg02s+hHqqNIdDa5nRQ7EhJJMmcIrcAPtNZ/VUp5gP1Kqd1a67eS2KZu5cGLsrljUoM/LmDDc7DwdaL3kR+UDURn0Iplm6vfm8jGiS8kxlBmdfarf+S8vouEX7LozDUL95vfDHsR78ATjuviTbUgJJa+kLoiaQJBa30EOGJ+9imlKoFRQL8QCFurdnLDtCYsJcHBHFg6F0hLY+G8IB95lxoBXfYADvH4PybEUDZ7rtH+JAWIdTexXLNI23484CRKqYAIaItwcSoiELqXvqLK6xFxCEqpscBngb84rFuqlNqnlNr38TEXFUsvZMXrG2iiLWBZQwasvnxQoArFyu9z5DBo3WF4Nn3589PzQ/6ciOaB7dKHugcGiCWVBGRglQA3IdEk3aislBoE/AZYqbUOmRtrrTcDm8FIbtfNzYuO4BH81C/C3j+5ft/63S9S237McVeHOB64IFyNgNIbo1ZRJN1PuivyFPVWEpSBVVItCIkmqTMEpVQ6hjDYqrX+bTLb0mmcRvCPP+b/vjXvMGNHPUbK0sOMXWF8X133mOvuUlQKW6tso8Uwydl6st+zI7PnGhlG/1pp/O+PwgBiLgSktcbb4g0R6pJqQUg0yfQyUsCDQKXW+q5ktSNunF5uk+lfh+fOAMxU1padoCHdfXdtuo2lfzdGiwtHzw2fnK2+AYicfM7aJumzBMEghgysdq+jYJuRUx1t8TIS4iGZKqMLgUXA60qp/zOX3ay1ftLtBx9lnOhxlvzlHxzGqXTB92YFCgOLhgxIbYO2VPd9NrSdYtk7d1JzYTpnrZ3GdHvqB6AlK51n107j7SgK5HTmem0sKOesHfu4cP0udPVKfKNyGfzdm/vmiL6+gU3jX/B/TYQbZ7hrntmWysJROXgc6lN0xsDe2VQLguBEMr2M9hLSXYYnPSW9x1nzrdTSAFsnwoqZUJttrnQ5u7YUUJqwRXCO+Y4Zqau/ls9fU4cyYV0Z2d5aGvKH8caaEhoWFON2JeI1LOdvrwiIoB7sreuzQWQbz6nAmKzC/gP72fbctrjTjbtR3VpNc1o7L9x6GZes/HWIkP/TbdOZ3tkTEYQEkHSjcm/nv344mV++/zSHBpsLohBxBSeMmgThsCew8145jpevvCVwgyiT28VC/vYKCtc+gsdbF3oafTiIzKpw9scX/9hlbpz2SmoH5k1Go7lw/S481XX4RuXywq1zOHjl55heHddhAAlWEzqPCIQ4qKisYJPveZojdO4BaLhu7Je5v3W/o4eIxbdTzmHFhJ+G9chJpPosf3sFk5dtIq2xOaSQjj9gro8FkUFgHMBKn3MKsK5w43x7XhFvzwusaJjZ5rJxDEiwmhAPIhCiwBqp/+yJn/GO9x3/8lSVSpuO7S2eWDCRCfOXUFI5nnufutd1uw03v9KRJTRGt8SP2z4GYHjq8IDP4ZiwrswvDJbOxV874WAOLLoKvn4VjKlPYcPOdSx84E990nU015PrmG48EW6cToFldqpbEzA1QILVhPjoEYFpvYGdZdsChAEQszDITMtkzfw1gDFa8wzwOG432pcSoF8GHN0Slcbvjmj/a2pvoqm9CW+Ll+a2Jn/SNCfXRYtsrzEKXn0JIYV0tAIUHPS0sbTpMbbmOQfJ9XbmTJnT6904JVhNiAcRCC5UVFZQuqWU+XfNZ93D63iu9qW49peRlsF3ZnwnYNniixeHdEDpaen8+Jl2550EqWzCGaUtlldFF6vQkG+MgiPZNhrSDaHhJ4z/fG+jaHxRr6+YJdXYhHgQlZEDwXrYOl8daGL0iQKFQqMDCqDkb6/wewzNzB/G0B9ezH83vojvlA+AzNRMmoZmAg4VzhzcEsMZlYNnA+G2tYzj0YSChwiNaG0LLjmZehKJdONMlBooFkqmlgQ8u9D7ZjlC8hCB4ICTHjacMMjz5EXl0WEZbred2czqq+HQkBqGVj9NY3bHzuub6rl+WhppvhS+8VfbTKGTeX+iMTzHahwvCMqucWJUDg+Zx7GMtPbj7juwj2eefYKP205ScI1lpE5CKc0wdEV8i9aaTDPgpCnVUC9mhglAKa2eEbfQlGA1IR56lUBoaW/plmjbcN4/Ttxy7S3+CmPB2Ns7Y+0jbDuzOcBoa8QsBI7LT+lWfnTFQL5xZEjEjqG6tTqiwTISjgLQhewWo0O3aM3KoHLtIsdt89PzqaisYPue7TS3Nxt2CCurK7Dw9Z7lytoVMS5WpPHGgvKI9Q4SleNIgtWEztKrBEJ34eZt4kSeJ8/44KBr8bYGCi9PdR0rvhZqtHXio/aTbHxjQ2i+InMEqT84zGLTf73ha4ECwamsZjiiNTjmefIobhvBTZe+yaKr2hl9HO58rpm5ax/haNsc3p5XFDDS9rZ6HYVNQ4Zhh+gJrqzB7U0q4XIczZ5rOBGYbXQafAjJJenPTwLoVQKhuyKVF01dFKqHbTX6/BbbFUtPS6dkaol7ZHDQS/vgRdnUZjdE1YZcT27oQtsIUmFEEE9fuY2/pg7Fu6BjRBjrNXLLmmnHM8DToZ/2GKqsQ+Zof/POOhas3AZAw6xpAEZRF+0+2/LbIZJcD2HFW8X8ZoyROcWtEE2s5J80ohSvfm8iDOk4jlXQyJUIOY6WV80MWzBHSB59JU9YRIGglBoMDNda/zNo+We01q91Wcsc6C6V0bgzx7GgbYFZf7jOGAk/a6zzB2ypHC6adhnFhcX+NkXqiFdPV0ax0Aikp6UzZ8qc0NmBwwgyvbGFCevK/ALBqs1rtcWqqRwOJ0NkMFrrCKP9Fi5cv4vB5xsR1RsHGsd0m20VHKdn1EMYlM3VtRcAHW2OF2t/2G0ykYQBhE9kaNt3otopCMGEFQhKqQXAPcBHZqrqa7XWr5qrHwYmdW3zAvlE8+DuS/mcNROmmekintwJNYahb25dDi/cOodZ598SsyHyo/aTrusGZmZzsqmBMVkj2VC4ioVZDjpjlxGkFUNgCYPMtlSqVfQeLnZDpNuI/mTTSU42ObffGu0Pru6o8WDdp/cmvckDLzxMS2tHXEV2M2zYlwO3ru4x9gNIUHW4eHAW2WclAAAgAElEQVSqP90ThKYQFUl/fsLgHIMfSqQZws3AZK31EaXU+cAjSqmbzdoFMTph9mJmz/V3XJY3zaxDxir7jCXSTME1EvYk1PwiB25YB5eG6SCDRpAdKSY0w7aUMveCuZz3qXNjOjULyxBZuqU0bNEVp3V+ryMH9U/xGVM4OqCRXS/u4pjvGAVZI9gwaRUL5/ccQWCRKE+jTncMUkSoV9PTMjF3hkgCIdWsfYzW+hWl1MXAH5VS+TiaUfs+9pfd6cUP91DMmTKH3z63g4a2jhFgdjNsLCc6jxLbCDI4xUSNr4atz20l76W3ufWut/hDbh03XwIrh0Bqu5FhNW9wXogLYnAitMmnT+b5t54PUA1ZthIgRLWU3Wx6HTmMZC19d9H4IorOKnK9ZkkjyMXzrLXTaPjaFXHt0k2lGZWXEQQMPoK5d3R5VMGI0SJJ8BJHT8vC3FkiRSr7lFJnWF9M4fAl4HLg013Yrj5J0fgiNp+7njFZI1EaxhyDzTtNbxuIHPU7ey7cuh5GjHRMMdHc2sy2qhf4Q24dS+caRl+UWXtBdSQ6q6g06ihYAXg1vho0mhpfDc+/9TwXn3OxP1o315PLNdOu6ZhBzCgl15OLAsb4Uo3214w02hXUkV1de4HfwAqJEwZbq3Yy9plppPy+kLHPTAusMBctDpXupq/cRv72yDUmkoVWhK2bbY+uL91S6r/PbtsG33v7syH0TyLNEL5LkGpIa+1TSs0EFnRZq3o54QzfC0fPNSqhTSo0OqJgIrlhmiPIQ78vxGmSdmgwfPMK9wI8VqKzktfhicr7/R5D9vX7/7WfTUs2daTD/uGjNOSX88aaEooXFDPuzHEdnfvXwzc30Wyt2snSv9/qn2UdbDwcWGEuWqIw0PcmYs1yKknwBCciCYSTwCeBd4OWfwF4uUta1E/wxVk165MpA/mg3SG9BeGrsQHU+mqYfMsmvDc6uzzV+Gq4YeNiPmz1UTDPiiyuYdKyBzjadhSC0jZHJF7lok21s/r7ioYgIdbQdorVlXe7CgQnNd6KCAb6eHBTGzaltnWZnjnWDl6S4AlORBII92AYloNpNNeJtcsBtym9febwl9Vz+eL3HwvMahqtR8mTO/np7xopnekQ5BaFjjm3EdIamyk4bkQOO/FBm88hsriFWWv3MOv8W5x/5MDVtRdAPH1MUPTuoUHO0uVQY/iZlf2eeFu8ri6eVpK/ztJZXbLlIdZZYu3g3WJPJAle/yaSQBjrFGugtd6nlBrbJS3qiXRBUrZ3r/4cea/+k/Mefhna2yAlFeZeGdV+tz61gVuL22hIN+szpxCTz1d9uuGhtOG5QMO0G0mNLA5S7bgJsYIs95lV/snBeAcGqfEcXDxbstJ5Y018SeDiiZNZ8fqFMMQ5JTqEd1iItYOXJHiCE5EEwoAw67IS2ZAeSyfyy0TTKYx54i98+rFXDGEAxv+dT8B5k5z3awqlrXmHAzrxtlRiVsk0pxkd/Pv3GN+tYLv8+hSqPM5qJCvW4MSoHK5vvIM//3kPhxqPGG6khati09/HQpAAchJi2akD2FDoPrOyInvfe/c9f6zF3Vkj2XDzlczdXI6n+pi/VnW89oOQmYiNiF5GsVTeCyLWDl6S4AlORBIIryqllmitt9gXKqW+Bezvumb1ICLklwlmxaGZcNznvK/UVBhkfJy6fpd7EZzg/dqE0uprOqcmCubgEBi70uhg37/HSFK3/75SLjvuHJw2tMHY5u4fnc+2Pdv8gWYHGw+z+LWbKc/7O0XjiwI8iTaN2u3P8tlZFo/KYbDN1mJ5ZN10qSG8/IF8YQSS0vDq2/uMJHtmh3mw8TBLU5/gqu3zuGJCfK6mPYHOdPCSBE8IRmknTxdrpVKfBJ4AmukQAEVABnCl1vqDLm+hjaJzJuh9Zb/pzkO6ewMpBX+t7PRu9aSzUU6X3mm/s6f59d0pa6IrjBMt6a2w6c+D+PSi6/AuKKaisoKfP/1zWttbQ7ZbPdy9FnSuJ5c1164JWZ6Zksnw1OERZ0356fkB21jfz9qxj+krtwUIz5asdJ6955qQmsThuOPhO1zbvWXJFodfJJbq1mrCvWsQeQaxsaC8z/i7C93LvMHz9mutI74wYWcIWusPgQvMgLQJ5uJdWus9CWhj7yCK/DKdoWHkMAZWOxj8gvf75E625h1mxdetVNmJpSUNVsyE6yZC2ZZSan21aAcdVEsa3N+639VIecx3LGxnFU1HZt+morKC7c88xIetPnJvMCZBR7MNtda3CqYx4WtX4LRHt2Art3ZHm9U2XrqrprIgxEOkXEYDgFLgTOB14EGtdWu43/Q5nPLLADQ28NQrd1A2EXa9tIs6Xx25nlzmTJlD0Xh3QWypVNb98Gy2HXqBqsGGoXTDc7Dg3XSeWzONAwXlrDg0k6deuYOPf1PGkssNvX9XUd9UHzG5HeDvZN2Ml7FUaAtHRWUFm8t/zindCgqODuxYV+Vp507f85RWjg9Rd4TzxQ+X0bWisiKi6kSieoX+QKRu5n+AFuDPwCygkOjzJPUNLH3+TzfA8Y7kbRw7xse/KGPH5SlGx4Ux2ty+ZztDU4c6dhZWh7m1aif3nHyFFtOIeDAHlnwF/p53PqPmF3HWjn1w238w88MjDP9R1woDi2gK5FgdoZPwmHz6ZKAjzXOwR0wsHWrZ3jL/NXVra9neMsadOS5A6ITzxS+ZWsK9T93rejyntlhtDhYkkYK+BKG3EqmrOUdrPRFAKfUg8ErXN8mdjzJOJCeBVGk6ix9QDA4qHXlbcTundGikb7hoz40F5azbc2dA9k+AxnT4f+1vsdXUmdPYQtnErlETdQbLY6W4sJgD1Qd4+rWnA9bveWsPw08bDoM6jMreFi+ZKZlUvl0ZUxRtNMFRtb5avzCw6lTXXlfjaGCv8dVQXFjsKhCcjhc82wimM1G9lk3kwvW78FTX4TMLHEVrC1E6fAJFmcV0D32l9oETkQSCv9fSWrcqldwEp91RIMftpfLYUjtbhBSbN3Hr0Ky2H/OF7gvg2Ik6Zv7gUX+ftvoSuiWnbGZqJk1toUFRKSoFrXVI57L/X6EOZi2tLex6aZc/Zbjh+2+4e8YSRVtRWYFSKqIB1vKvz99ewaRlD5De2OIao2AVG8rz5EXtqx9NWdFYo3qDDeRWgSMgJgO5E7GmrrDTlzs4ITYiCYRzlVJWeSYFZJnfFaC11oPdf9r7CPdSzcwfxsCqwM7ErQOKFO3pps/Obgrs/90ETqzkeYwsp/eX30+7Do0zyEjPQCsd4sNeOqMUMDrH+566z696icZAe3XtBdybbczmwkXRWqP7bG8tDxUPZPO0U45ttJOels7MKTPxtniZsfYRfwfrFqNw2Rfm4G3xMnPKzACX2eB92YmmrrbTfQ4XcbzYwdU4vTH66O/lVcbsa2NBeYhHVry5iXpUFloh4SSkHoLWOkJWnL5FWB30mhImL9tEWmPH+tsrUviOzYYARkc694K5fNz2McNThzsep2RqCfc9dV+IN8/JTJj+dXg3zxAGKe2R8xJFIpNUxo8ZT9neMteOtv5UPctmLQuZGQGOAnLQgEH4ToXGWjiW/cRdAH4ydVDANb39s/WccvLwbYehjYaXUc7gQMO9vSiPFaPgr2qXbcYoZM9lI+X+3xiV8IzaDBddNC0kfgLg7qyRHGx08C4zcQv6ChYGAfutdgmeizH6e8VbHR38xnOM7KSSm0hIBL2qpnJX4/by1PhqePnacRxtW+DX/6pPjGD4dy5hXpCX0bfVOdwy/3d4qh9y1BHnp+e767MVPHcG/mmCPwq5k2qj1Da47m/t/JKXacI9QGyYZ5hjkFLpllJHAZmRmkFGWkZIzYQ5U+Y47r9kagkP7H4gYGSekZbB+mfa2HZms78Dd1USKaj5Tyt4bhHeCUY7nXISLXzdFAwjRsKTHd7R/o7ZXgkP93QQGwpXsfi1m0NsPdAx43IaeYeLVE6YC7NDOU7JTSQkAhEINtxeqlxPrtGhnD+TjX/vKPQyC8P1KqDUps1FdbC3jlkrdjCr5lw2LRlAc1oUBZWDO3/b9xSV4q5OCRIc2c1GrYXVl+iwwiBcegM3AXmyqZ7Ne7JZP6mZqiHwyTQP06ddziNZoWqPpvYmiguLOdp2lCdf3MXR+g733Kxtj0aVS8mqyJbW2Ezh2kd4+cpxHSvjKDu5tWon6/bcSZ2vjnWeXO4b/QNqLjFDyQvSuSbvGspfKg+ZNQWr0MKpZOwC5wv/PofP/9sjcZfIdBJikptISAQiEGy4vVT2kW9YXWu4NBdLbvIbSjtbhCScbn1YAwxqMVUlx62U1bDoKvf92Ue6dt23FV3sJiBHH4NvVzTwbfM0WrJO8ewngPPNDcy8S8vNZIBPrZ3GI+NXwCW3QH2DsU0bjL20jIaM8ELSX5HNxOOtw3PVLayervigzcfdA0aw4cbLWLjlxfDJB+sbjJG1PSfUV6Ah3Vhd56tj6YE72Mwt1FwyyChEMyE/IK1FrIZb+2yhurWav33tM3y+fn18iRKt6xdENKkrxHgsREIEgg23l2rcmePgUBQ7cNMFBy0v21sWZ0sDscpwLnxdEax4Ced5s2nJJv/34anD8bZ7/Z/BEJC/eOpnATMMpWHOgcB9pTe2cOH6XbDzFrhzHTz+GGDVfD7MocOPMvzDJ7h0+pUUndOhPjv0XBhhoGGMTbBZlE2EZRfW02A26eCpIyzO+D3lv7kmKCCwnLN27GPW2j3wwRFOjMrhvUvP4byy/R05odIDD9nQdoplVf/NGkJTcEDs3lKOz1GYEpmRCC6hueLQzIDZQjS5iUIGNP5MvqukhrMgAiEYp5cq6pFVBB2x5babCENfrieXY746CnypbHimzShjOf+LRsZU2yxlw5/SWHq5oqHDg5js1AEh+v6P2z72f9715i52vriTWl8t2c2apnT86iit4H8+Cxd6AztqT3Wd0bk8/muAkJrPH7WfDAnaG+biBgpwWpqHd3/RFGDEBxxLh1our3aBcJYtngMM9d25D73gl5duHlyWp5STU0C0hlunmcQDux/gmrZrDBtGT6ETmXyFvo0IhEQSQZ+ttcbb4iXHkxNVDh03m8GYrJG8P82WTspexvK8SQEqiYXzVsFEWF15d0e66qYvMnfBLjzVW/1pn6uvMkpn7zsQmBX0pIN+314fwZgFmOmzj/4bP56oWfi6c8fd3NrML/f80i8QwkUPf9DmY8NPvswP/2s/v8upYcXM8EF6x3zHjHoCFrf9xC8MLOzJBN1mTla9ayeiNdw6zSRaWlt48sVdhtqsk1hup8FEO2AJOa8YM/kKfR8RCInEeokcdMSlttxleZ9qYenLP3I1puZ6cjl6/nMsSr0roHMGw5snIPe/U/Eem3cNwEJs9Yaf3Al3dgitgVU1TF62ibq2azgwbzK7XtoVVRqLg0NCZwFVnnZ/dTW3EXh9U70/d1BxYTEPPf+QowsrwJ2+53lp48Xs+evTtER4UnM8OWyc+IL/+/KPPgjrnOUYs0A6G3Y2UPLvK2nIzwupjxCt4dZtJnG0vnsS6bkRkiU3ShWn0H8QgZBootARLxw9l1P/tYZ/+3yDMeq1vahZLRjqnDYoGl/E0NShAbroxSnjWfidu+GDG2HIEKivh1YzDsJlym/XM6+4/262fupUh6/+cdjwXDNz1/+RA/Mmx5T985tXhMZJWLOHcOU5n9h+PyWF4F1QzOKLFzum2wZjRrH79d20R3hKM9IyWDR1UYARtyE/zx9IaJ/FjD4Od9rsEjdPh6ohMDxlIHc90cTCvxlxDZagBPxCIdqaA+G81bqCaKP3u8wNVugzpCTz4EqpmUqpA0qpd5VS/57MtnQ3Y742jyP3p/Pob2HMMWM6X3Acbsi7MEAXXlxYzKYlm3j8+4/zxyElbLj5FeMl1hqOHesQBhbWlN/kN8NeBDo6Davi2sEcY8Ro1UzemWsIgqg7LeUeNHdoiOkZ5KJ6qRrUzoI37+WNh4w6BOHSVESKWs7z5LFg2gLDYGvjjTUltGZl+Gcx1vkeMhMJbp0Ic+ty2f7p5dy97B7e2ZzBwr8FXsu0xmYmrAt0ALDfj01LNjkacUumlpCRFjj9C/ZW6xHcsMpQadrphBus0HdI2gxBKZUK/AyYAXgxqrP9QWv9VrLaFA3WaDveUP83F0yC1FS+cvvvKdl4NCSIzYpAtTNhXVlolTUnbFN+K5+QxU2XpoS4ejZkGMt/qBRzLpjD9ue2R6U2cqOgPpWSN9pYMVtRm+UUemwIjf/46GnSnn+RNt25qmp5njw2LdnkqEO3RvU3Vd4fcr6N6bDs2lzKp8yh/KUyau6r4a55gTMHi2xv7A4AcXurdRdhVJxC/ySZKqPzgXe11u8BKKV+DVwO9GiBkEiOf3UOW+efT3NbE1oZo3hr8u/UyWV7awPUHwUObplA2Cm/16VmstfTzqi0UYz69CiGplhqqhpyG6E+PfoU3NnNsOEvHso3XcbC84a6Go3B6JhxsR9YpKWkobUOERrppPAffzjFVT+cb6uHHFhgp+x4GTUu52ulKrcE3yFzpgSB17Mhv3ORvnF5q3UncbjBCn2PZAqEUUCV7bsX+HzwRkqppcBSgLzRed3Tsi4gEfWFHyoeaPjgm9qIg06dWIQpf44n19FOkO9L4aoh83jwomx+PV1R236S3AZDldVspdAA9zQa5vqGdFgx5Rh3/aKMku/dgLs4iI7Wtla++wps+wwczTKWDUkZwH2/a2HRX+uBUH3/lme3hKTnDiZFpYTMguzeU2CkynhjjUT6Cv2HZAoEp64lRL+gtd4MbAY4Y9IZ4XMi93CUUq6lFDNTMiP+fvV05Q/IsmjIgNUzFAvfIOyU3xqd3vepm1n6f7fS0N7hbpjdAj9+pp2yCbBiagMN5qD6aCy1GGx3s3YgLJndzqYtD1BwQRRZW8Pla1Lw5Hio/Q8rl1EpE9aVhWSetfT9ZROJKAyC8zDZOTTEsDU4eRn1VHrkzEPolSRTIHiB0bbv+YB7ekmBD9vqHZcfGgz8tdJxXXA0q+V+uuwdI4fPaF8KP36mnYWvw9iVkfMKRUtzGqyd0sKdz4a6d4YQIXmfJVCsXEbZ1c6eUNneGh7Z+0jYfeV58pg5ZaY/IWEwBdkjubfmRr8RPlFFZ7qqZrKkrRYSSTIFwqvAp5RSpwPVwFeBPj0/twLTLOzugk3ayCNkLy4/+jis+dsgRpRcwdvzilwD2gqyIrsJWpW6qF7FwtNGMHTtHN6+tojleSv9AUsHE1R/waJqSIf6JSCwzG1u6CIYrOR2YEREP3hRNndMagixo/hGOavDLCwjNMDQ1KEhMQVZLbBg8On+7/EUnRGE3kjSBIJZge0G4GkgFfil1vrNZLUnWjo7IiutnhHwPSRjpQ4tLn8ox8jb88AvyjgLIz4huMBLduqAwEA1pzZvaoH1OwJSFMxavgMwOtHB3jq2TjSrHnXq7Jwp8HXs8UQmYWcCwxrhVIpREyI4a6s9ud1DxYNYNu2Uv26CZUdpS09h/PcWkXc8tAayhT2ArLiwmKEvH+CX7z9N1WBDsMw+ANvOfoGq+15gmCePppamuIrO2BmVNkpUO0KPJ6lxCFrrJ7XWZ2mtz9Bab0hmW5JBsBrBqbh8Q4ZRu/nC9UaunmumXcPQQbkoFGOyRrL53PUdUcgunPz5T0JTFDSd4sL1u6hcu4jWrAxWX+IQyRoHGaSyIeerMGAAqy8hfKSxhqvfSUWnECAMlIYphwxDb8oaGLMKbpjW5HiNfnRFFt4FxZRMLSFVhQZIpKiOR93b4sXb4mXVf77CwbuhfZ0hdP7nsx11GWp8Na4R1J3JRdVVKiNBSCS9KlK5pb2lT4+y3Ea2B4cYgWNKKT539uf43Nmf8wdz1QAbcS7yYrH8cGgHtnUi3HxJHVXe+xh8UybH43OAMjDVPsPShrDxM7cYgup1ODjksYg/LR/TFmJn0EEFgw4NAbRzHMZH7Sf9KTGc0mG063b/yN5KMuix2SKcci+50ZmiMzJDEHoDvUogpKekRx2mn0jsL7I9MC0gJYSDKsmpkIl9O8vjyNvuJT89370AjjLqGlz6v0+yZPqSmNv/UPEgbv9svV/nPvsAPDjZii3QHG8/1emqbHZSNNy1/B4AFlrnufdPpJ4doRSoMuwNbuuixdLv159yNr5bI3vrutvTW0Rbv1qKzgh9maSqjPoCkQRUfnq+/y/SPsKladDKcKe89mfXxlRgp6KyguunnQpIVfHA+dEHmkWNhu+8CovPXcdZO/YZy57cCUcO0xbhKfMM8PDJNE/cTbD0+24j+ODlVnoLCDRc2xmUOYg8Tx4KRZ4nj9IZpWJQFvosvWqG0Nc5LdXDB23hI3frm+pj8nRxskskYjbgRxu6/tJX4edPwdaJddz8j0epOvwoBSdg9qwIxmoN1w/4LA0Xn9dhUI+DWl8ty2YtC0mYl5aSFjKyt2IMJqwrY8NzNSz5ihk9bZKRlsF1064TASD0G3qVQOiJNoRI7Qm3vrq1mgyV4S9duX53GyumRtZl2z1drP27zUASUYwnHEMbofanxufgdNgHh8Cm8yMbq2+/6SWevacAZn7P7/OvO+nvZM0C2toDjSLB3y28C4rxLihmALAkQTEHCccpxbmkmxC6ABUu02RPo+icCXpf2W+S3YyEsGnUbprT2slQGf5cRsvzVlI2Ab5+FVGN4u9Zdk/Y9e+9+x73l98fMWNoZ8lIy+CX25r9sQZjV7qnvHZj2MmOWtC5g4fyrZRCbvnvtxjyrTrXa5DnUmktLSWNAekDqG9ytiF4Bnh46HsPOa6rbq0Om3U1Uaw4NDO2Dj64qhkY6UluXS9CQYga9dmz92utiyJt16tmCB9lnHA01HYHXRERuu/APna9uIujvqPkenIZeFE2365oYPUlkTvWSGmqrcpnjsIgXKqI4O0sHLZvbm3mm1caAmzM8dgD21LbwJdppLoAOOo7ys9aXuDcXHN/DtfAHlxmjyIeNGAQDU0NrsIAcHUjBZuBv8XbtdG/sZatlKpmQjfSqwRCT/AyShQvvPuXgCCzOl8dyy5JI7M+lQ3PtYVN92AvCONt8ZKZkhlS//eOl+5wzNeT2gZL98EviqA9nOcPRCU0LIPxwRzDluA0xlbaWW3UlgJtQcsb0g0XUKeKZulp6cycMtN/P8adOY5NhYZwKN1SGrbDD0e3qiFj7eClqpnQjYiXUbw8uRNmT4NJhcb/J3dG9bNdL+0KiDgGOKVbufHKbK44lsfmnTDal4LC8HTxDPA4errkp+eHCANwtx20pxjG31/9zlDXoElYeLJWoXV7s5uh9BVDEIXgInCsmcbmnR3Fg8ZkjeSaaddwxYQr/F5b7737HqVbSpl/13zXGA47gzIHua5bcWim/y8qOnnfY+7g3VKZS1UzoQvoVTOEHmdUjnX6b8Mt584HbT7K33yImhYvPyDQWOxt9YZ+dklL7Zb3aLRZL2fh68ZfS1Y6+Tdl8FH7ybDtjRaN0YkH5xnadH4MO1HG7GDzTnj/Hjg5ahjlL90RUOynorKCB3Y/ECJUw3HdtOtc11mV5a5+byIMieACG8d9j7ls5Q2rnG0IUtVM6AJ6lUD4RPPgnpXdsTP6XdOgWHCNkasomFxPLt4WL/knB4d0gI/sfYRjvjpyPLnMmTLHX2pTOYzy50yZE1AABkw3yrEXcyL/FTzVdf4qbZdOhN/u2kpDevxThTHHjU4cOmoZL7rKXW3khlWbYMG76VTcNgvvoBNkqo4U4WV7y2ISBl/+zJfdPYYUeAcZ13rjxBciP2Px6PVj7eClqpnQjfQqgdDjiHX6bxtZ3umiI7fq7gYLA3vWzTpfHdv2bAOgaHyRY0dbNL6IoalDeWTvI9T56vxpn3PGF/HQ12cFbFuyYx8z/6D9ldg0dC5WQcPsqiygMcQFtTN5kg4NgWfvucYoK6phzOMvUHxHOdneWubfpsO20Yr6tv7v/9d+xleOD3DV7TTx6PU708FLVTOhmxCBEA+xTv9tI0vLVdPqhHMG57Jo6iL/KNbeaZXtLQsxELe0tlD+Url/luBkbM8vzHccFdv3fdaOfUxfuY30RiK7j0aqmqbggYmNPDDB3DZOC1XO4Fzenlfkd9VsuWObv6Z0gYsX0mhfCgfv0jxUnM310075A93Cpa4OFhAbC8rDzxJive/BSAcv9FDEqBwPN6wypvt2wk3/g0aQC1831Cttt8Oaa9e4qjTcDMR2Q6qVwdP+Z18e/NkyoM5au8ffyVpseM4wBtvJboZHfwvDGpxPLQBF3E+WfbYEwP13B7TTrY0/fqYdpTW3f7Y+JOrZCuhzuk4xEet9F4RegswQ4iHW6b/LyNI3KnxMwTDPMEcvmkixCMHsO7DPXyns7qyRbChcxUIXNUdWi+ECCoYQ2FhuCLAVXWTCSSeFzMxsTjadJMeTw32fupmFWXPhkLmBgzCFjhmW3YAN7snqrOsYly1K9PpCH0UEQrzEMv13MCi2ZGXw4i1zQmoq21VAJVNLQqp7WbEITts77aeisiLAyHyw8TCLX7uZxouy+HZFx7A/WPcPgfl9YqqzHAlTBXVamocFly4OUJctDO6wHYSp5SnlhJtKaUzWyMQ4JojaR+iDiMqoO5k910g5MGIkWsGJ/Fz+tHGhYTQNQ3FhMaUzSgOybl58zsWU7S1j1X2ruOPhOyJmQHWzQ9w6I5WWrI4e36kugOXxA+5ZQTvDt99Kp/12eHdzJiWvG0KrdEspK+9bydhnprG1yubb76SmCYOjSimK6nKC0J/pVbmMCs4r0D/Y84OkHDvR7q4bC8pRSvkLpzhFG7sR7HUExowhXGrm+XfNd0wYp4D6jdlk1RmzhNQ1zh5BSsMjv7XVRu6E11BmWibNrc0MUulknGymNgtS242I5RRtBM3ZSU9L55pp1/BI1i0APPXKHVy4fheDq4/BEMMdSp84hqovnYAAABKOSURBVG9ULu9deg6ffuyVADvDryal8KMrsvio/SS5Qa66wfQod2ZBSDB9MpdRT0hdYS+QEy9W/pxYz8lptG/PgFrhkLXTzQ4x+jhk13WojEafcNa/ZzeFqpJixZPloWRqCVv+cC8+U/VkFc5pdxAwLa0t7HppFxuvNZ/jgiLH2ZR1/Y58/nfMWrsH/cFhGvLzGP+9En6+IHK2Um+LN2KxI0HoD/QqgdCX2HdgH3e8dEenUi27eR3V+mpDZg+Wu+XF51zM8289HyBIlIZDgw03U8sge+ezsPjy0BrIJzOJPCuIkDSvxlfDvU/dC+nu2wRzzHfMVWB6W7wB696e1yEwYhGyAdHgQcLBCREYQl9FBEIS2Fq1k22vdSS2C+cj74TbaH+YZ5jr7GH/v/ZTOqOU7c88xAetPqNojdl5H8wxRv/Q4UlUG/xkuHX0phYqVcPlgybyu8Y3E5puO1z94uBOPxGzx0j76FGpUwQhwYhROQmsrrw7JO2CpfKJhpKpJWSkBepurFq/4WYPJa9D1Z1NjDkeaiewG45rY/AkKjgO9b/M44WC5ZSUrkmoMEhk/WLLYD3/rvmUbimNqQypIPQXetUMocclt+skhxqdff+tztx+jsHqDDDSPi9oW+CPKbAMpuPOHOea1C7Hk0Ph2kdIa2x2rVtwaIhhiEVF16mnp6VTvOAaym+/wmhbi9e1eE2sDBwwkCuLr2TcmeNcr0e0uKnRIHLUsiD0J2SGECMxpUh2oSDLOcVBOPVI8PEfybqFo9NeQl/+D9Zcu8bvPTNnyhzS0wKV9FbU787cOvJ+5L7v/PoUbviK+xjhwgkX8omUgSgNBcfg58+kUxIUB1AytSTk+LGQooxHMiM91HodHKsRLeGM8IIgdNCrZgh9xctoQ+EqFr92c4DayK4esQrfOOFm8LSuS/6EfIamDqVsbxk1vhpSVAotrS38vuL3bHMwFts558IZPP3a067rV9eezowfv0pao7WkgdZXt7M/dSiY3jz5hUY7jMysxxjmGcbk0yeHGLRTVSpKKVrbA9NLWCqnOl8d2/dsZ2jqUFe7ipM3ldO24dRowYgNQejP9CqB0FdYOHou5Xl/p/ylctfOzDFZncMypw7K2o9dTeI75Yt4t/f/a7/ruhSVwpzDj1LwncAUEWmNzUxYV4bX5t457sxxrDlzTUB7x48aH9B5N7U0RaxwZnelDcZJDXTvU/dyoPoAS6YvCdg2nBFeEIQORCAkiaLxRVwx4YqE7jN/ewUT1pWR7a1l1fcVzZ7oDbx5njzXkTSYI3cV6pEEkO11/51FcWFxQMc+/675UbXLrU1OaiCAp197mvGjxgccyy31R6IM1oLQVxAbQhLYWrWTdQ+vS6jHS/72CiYv28TAqhqU1ngHxebtYwWvRYPdIwmgIT/2kXa0x3LbLpzwCrYNOKX+CBfVLQj9FZkhdDNbq3ay9O+30tBmJLir8dXwwO4HONp21D9jCPaqiUZvbXkQWbgld3NiUOYgRzVTOA4OgZQ1RmTzdWMnMyG6Q/lxGrUHE24U76YGAmdhETxDCYfYCYT+iswQupnVlXf7hYGFlaIhGuzF4O0eT57qQFdTp+RuyiG6LCMtw19r2GkknZnq4tmjjFiGQ0PgTt/z/llO/vYKFp+7juV5K5n56VLytzvPfpyO9eXPfDnqUfzk0ye7XiOxDQhC55AZQjfjFoNwzHfM/9lpVjD+8X1ccMcuqF4Vkn9faaOmwmBvh1Cw9Ps3XZpClaedPE+ef7RtL6sZbMy2j6QrKiv4+dM/j3hOlvG35HWYvGyTf6YysKqGycsMf3+vQ04hp1H7EpaEbBdMRWUFz7/1vOO6RNgGwnkayexB6MuIQOhmCrJGcLAxtEhOjqdDv/Nx28f+z94WL2ft2MfMlTs66igcOWzUVQCYPZflVTPhuy0htRYWvjOAhV9dz8bSwLiANWeuAQX5aeFdLMv2loW4hbpR46uh8G5DbbV1or1wTTNr/98vOH7lOCAx6SXcDMopKiVu24B0+EJ/RgRCjMQblLahcFWADQFCy0U2tTex4q1iGGTmkFj704COHjC+3393R5GWMFW8VhwK/Olvhr2Id9CJiG0NZ7gNJteTi8dbF1Jg52AOXD+tieX/s4PTvjkvpMON1pXWvr1bu9p1uz+yORbBE3w8SV4n9FdEIHQzC0cbHffyt+/kaH0dQz1DmfOF2Ux2ydMPhJSPdF2e4Cpe4Qy3wdT56hh8E2S2OhfY+cnxvRTvgaumzQtYV91a7U8DbifTyottoym1LWy7cj25ZLal+reLBafjCUJ/QwRCjCQiUnnh6LnUXJiOUooMlUFTe1Pocc7pMMZ+e+RQBlY7jIpPc06BEbKvCOmcLYIjfyefPjls5HIw9ZlQ71YvQUHFm3shJYWrL77av9hJGCilaE4LdZu1VFxucQWXXXAZzWntjsbzcNiPp7VmY0G5zBKEfokIhCTi7wyDBqd2o3J+ej4Vt81i1oodgWqjAQMMlVCUOCXJs+MU+fv8W88zcfREXq8KTFiUqlJp0y6j8Ah98Qt/r+CndefhXWAU8lm/d31ItLaTkLBj2Qic0lZEm87CTvDxItVEEGEh9FWSIhCUUv8JzAWagX8Ci7XWx8L/qv/y9rwiZtWc62gfSBRuCeCOHDvC8lnL/Z2sR2VwqrWJzmpY2pThiVR+8gCbfM9HlYHUCScPpViymgqCEEqy4hB2AxO01p8B3gZuSlI7eg+z58KTe+Cvlcb/BAoDCJ8ArriwmE1LNvFS/jJyjjXRHIe6PbXdyH/04KHdCc9AKllNBSE+kjJD0Fo/Y/v6MjDPbVs7faUegkV1a3WADcFS69jP0dviRen4jxXpurnVURjmGeb/7ewbH6RqWQwHDS6pqWHpPrM9Lqk1anw1Md1juyrMzQAevLwvPUOCkEh6gg3hOmCb20ql1FJgKUBufm53talbyFAZDE8djrc9fAcVXN0sVoJ13k5up3OmzGHbnm2uKbkBBhw9GXVKjOxmmHII/vd0aEsxZgZL98HPnzLWu+1ntC+F5XkrUaeNDFGLRTKO53pyHYVarifycyN2AUHoQoGglHoWOM1h1Wqt9e/NbVYDrcBWt/1orTcDmwHOmHSGTnY9hEQyPHU44Fwb2G5U7o4R7RUTrmBo6lDXKGYrWG7Dc/D1q3A2Hmtj8WltmfxnRRYlLxzjVO5A0n2NpLW0s3UijF1pBKwNbYCMVmi2PYHZLfDjZ9qNGVFQ8J1FuPs/Z8ocfvvcjoAYj4y0DBZNXRSwXfA+ZMYgCAZdJhC01tPDrVdKfRO4DLhEa50ApYgQL8WFxYw7cxyZKZl+YWWnMTebha83sGIm1A4M/f2Y4/D+PdCS1U76LavZ+HsjQvqsHfs4vPW3LJ/a4I9RqB0I6a0w7CQczTaqtf34mXZ/yg0gNPguAkXji5hZcy7L3rnTX5wnGi8jQRAMkuVlNBP4N+CLWuuGZLSht5CM0WtTe1OIKyzAn35yFTNuKGNjeXtANDIYKqINzxmf0xtbjI689EbA8JK63beLhvrAW92SBoPq4b0Hc/FU1znbStyC8lywYjySMZMUhN5OsmwI9wOZwG6lFMDLWuvSJLWlx9Jdem27736OJ4c5U+aQPyG0Q317nhFNvXDtHth5mNWXpnJoUBsFxwOrqAF877zD/OL+79Ou20lRKf7SmMEcGgIv3DqHC9fvCkjOZ3FiVA4PRRlYB9EH4YX7Xf7JwVxde0Gn9iMIvZlkeRmdmYzjxoNTR2Nf5tR5O/2mpxkvg33363x1bNuzLaSWsWX8fntekV8wrAKWfeL7pLQFdvbfmwUPnA+YQsBNGAB8Ms1Dw9euoDJ1aECmVIDWrAwq1y6KerTf2VlBQNBeqxfvwBNsHCiBaUL/oyd4GfV4nIyQVmdvGX3dRqbBEcKxjGA7O9qNBSff/ZbWFsdaxsFusfnp+ai20M5+cxERI5bBMPgumLHY2KeZHtsqAdqQP4w31pQ4ps3uSiJlgI31HgpCb0IEQidw8grqzO8StW08uAWk1fhqmH/XfEfDrL1tDaPzGFgV6OffFibc0ard7LRf74LibhcAsSK2CaEvIwKhnxMuo6lGR0z/8MaakhBVT6o2UlQEk6JS2LRkU2Ia3gk6k+dIEPoTUkKzn1MytYSMNLcUpQbh0j94FxSz/75STo7OQyvFydF5XD5oouO2MybOiLu9ncWyldT4agIEnVX6UxAEmSH0e4Izh2qcQ0LCFcsJVvWUACef3cLu13f7vYxmTJzBkukd5TG7e7QeLs+RzBIEwUAEghCQObR0S6mjCinWwvVLpi8JEAB2kpGVNFzyPkEQDERlJATgpEJKROF6O8nISuom0GIVdILQlxGBIARQXFhM6YxS8jx5KBR5nry4C9cHk4zRencIOkHo7YjKSAjBqfhMtERjG3DzbOrK0Xq4KmuCIBiIQBASRrS2AbeayF09Wo9H0AlCf0BURkLCiNY20B1qKUEQYkdmCELCiMU2IKN1Qeh5yAxBSBjiySMIvRsRCELCEE8eQejdiMpISBjiySMIvRsRCEJCEduAIPReRGUkCIIgACIQBEEQBBMRCIIgCAIgAkEQBEEwEYEgCIIgACIQBEEQBBMRCIIgCAIgAkEQBEEwEYEgCIIgACIQBEEQBBMRCIIgCAIgAkEQBEEwEYEgCIIgACIQBEEQBBMRCIIgCAIgAkEQBEEwEYEgCIIgACIQBEEQBBOltU52G6JGKfUxcDDZ7egkeUBNshvRBfTF8+qL5wR987zknKJjjNZ6eKSNepVA6M0opfZprYuS3Y5E0xfPqy+eE/TN85JzSiyiMhIEQRAAEQiCIAiCiQiE7mNzshvQRfTF8+qL5wR987zknBKI2BAEQRAEQGYIgiAIgokIBEEQBAEQgdCtKKX+Uyn1D6XUa0qpJ5RSOcluU2dRSs1USh1QSr2rlPr3ZLcnESilRiulnldKVSql3lRKrUh2mxKFUipVKfU3pdQfk92WRKGUylFK7TDfqUql1JRktylelFKrzGfvDaXUY0qpAd15fBEI3ctuYILW+jPA28BNSW5Pp1BKpQI/A2YB5wBfU0qdk9xWJYRW4Ada60LgC8D1feS8AFYAlcluRILZCJRrrc8GzqWXn59SahSwHCjSWk8AUoGvdmcbRCB0I1r///buJ8SqMg7j+PeBKXMMoVWhE2h/IVpoUIhCEWP0T5SgZREFgYuCVkW6aV+Ii6AWWhsHBM2ghZWVBLNRKhuQsU0Z6C0jN5aUMFlPi/MOXKLiztzjeZnr84EL97xnLveBezm/ed9zzu/6iO3LZfMYMFEzzxDuA761fdr2HLAf2FY509Bsn7N9ojy/SHOAWV031fAkTQCPA3tqZ2mLpJXA/cBeANtzti/UTdWKMWC5pDFgHPixyzdPQajnOeDD2iEWaTVwtm+7xwgcOPtJWgOsB47XTdKK3cDLwF+1g7ToFuA88G5ZCtsjaUXtUMOw/QPwBnAGOAf8YvtIlxlSEFom6dOy/vfPx7a+v9lJszwxVS/pUPQvYyNz/bKk64H3gJds/1o7zzAkbQF+tv1V7SwtGwPuAd6yvR74DVjS57Ik3UAz014LrAJWSHqqywxjXb7Z1cD25v/bL+kZYAsw6aV7E0gPuLlve4KOp7ZXiqRraIrBlO1DtfO0YBOwVdJjwHXASkn7bHd6oLkCekDP9vwM7iBLvCAAm4HvbZ8HkHQI2Ajs6ypAZggdkvQI8Aqw1fbvtfMM4QvgdklrJV1Lc+Lrg8qZhiZJNGvS39jeVTtPG2y/anvC9hqaz+noCBQDbP8EnJV0ZxmaBE5VjNSGM8AGSePluzhJxyfKM0Po1pvAMuCT5vPmmO3tdSMtnO3Lkl4APqa5EuId27OVY7VhE/A0cFLSTBnbYftwxUzx314Epso/JaeBZyvnGYrt45IOAidolpS/puM2FmldERERQJaMIiKiSEGIiAggBSEiIooUhIiIAFIQIiKiSEGIGICkPyXNlLvOD0gaL+M3Sdov6TtJpyQdlnRH2feRpAuj1GE0RlsKQsRgLtleV7pQzgHby81D7wOf277V9l3ADuDG8prXae5riFgSUhAiFm4auA14EPjD9tvzO2zP2J4uzz8DLtaJGLFwKQgRC1DaEj8KnATuBkataVxcxVIQIgazvLSz+JKm58zeynkiWpdeRhGDuWR7Xf+ApFngyUp5IlqXGULE4h0Flkl6fn5A0r2SHqiYKWLRUhAiFqn8nsUTwEPlstNZ4DXKb0NImgYOAJOSepIerhY2YgDpdhoREUBmCBERUaQgREQEkIIQERFFCkJERAApCBERUaQgREQEkIIQERHF34HBKQOl8l5zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f934a571cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Random Forest Classification (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9349268b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('pink', 'lightgreen')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Random Forest Classification (Test set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of Logistic Regression is:  70.73 %\n",
    "\n",
    "The accuracy of KNN is:  62.92 %\n",
    "\n",
    "The accuracy of SVM is:  70.73 %\n",
    "\n",
    "The accuracy of Naive Bayes is:  71.21 %\n",
    "\n",
    "The accuracy of Decision Tree Classifier is:  53.63 %\n",
    "\n",
    "The accuracy of Random Forest Classification is:  58.53 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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