SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the training & test data\n",
    "train_data = pd.read_csv('risk_analytics_train.csv',index_col=0, header=0)\n",
    "test_data = pd.read_csv('risk_analytics_test.csv',index_col=0, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Gender Married  Dependents     Education Self_Employed  \\\n",
      "Loan_ID                                                            \n",
      "LP001002    Male      No         0.0      Graduate            No   \n",
      "LP001003    Male     Yes         1.0      Graduate            No   \n",
      "LP001005    Male     Yes         0.0      Graduate           Yes   \n",
      "LP001006    Male     Yes         0.0  Not Graduate            No   \n",
      "LP001008    Male      No         0.0      Graduate            No   \n",
      "...          ...     ...         ...           ...           ...   \n",
      "LP002978  Female      No         0.0      Graduate            No   \n",
      "LP002979    Male     Yes         3.0      Graduate            No   \n",
      "LP002983    Male     Yes         1.0      Graduate            No   \n",
      "LP002984    Male     Yes         2.0      Graduate            No   \n",
      "LP002990  Female      No         0.0      Graduate           Yes   \n",
      "\n",
      "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
      "Loan_ID                                                                      \n",
      "LP001002             5849                0.0         NaN             360.0   \n",
      "LP001003             4583             1508.0       128.0             360.0   \n",
      "LP001005             3000                0.0        66.0             360.0   \n",
      "LP001006             2583             2358.0       120.0             360.0   \n",
      "LP001008             6000                0.0       141.0             360.0   \n",
      "...                   ...                ...         ...               ...   \n",
      "LP002978             2900                0.0        71.0             360.0   \n",
      "LP002979             4106                0.0        40.0             180.0   \n",
      "LP002983             8072              240.0       253.0             360.0   \n",
      "LP002984             7583                0.0       187.0             360.0   \n",
      "LP002990             4583                0.0       133.0             360.0   \n",
      "\n",
      "          Credit_History Property_Area Loan_Status  \n",
      "Loan_ID                                             \n",
      "LP001002             1.0         Urban           Y  \n",
      "LP001003             1.0         Rural           N  \n",
      "LP001005             1.0         Urban           Y  \n",
      "LP001006             1.0         Urban           Y  \n",
      "LP001008             1.0         Urban           Y  \n",
      "...                  ...           ...         ...  \n",
      "LP002978             1.0         Rural           Y  \n",
      "LP002979             1.0         Rural           Y  \n",
      "LP002983             1.0         Urban           Y  \n",
      "LP002984             1.0         Urban           Y  \n",
      "LP002990             0.0     Semiurban           N  \n",
      "\n",
      "[614 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "print(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Gender Married  Dependents     Education Self_Employed  \\\n",
      "Loan_ID                                                           \n",
      "LP001015   Male     Yes         0.0      Graduate            No   \n",
      "LP001022   Male     Yes         1.0      Graduate            No   \n",
      "LP001031   Male     Yes         2.0      Graduate            No   \n",
      "LP001035   Male     Yes         2.0      Graduate            No   \n",
      "LP001051   Male      No         0.0  Not Graduate            No   \n",
      "...         ...     ...         ...           ...           ...   \n",
      "LP002971   Male     Yes         3.0  Not Graduate           Yes   \n",
      "LP002975   Male     Yes         0.0      Graduate            No   \n",
      "LP002980   Male      No         0.0      Graduate            No   \n",
      "LP002986   Male     Yes         0.0      Graduate            No   \n",
      "LP002989   Male      No         0.0      Graduate           Yes   \n",
      "\n",
      "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
      "Loan_ID                                                                      \n",
      "LP001015             5720                  0       110.0             360.0   \n",
      "LP001022             3076               1500       126.0             360.0   \n",
      "LP001031             5000               1800       208.0             360.0   \n",
      "LP001035             2340               2546       100.0             360.0   \n",
      "LP001051             3276                  0        78.0             360.0   \n",
      "...                   ...                ...         ...               ...   \n",
      "LP002971             4009               1777       113.0             360.0   \n",
      "LP002975             4158                709       115.0             360.0   \n",
      "LP002980             3250               1993       126.0             360.0   \n",
      "LP002986             5000               2393       158.0             360.0   \n",
      "LP002989             9200                  0        98.0             180.0   \n",
      "\n",
      "          Credit_History Property_Area  \n",
      "Loan_ID                                 \n",
      "LP001015             1.0         Urban  \n",
      "LP001022             1.0         Urban  \n",
      "LP001031             1.0         Urban  \n",
      "LP001035             NaN         Urban  \n",
      "LP001051             1.0         Urban  \n",
      "...                  ...           ...  \n",
      "LP002971             1.0         Urban  \n",
      "LP002975             1.0         Urban  \n",
      "LP002980             NaN     Semiurban  \n",
      "LP002986             1.0         Rural  \n",
      "LP002989             1.0         Rural  \n",
      "\n",
      "[367 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "print(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(614, 12)\n",
      "(367, 11)\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001002</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</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>LP001003</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</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>LP001005</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</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>LP001006</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</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>LP001008</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</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": [
       "         Gender Married  Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001002   Male      No         0.0      Graduate            No   \n",
       "LP001003   Male     Yes         1.0      Graduate            No   \n",
       "LP001005   Male     Yes         0.0      Graduate           Yes   \n",
       "LP001006   Male     Yes         0.0  Not Graduate            No   \n",
       "LP001008   Male      No         0.0      Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0         NaN             360.0   \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area Loan_Status  \n",
       "Loan_ID                                             \n",
       "LP001002             1.0         Urban           Y  \n",
       "LP001003             1.0         Rural           N  \n",
       "LP001005             1.0         Urban           Y  \n",
       "LP001006             1.0         Urban           Y  \n",
       "LP001008             1.0         Urban           Y  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001015</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001022</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001031</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001035</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001051</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Gender Married  Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001015   Male     Yes         0.0      Graduate            No   \n",
       "LP001022   Male     Yes         1.0      Graduate            No   \n",
       "LP001031   Male     Yes         2.0      Graduate            No   \n",
       "LP001035   Male     Yes         2.0      Graduate            No   \n",
       "LP001051   Male      No         0.0  Not Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001015             5720                  0       110.0             360.0   \n",
       "LP001022             3076               1500       126.0             360.0   \n",
       "LP001031             5000               1800       208.0             360.0   \n",
       "LP001035             2340               2546       100.0             360.0   \n",
       "LP001051             3276                  0        78.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area  \n",
       "Loan_ID                                 \n",
       "LP001015             1.0         Urban  \n",
       "LP001022             1.0         Urban  \n",
       "LP001031             1.0         Urban  \n",
       "LP001035             NaN         Urban  \n",
       "LP001051             1.0         Urban  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               11\n",
       "Married               0\n",
       "Dependents           10\n",
       "Education             0\n",
       "Self_Employed        23\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            5\n",
       "Loan_Amount_Term      6\n",
       "Credit_History       29\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Null_Value_Treatment(train_data):\n",
    "    for i in train_data.columns:\n",
    "        if train_data[i].dtypes == \"int64\" or train_data[i].dtypes == \"float64\":\n",
    "            train_data[i].fillna(train_data[i].mean(),inplace=True)\n",
    "        else:\n",
    "            train_data[i].fillna(train_data[i].mode()[0],inplace=True)\n",
    "    return train_data.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Replacing numeric columns by mean to show the average value and replacing the character columns by mode to \\nshow the highest repeated value'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Replacing numeric columns by mean to show the average value and replacing the character columns by mode to \n",
    "show the highest repeated value\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Null_Value_Treatment(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Null_Value_Treatment(test_data):\n",
    "    for i in(test_data.columns):\n",
    "        if test_data[i].dtypes == \"int64\" or test_data[i].dtypes == \"float64\":\n",
    "            test_data[i].fillna(test_data[i].mean(),inplace=True)\n",
    "        else:\n",
    "            test_data[i].fillna(test_data[i].mode()[0],inplace=True)\n",
    "    return test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Null_Value_Treatment(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Married',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'Property_Area',\n",
       " 'Loan_Status']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Highlighting all the categorical columns\n",
    "# Categorical columns have data in dtype object, and before implementing it on ML algorithm it is important that we \n",
    "# convert the object dtype into int (number) by label encoding them.\n",
    "\n",
    "# label encoding basically assigns a number to a string. \n",
    "# Before moving on to Label encoding it is essential that we seperate the categorical columns first as shown below:\n",
    "\n",
    "colname=[]\n",
    "for i in train_data.columns:\n",
    "    if train_data[i].dtypes== \"object\":\n",
    "        colname.append(i)\n",
    "colname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gender', 'Married', 'Education', 'Self_Employed', 'Property_Area']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colname=[]\n",
    "for i in test_data.columns:\n",
    "    if test_data[i].dtypes== \"object\":\n",
    "        colname.append(i)\n",
    "colname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn. preprocessing import LabelEncoder\n",
    "colname = [\"Gender\",\"Married\",\"Education\",\"Self_Employed\",\"Property_Area\",\"Loan_Status\"]\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "for x in colname:\n",
    "    train_data[x]= le.fit_transform(train_data[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001002</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001003</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001005</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001006</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001008</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001002       1        0         0.0          0              0   \n",
       "LP001003       1        1         1.0          0              0   \n",
       "LP001005       1        1         0.0          0              1   \n",
       "LP001006       1        1         0.0          1              0   \n",
       "LP001008       1        0         0.0          0              0   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0  146.412162             360.0   \n",
       "LP001003             4583             1508.0  128.000000             360.0   \n",
       "LP001005             3000                0.0   66.000000             360.0   \n",
       "LP001006             2583             2358.0  120.000000             360.0   \n",
       "LP001008             6000                0.0  141.000000             360.0   \n",
       "\n",
       "          Credit_History  Property_Area  Loan_Status  \n",
       "Loan_ID                                               \n",
       "LP001002             1.0              2            1  \n",
       "LP001003             1.0              0            0  \n",
       "LP001005             1.0              2            1  \n",
       "LP001006             1.0              2            1  \n",
       "LP001008             1.0              2            1  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "colname = [\"Gender\",\"Married\",\"Education\",\"Self_Employed\",\"Property_Area\"]\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "for x in colname:\n",
    "    test_data[x]= le.fit_transform(test_data[x])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Y_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001015       1        1         0.0          0              0   \n",
       "1  LP001022       1        1         1.0          0              0   \n",
       "2  LP001031       1        1         2.0          0              0   \n",
       "3  LP001035       1        1         2.0          0              0   \n",
       "4  LP001051       1        0         0.0          1              0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History  Property_Area Y_predictions  \n",
       "0             1.0              2      Eligible  \n",
       "1             1.0              2      Eligible  \n",
       "2             1.0              2      Eligible  \n",
       "3             NaN              2      Eligible  \n",
       "4             1.0              2      Eligible  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Gender\n",
      "mapping {0: 0, 1: 1}\n",
      "Feature Married\n",
      "mapping {0: 0, 1: 1}\n",
      "Feature Education\n",
      "mapping {0: 0, 1: 1}\n",
      "Feature Self_Employed\n",
      "mapping {0: 0, 1: 1}\n",
      "Feature Property_Area\n",
      "mapping {0: 0, 1: 1, 2: 2}\n"
     ]
    }
   ],
   "source": [
    "# Label encoding the categorical data\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder  #importing LabelEncoder function from sklearn library \n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "for x in colname:\n",
    "    test_data[x]=le.fit_transform(test_data[x])   #using LabelEncoder fn le and performing label encoding using fit transform\n",
    "    \n",
    "    le_name_mapping = dict(zip(le.classes_,le.transform(le.classes_))) #makes a dictionary [key:value] to check the class \n",
    "    # and it's corresponding transformed mapping.\n",
    "    \n",
    "    print(\"Feature\",x)\n",
    "    print(\"mapping\", le_name_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_df = train_data.corr()\n",
    "corr_df\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(20,30))\n",
    "sns.heatmap(corr_df,vmin=-1.0,vmax=1.0,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_data.values[:,0:-1]\n",
    "Y_train = train_data.values[:,-1]\n",
    "Y_train = Y_train.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 11)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614,)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_data.values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 13)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'LP001015'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[109], line 9\u001b[0m\n\u001b[0;32m      5\u001b[0m scaler\u001b[38;5;241m.\u001b[39mfit(X_train)\n\u001b[0;32m      7\u001b[0m X_train \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mtransform(X_train)\n\u001b[1;32m----> 9\u001b[0m X_test \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mtransform(X_test)\n",
      "File \u001b[1;32mc:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_set_output.py:140\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    138\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 140\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    141\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    142\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    143\u001b[0m         return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    144\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    145\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    146\u001b[0m         )\n",
      "File \u001b[1;32mc:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\preprocessing\\_data.py:1004\u001b[0m, in \u001b[0;36mStandardScaler.transform\u001b[1;34m(self, X, copy)\u001b[0m\n\u001b[0;32m   1001\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m   1003\u001b[0m copy \u001b[38;5;241m=\u001b[39m copy \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy\n\u001b[1;32m-> 1004\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(\n\u001b[0;32m   1005\u001b[0m     X,\n\u001b[0;32m   1006\u001b[0m     reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1007\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1008\u001b[0m     copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m   1009\u001b[0m     dtype\u001b[38;5;241m=\u001b[39mFLOAT_DTYPES,\n\u001b[0;32m   1010\u001b[0m     force_all_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow-nan\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1011\u001b[0m )\n\u001b[0;32m   1013\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sparse\u001b[38;5;241m.\u001b[39missparse(X):\n\u001b[0;32m   1014\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwith_mean:\n",
      "File \u001b[1;32mc:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:604\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    602\u001b[0m         out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    603\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 604\u001b[0m     out \u001b[38;5;241m=\u001b[39m check_array(X, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    605\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[0;32m    606\u001b[0m     out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n",
      "File \u001b[1;32mc:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:917\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    915\u001b[0m         array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    916\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 917\u001b[0m         array \u001b[38;5;241m=\u001b[39m _asarray_with_order(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype, xp\u001b[38;5;241m=\u001b[39mxp)\n\u001b[0;32m    918\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m    919\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    920\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    921\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ninad\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_array_api.py:380\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m    378\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39marray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    379\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 380\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39masarray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    382\u001b[0m \u001b[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001b[39;00m\n\u001b[0;32m    383\u001b[0m \u001b[38;5;66;03m# container that is consistent with the input's namespace.\u001b[39;00m\n\u001b[0;32m    384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array)\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'LP001015'"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc_model = SVC(kernel = 'rbf',C=20, gamma =0.01)\n",
    "svc_model.fit(X_train,Y_train)\n",
    "Y_pred = svc_model.predict(X_test)\n",
    "print(list(Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8159609120521173"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>Y_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married  Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes         0.0      Graduate            No   \n",
       "1  LP001022   Male     Yes         1.0      Graduate            No   \n",
       "2  LP001031   Male     Yes         2.0      Graduate            No   \n",
       "3  LP001035   Male     Yes         2.0      Graduate            No   \n",
       "4  LP001051   Male      No         0.0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  Y_predictions  \n",
       "0             1.0         Urban              1  \n",
       "1             1.0         Urban              1  \n",
       "2             1.0         Urban              1  \n",
       "3             NaN         Urban              1  \n",
       "4             1.0         Urban              1  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data= pd.read_csv(r'risk_analytics_test.csv')\n",
    "test_data[\"Y_predictions\"]=Y_pred\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[\"Y_predictions\"] = test_data[\"Y_predictions\"].map({1:\"Eligible\",0:\"Not Eligible\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Y_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married  Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes         0.0      Graduate            No   \n",
       "1  LP001022   Male     Yes         1.0      Graduate            No   \n",
       "2  LP001031   Male     Yes         2.0      Graduate            No   \n",
       "3  LP001035   Male     Yes         2.0      Graduate            No   \n",
       "4  LP001051   Male      No         0.0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Y_predictions  \n",
       "0             1.0         Urban      Eligible  \n",
       "1             1.0         Urban      Eligible  \n",
       "2             1.0         Urban      Eligible  \n",
       "3             NaN         Urban      Eligible  \n",
       "4             1.0         Urban      Eligible  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.to_csv(r'test_data_output.csv',index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y_predictions\n",
       "Eligible        308\n",
       "Not Eligible     59\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.Y_predictions.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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