Loan Approval Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fda3257f",
   "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,
   "id": "ca78152d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"loan_data_1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3398933d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>0</td>\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>4583.0</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>1</th>\n",
       "      <td>1</td>\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>3000.0</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>2</th>\n",
       "      <td>2</td>\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>2583.0</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>3</th>\n",
       "      <td>3</td>\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>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</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": [
       "   Unnamed: 0   Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0           0  LP001003   Male     Yes          1      Graduate            No   \n",
       "1           1  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "2           2  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "3           3  LP001008   Male      No          0      Graduate            No   \n",
       "4           4  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0           4583.0             1508.0       128.0             360.0   \n",
       "1           3000.0                0.0        66.0             360.0   \n",
       "2           2583.0             2358.0       120.0             360.0   \n",
       "3           6000.0                0.0       141.0             360.0   \n",
       "4           2333.0             1516.0        95.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Rural           N  \n",
       "1             1.0         Urban           Y  \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": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32983f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "604d4396",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 381 entries, 0 to 380\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         381 non-null    int64  \n",
      " 1   Loan_ID            381 non-null    object \n",
      " 2   Gender             376 non-null    object \n",
      " 3   Married            381 non-null    object \n",
      " 4   Dependents         373 non-null    object \n",
      " 5   Education          375 non-null    object \n",
      " 6   Self_Employed      360 non-null    object \n",
      " 7   ApplicantIncome    369 non-null    float64\n",
      " 8   CoapplicantIncome  363 non-null    float64\n",
      " 9   LoanAmount         373 non-null    float64\n",
      " 10  Loan_Amount_Term   370 non-null    float64\n",
      " 11  Credit_History     351 non-null    float64\n",
      " 12  Property_Area      381 non-null    object \n",
      " 13  Loan_Status        381 non-null    object \n",
      "dtypes: float64(5), int64(1), object(8)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de7511a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>381.000000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>351.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>3563.422764</td>\n",
       "      <td>1267.005289</td>\n",
       "      <td>104.914209</td>\n",
       "      <td>340.864865</td>\n",
       "      <td>0.837607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>110.129469</td>\n",
       "      <td>1427.371257</td>\n",
       "      <td>2388.048316</td>\n",
       "      <td>28.484822</td>\n",
       "      <td>68.549257</td>\n",
       "      <td>0.369338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>2583.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>3326.000000</td>\n",
       "      <td>830.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>285.000000</td>\n",
       "      <td>4226.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>380.000000</td>\n",
       "      <td>9703.000000</td>\n",
       "      <td>33837.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "count  381.000000       369.000000         363.000000  373.000000   \n",
       "mean   190.000000      3563.422764        1267.005289  104.914209   \n",
       "std    110.129469      1427.371257        2388.048316   28.484822   \n",
       "min      0.000000       150.000000           0.000000    9.000000   \n",
       "25%     95.000000      2583.000000           0.000000   90.000000   \n",
       "50%    190.000000      3326.000000         830.000000  110.000000   \n",
       "75%    285.000000      4226.000000        2008.000000  127.000000   \n",
       "max    380.000000      9703.000000       33837.000000  150.000000   \n",
       "\n",
       "       Loan_Amount_Term  Credit_History  \n",
       "count        370.000000      351.000000  \n",
       "mean         340.864865        0.837607  \n",
       "std           68.549257        0.369338  \n",
       "min           12.000000        0.000000  \n",
       "25%          360.000000        1.000000  \n",
       "50%          360.000000        1.000000  \n",
       "75%          360.000000        1.000000  \n",
       "max          480.000000        1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9d80af0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Loan_Status</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>52</td>\n",
       "      <td>242</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>106</td>\n",
       "      <td>245</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Loan_Status       N    Y  All\n",
       "Credit_History               \n",
       "0.0              54    3   57\n",
       "1.0              52  242  294\n",
       "All             106  245  351"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(dataset['Credit_History'], dataset['Loan_Status'], margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22b70fe6",
   "metadata": {},
   "outputs": [
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       "<Axes: >"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.boxplot(column = 'ApplicantIncome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec5ba905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['ApplicantIncome'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dc4582e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['CoapplicantIncome'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3d0b4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'ApplicantIncome'}, xlabel='Education'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.boxplot(column = 'ApplicantIncome', by = 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d389493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.boxplot(column = 'LoanAmount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "028cb707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['LoanAmount'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6910d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['LoanAmount_log']=np.log(dataset['LoanAmount'])\n",
    "dataset['LoanAmount_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "605229d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0            0\n",
       "Loan_ID               0\n",
       "Gender                5\n",
       "Married               0\n",
       "Dependents            8\n",
       "Education             6\n",
       "Self_Employed        21\n",
       "ApplicantIncome      12\n",
       "CoapplicantIncome    18\n",
       "LoanAmount            8\n",
       "Loan_Amount_Term     11\n",
       "Credit_History       30\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "LoanAmount_log        8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "336898fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Gender'].fillna(dataset['Gender'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "be8d5002",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Dependents'].fillna(dataset['Dependents'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aaae7b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Education'].fillna(dataset['Education'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbb7aa47",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Self_Employed'].fillna(dataset['Self_Employed'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "738a4be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['ApplicantIncome'].fillna(dataset['ApplicantIncome'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7fb212ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['CoapplicantIncome'].fillna(dataset['CoapplicantIncome'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c392e893",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['LoanAmount'].fillna(dataset['LoanAmount'].mean(), inplace = True)\n",
    "dataset['LoanAmount_log'].fillna(dataset['LoanAmount_log'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3415782e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Loan_Amount_Term'].fillna(dataset['Loan_Amount_Term'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0ce80a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Credit_History'].fillna(dataset['Credit_History'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13ad4d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0           0\n",
       "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",
       "LoanAmount_log       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "91dceda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['TotalIncome'] = dataset['ApplicantIncome'] + dataset['CoapplicantIncome']\n",
    "dataset['TotalIncome_log'] = np.log(dataset['TotalIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4160969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['TotalIncome_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "69228b91",
   "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>Unnamed: 0</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",
       "      <th>LoanAmount_log</th>\n",
       "      <th>TotalIncome</th>\n",
       "      <th>TotalIncome_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\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>4583.0</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",
       "      <td>4.852030</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>8.714568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>3000.0</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",
       "      <td>4.189655</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>2583.0</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",
       "      <td>4.787492</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>8.505323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>6000.0</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",
       "      <td>4.948760</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>3849.0</td>\n",
       "      <td>8.255569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0           0  LP001003   Male     Yes          1      Graduate            No   \n",
       "1           1  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "2           2  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "3           3  LP001008   Male      No          0      Graduate            No   \n",
       "4           4  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0           4583.0             1508.0       128.0             360.0   \n",
       "1           3000.0                0.0        66.0             360.0   \n",
       "2           2583.0             2358.0       120.0             360.0   \n",
       "3           6000.0                0.0       141.0             360.0   \n",
       "4           2333.0             1516.0        95.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  LoanAmount_log  TotalIncome  \\\n",
       "0             1.0         Rural           N        4.852030       6091.0   \n",
       "1             1.0         Urban           Y        4.189655       3000.0   \n",
       "2             1.0         Urban           Y        4.787492       4941.0   \n",
       "3             1.0         Urban           Y        4.948760       6000.0   \n",
       "4             1.0         Urban           Y        4.553877       3849.0   \n",
       "\n",
       "   TotalIncome_log  \n",
       "0         8.714568  \n",
       "1         8.006368  \n",
       "2         8.505323  \n",
       "3         8.699515  \n",
       "4         8.255569  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "802bb1fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 381 entries, 0 to 380\n",
      "Data columns (total 17 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         381 non-null    int64  \n",
      " 1   Loan_ID            381 non-null    object \n",
      " 2   Gender             381 non-null    object \n",
      " 3   Married            381 non-null    object \n",
      " 4   Dependents         381 non-null    object \n",
      " 5   Education          381 non-null    object \n",
      " 6   Self_Employed      381 non-null    object \n",
      " 7   ApplicantIncome    381 non-null    float64\n",
      " 8   CoapplicantIncome  381 non-null    float64\n",
      " 9   LoanAmount         381 non-null    float64\n",
      " 10  Loan_Amount_Term   381 non-null    float64\n",
      " 11  Credit_History     381 non-null    float64\n",
      " 12  Property_Area      381 non-null    object \n",
      " 13  Loan_Status        381 non-null    object \n",
      " 14  LoanAmount_log     381 non-null    float64\n",
      " 15  TotalIncome        381 non-null    float64\n",
      " 16  TotalIncome_log    381 non-null    float64\n",
      "dtypes: float64(8), int64(1), object(8)\n",
      "memory usage: 50.7+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "09fa5a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, np.r_[2:13, 14:17]].values\n",
    "y = dataset.iloc[:, 13].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b566e309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Male', 'Yes', '1', ..., 4.852030263919617, 6091.0,\n",
       "        8.714567550836485],\n",
       "       ['Male', 'Yes', '0', ..., 4.189654742026425, 3000.0,\n",
       "        8.006367567650246],\n",
       "       ['Male', 'Yes', '0', ..., 4.787491742782046, 4941.0,\n",
       "        8.50532301884575],\n",
       "       ...,\n",
       "       ['Female', 'No', '0', ..., 4.2626798770413155, 2900.0,\n",
       "        7.9724660159745655],\n",
       "       ['Male', 'Yes', '3+', ..., 3.6888794541139363, 4106.0,\n",
       "        8.32020459757888],\n",
       "       ['Female', 'No', '0', ..., 4.890349128221754, 4583.0,\n",
       "        8.430109084509125]], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bdf7f24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N',\n",
       "       'Y', 'N', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'N', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N',\n",
       "       'Y', 'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N',\n",
       "       'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N',\n",
       "       'Y', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'N',\n",
       "       'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'N', 'Y', 'N', 'Y', 'Y',\n",
       "       'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e54ef3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "56556364",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ed6fd9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    X_train[:,i] = labelencoder_X.fit_transform(X_train[:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c158fcd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[:,10] = labelencoder_X.fit_transform(X_train[:,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "65df9c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_y = LabelEncoder()\n",
    "y_train = labelencoder_y.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "91969427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5e898363",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    X_test[:,i] = labelencoder_X.fit_transform(X_test[:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f58541b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[:,10] = labelencoder_X.fit_transform(X_test[:,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93f6c4a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_y = LabelEncoder()\n",
    "y_test = labelencoder_y.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ea2237a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, ..., 4.718498871295094, 3812.0, 8.245909264774093],\n",
       "       [1, 0, 2, ..., 4.382026634673881, 3547.0, 8.173857454773621],\n",
       "       [1, 0, 0, ..., 4.543294782270004, 3563.4227642276423,\n",
       "        8.178476812574203],\n",
       "       ...,\n",
       "       [1, 1, 0, ..., 4.499809670330265, 4949.0, 8.506940814951886],\n",
       "       [1, 1, 3, ..., 4.867534450455582, 5499.0, 8.612321536507814],\n",
       "       [1, 1, 2, ..., 2.833213344056216, 2385.0, 7.776954403322442]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6317f7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b98de539",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b9d10e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "DTClassifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "DTClassifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "95f5c165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = DTClassifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ee3d06f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Decision Tree is : 0.7922077922077922\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"The accuracy of Decision Tree is :\", metrics.accuracy_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "19e11aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "NBClassifier = GaussianNB()\n",
    "NBClassifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "199f63e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = NBClassifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "935430a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Naive Bayes is : 0.8051948051948052\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of Naive Bayes is :\", metrics.accuracy_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5c454bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata = pd.read_csv(\"loan_test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0d9d5025",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4887.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001032    Male      No          0      Graduate            No   \n",
       "1  LP001034    Male      No          1  Not Graduate            No   \n",
       "2  LP001036  Female      No          0      Graduate            No   \n",
       "3  LP001038    Male     Yes          0  Not Graduate            No   \n",
       "4  LP001041    Male     Yes          0      Graduate           NaN   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0           4950.0                0.0         125             360.0   \n",
       "1           3596.0                0.0         100             240.0   \n",
       "2           3510.0                0.0          76             360.0   \n",
       "3           4887.0                0.0         133             360.0   \n",
       "4           2600.0             3500.0         115               NaN   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             NaN         Urban  \n",
       "2             0.0         Urban  \n",
       "3             1.0         Rural  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5bd4e5c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28 entries, 0 to 27\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            28 non-null     object \n",
      " 1   Gender             27 non-null     object \n",
      " 2   Married            28 non-null     object \n",
      " 3   Dependents         27 non-null     object \n",
      " 4   Education          27 non-null     object \n",
      " 5   Self_Employed      27 non-null     object \n",
      " 6   ApplicantIncome    27 non-null     float64\n",
      " 7   CoapplicantIncome  26 non-null     float64\n",
      " 8   LoanAmount         28 non-null     int64  \n",
      " 9   Loan_Amount_Term   26 non-null     float64\n",
      " 10  Credit_History     26 non-null     float64\n",
      " 11  Property_Area      28 non-null     object \n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 2.8+ KB\n"
     ]
    }
   ],
   "source": [
    "testdata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "71cc4086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               1\n",
       "Married              0\n",
       "Dependents           1\n",
       "Education            1\n",
       "Self_Employed        1\n",
       "ApplicantIncome      1\n",
       "CoapplicantIncome    2\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     2\n",
       "Credit_History       2\n",
       "Property_Area        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d53d8417",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata['Gender'].fillna(testdata['Gender'].mode()[0],inplace = True)\n",
    "testdata['Dependents'].fillna(testdata['Dependents'].mode()[0],inplace = True)\n",
    "testdata['Education'].fillna(testdata['Education'].mode()[0], inplace = True)\n",
    "testdata['Self_Employed'].fillna(testdata['Self_Employed'].mode()[0], inplace = True)\n",
    "testdata['ApplicantIncome'].fillna(testdata['ApplicantIncome'].mean(), inplace = True)\n",
    "testdata['CoapplicantIncome'].fillna(testdata['CoapplicantIncome'].mean(), inplace = True)\n",
    "testdata['Loan_Amount_Term'].fillna(testdata['Loan_Amount_Term'].mode()[0], inplace = True)\n",
    "testdata['Credit_History'].fillna(testdata['Credit_History'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bb108f2c",
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3e771690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testdata.boxplot(column = 'LoanAmount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d963debf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testdata.boxplot(column = 'ApplicantIncome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0721f5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata.LoanAmount = testdata.LoanAmount.fillna(testdata.LoanAmount.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b89ebe27",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata['LoanAmount_log'] = np.log(testdata['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "78380cc6",
   "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",
       "LoanAmount_log       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "826b5ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata['TotalIncome'] = testdata['ApplicantIncome'] + testdata['CoapplicantIncome']\n",
    "testdata['TotalIncome_log'] = np.log(testdata['TotalIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e3eee575",
   "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>LoanAmount_log</th>\n",
       "      <th>TotalIncome</th>\n",
       "      <th>TotalIncome_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.828314</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>8.507143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>3596.0</td>\n",
       "      <td>8.187577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.330733</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>8.163371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4887.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4.890349</td>\n",
       "      <td>4887.0</td>\n",
       "      <td>8.494334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.744932</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>8.716044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001032    Male      No          0      Graduate            No   \n",
       "1  LP001034    Male      No          1  Not Graduate            No   \n",
       "2  LP001036  Female      No          0      Graduate            No   \n",
       "3  LP001038    Male     Yes          0  Not Graduate            No   \n",
       "4  LP001041    Male     Yes          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0           4950.0                0.0         125             360.0   \n",
       "1           3596.0                0.0         100             240.0   \n",
       "2           3510.0                0.0          76             360.0   \n",
       "3           4887.0                0.0         133             360.0   \n",
       "4           2600.0             3500.0         115             360.0   \n",
       "\n",
       "   Credit_History Property_Area  LoanAmount_log  TotalIncome  TotalIncome_log  \n",
       "0             1.0         Urban        4.828314       4950.0         8.507143  \n",
       "1             1.0         Urban        4.605170       3596.0         8.187577  \n",
       "2             0.0         Urban        4.330733       3510.0         8.163371  \n",
       "3             1.0         Rural        4.890349       4887.0         8.494334  \n",
       "4             1.0         Urban        4.744932       6100.0         8.716044  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d393485e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28 entries, 0 to 27\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            28 non-null     object \n",
      " 1   Gender             28 non-null     object \n",
      " 2   Married            28 non-null     object \n",
      " 3   Dependents         28 non-null     object \n",
      " 4   Education          28 non-null     object \n",
      " 5   Self_Employed      28 non-null     object \n",
      " 6   ApplicantIncome    28 non-null     float64\n",
      " 7   CoapplicantIncome  28 non-null     float64\n",
      " 8   LoanAmount         28 non-null     int64  \n",
      " 9   Loan_Amount_Term   28 non-null     float64\n",
      " 10  Credit_History     28 non-null     float64\n",
      " 11  Property_Area      28 non-null     object \n",
      " 12  LoanAmount_log     28 non-null     float64\n",
      " 13  TotalIncome        28 non-null     float64\n",
      " 14  TotalIncome_log    28 non-null     float64\n",
      "dtypes: float64(7), int64(1), object(7)\n",
      "memory usage: 3.4+ KB\n"
     ]
    }
   ],
   "source": [
    "testdata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b2f7be72",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = testdata.iloc[:, np.r_[1:15]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0b1375a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    test[:,i] = labelencoder_X.fit_transform(test[:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2ede3331",
   "metadata": {},
   "outputs": [],
   "source": [
    "test[:,10] = labelencoder_X.fit_transform(test[:,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "931b4dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        4.8283137373023015, 4950.0, 8.507142855562735],\n",
       "       [1, 0, 1, 1, 0, 3596.0, 0.0, 100, 240.0, 1.0, 2,\n",
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       "        4.499809670330265, 35673.0, 10.482149379105689],\n",
       "       [0, 1, 0, 0, 0, 3166.0, 0.0, 36, 360.0, 1.0, 1, 3.58351893845611,\n",
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       "        1880.0, 7.539027055823995],\n",
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       "        4.983606621708336, 4784.7692307692305, 8.47319307508963],\n",
       "       [1, 1, 0, 0, 0, 2297.0, 1522.0, 104, 360.0, 1.0, 2,\n",
       "        4.6443908991413725, 3819.0, 8.247743887225516],\n",
       "       [0, 0, 0, 1, 0, 2165.0, 0.0, 70, 360.0, 1.0, 1, 4.248495242049359,\n",
       "        2165.0, 7.68017564043659],\n",
       "       [1, 0, 0, 0, 0, 3501.8518518518517, 0.0, 94, 360.0, 1.0, 1,\n",
       "        4.543294782270004, 3501.8518518518517, 8.161047208082275],\n",
       "       [1, 1, 2, 0, 1, 2726.0, 0.0, 106, 360.0, 0.0, 1,\n",
       "        4.663439094112067, 2726.0, 7.9105906122564775],\n",
       "       [1, 1, 0, 0, 0, 3000.0, 3416.0, 56, 180.0, 1.0, 1,\n",
       "        4.02535169073515, 6416.0, 8.766550149546351],\n",
       "       [1, 1, 0, 0, 0, 3859.0, 3300.0, 142, 180.0, 1.0, 0,\n",
       "        4.955827057601261, 7159.0, 8.876125585396185],\n",
       "       [1, 0, 0, 1, 0, 3833.0, 0.0, 110, 360.0, 1.0, 0,\n",
       "        4.700480365792417, 3833.0, 8.251403065380556],\n",
       "       [1, 0, 0, 0, 0, 2987.0, 0.0, 88, 360.0, 0.0, 1, 4.477336814478207,\n",
       "        2987.0, 8.00202481821611],\n",
       "       [1, 1, 3, 0, 0, 5703.0, 0.0, 128, 360.0, 1.0, 2,\n",
       "        4.852030263919617, 5703.0, 8.648747631156539],\n",
       "       [1, 1, 0, 0, 0, 3232.0, 1997.7692307692307, 108, 360.0, 1.0, 0,\n",
       "        4.68213122712422, 5229.7692307692305, 8.562122431949568],\n",
       "       [0, 0, 0, 0, 0, 2900.0, 0.0, 71, 360.0, 1.0, 0,\n",
       "        4.2626798770413155, 2900.0, 7.9724660159745655],\n",
       "       [1, 1, 3, 0, 0, 4106.0, 0.0, 40, 180.0, 1.0, 0,\n",
       "        3.6888794541139363, 4106.0, 8.32020459757888],\n",
       "       [0, 0, 0, 0, 1, 4583.0, 0.0, 133, 360.0, 0.0, 1,\n",
       "        4.890349128221754, 4583.0, 8.430109084509125]], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5ab1912f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = ss.fit_transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e0620b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = NBClassifier.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "435c903e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  }
 ],
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