Data Science Assignment(reunion).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import all necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(\"loan.csv\")\n",
    "df2=pd.read_csv(\"applicant.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_application_id</th>\n",
       "      <th>applicant_id</th>\n",
       "      <th>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Has_guarantor</th>\n",
       "      <th>Other_EMI_plans</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d68d975e-edad-11ea-8761-1d6f9c1ff461</td>\n",
       "      <td>1469590</td>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1169000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d68d989e-edad-11ea-b1d5-2bcf65006448</td>\n",
       "      <td>1203873</td>\n",
       "      <td>48</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>5951000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d68d995c-edad-11ea-814a-1b6716782575</td>\n",
       "      <td>1432761</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>2096000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d68d99fc-edad-11ea-8841-17e8848060ae</td>\n",
       "      <td>1207582</td>\n",
       "      <td>42</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>7882000</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d68d9a92-edad-11ea-9f3d-1f8682db006a</td>\n",
       "      <td>1674436</td>\n",
       "      <td>24</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>4870000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    loan_application_id  applicant_id  Months_loan_taken_for  \\\n",
       "0  d68d975e-edad-11ea-8761-1d6f9c1ff461       1469590                      6   \n",
       "1  d68d989e-edad-11ea-b1d5-2bcf65006448       1203873                     48   \n",
       "2  d68d995c-edad-11ea-814a-1b6716782575       1432761                     12   \n",
       "3  d68d99fc-edad-11ea-8841-17e8848060ae       1207582                     42   \n",
       "4  d68d9a92-edad-11ea-9f3d-1f8682db006a       1674436                     24   \n",
       "\n",
       "                Purpose  Principal_loan_amount  \\\n",
       "0  electronic equipment                1169000   \n",
       "1  electronic equipment                5951000   \n",
       "2             education                2096000   \n",
       "3                  FF&E                7882000   \n",
       "4           new vehicle                4870000   \n",
       "\n",
       "   EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                                            4   \n",
       "1                                            2   \n",
       "2                                            2   \n",
       "3                                            2   \n",
       "4                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                                NaN                0   \n",
       "\n",
       "   Has_guarantor Other_EMI_plans  Number_of_existing_loans_at_this_bank  \\\n",
       "0              0             NaN                                      2   \n",
       "1              0             NaN                                      1   \n",
       "2              0             NaN                                      1   \n",
       "3              1             NaN                                      1   \n",
       "4              0             NaN                                      2   \n",
       "\n",
       "                             Loan_history  high_risk_applicant  \n",
       "0   critical/pending loans at other banks                    0  \n",
       "1  existing loans paid back duly till now                    1  \n",
       "2   critical/pending loans at other banks                    0  \n",
       "3  existing loans paid back duly till now                    0  \n",
       "4   delay in paying off loans in the past                    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>applicant_id</th>\n",
       "      <th>Primary_applicant_age_in_years</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Number_of_dependents</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Has_been_employed_for_at_most</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "      <th>Balance_in_existing_bank_account_(lower_limit_of_bucket)</th>\n",
       "      <th>Balance_in_existing_bank_account_(upper_limit_of_bucket)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1469590</td>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Registered under the applicant's name</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1203873</td>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1432761</td>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1207582</td>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1674436</td>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   applicant_id  Primary_applicant_age_in_years  Gender  \\\n",
       "0       1469590                              67    male   \n",
       "1       1203873                              22  female   \n",
       "2       1432761                              49    male   \n",
       "3       1207582                              45    male   \n",
       "4       1674436                              53    male   \n",
       "\n",
       "               Marital_status  Number_of_dependents   Housing  \\\n",
       "0                      single                     1       own   \n",
       "1  divorced/separated/married                     1       own   \n",
       "2                      single                     2       own   \n",
       "3                      single                     2  for free   \n",
       "4                      single                     2  for free   \n",
       "\n",
       "   Years_at_current_residence            Employment_status  \\\n",
       "0                           4  skilled employee / official   \n",
       "1                           2  skilled employee / official   \n",
       "2                           3         unskilled - resident   \n",
       "3                           4  skilled employee / official   \n",
       "4                           4  skilled employee / official   \n",
       "\n",
       "  Has_been_employed_for_at_least Has_been_employed_for_at_most  \\\n",
       "0                        7 years                           NaN   \n",
       "1                         1 year                       4 years   \n",
       "2                        4 years                       7 years   \n",
       "3                        4 years                       7 years   \n",
       "4                         1 year                       4 years   \n",
       "\n",
       "                               Telephone  Foreign_worker  \\\n",
       "0  Registered under the applicant's name               1   \n",
       "1                                    NaN               1   \n",
       "2                                    NaN               1   \n",
       "3                                    NaN               1   \n",
       "4                                    NaN               1   \n",
       "\n",
       "  Savings_account_balance  \\\n",
       "0                     NaN   \n",
       "1                     Low   \n",
       "2                     Low   \n",
       "3                     Low   \n",
       "4                     Low   \n",
       "\n",
       "  Balance_in_existing_bank_account_(lower_limit_of_bucket)  \\\n",
       "0                                                NaN         \n",
       "1                                                  0         \n",
       "2                                                NaN         \n",
       "3                                                NaN         \n",
       "4                                                NaN         \n",
       "\n",
       "  Balance_in_existing_bank_account_(upper_limit_of_bucket)  \n",
       "0                                                  0        \n",
       "1                                              2 lac        \n",
       "2                                                NaN        \n",
       "3                                                  0        \n",
       "4                                                  0        "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.merge(df1, df2) #merge the two dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_application_id</th>\n",
       "      <th>applicant_id</th>\n",
       "      <th>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
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       "      <th>Has_guarantor</th>\n",
       "      <th>Other_EMI_plans</th>\n",
       "      <th>...</th>\n",
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       "      <th>Telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "      <th>Balance_in_existing_bank_account_(lower_limit_of_bucket)</th>\n",
       "      <th>Balance_in_existing_bank_account_(upper_limit_of_bucket)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d68d975e-edad-11ea-8761-1d6f9c1ff461</td>\n",
       "      <td>1469590</td>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
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       "      <td>real estate</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>skilled employee / official</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Registered under the applicant's name</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d68d989e-edad-11ea-b1d5-2bcf65006448</td>\n",
       "      <td>1203873</td>\n",
       "      <td>48</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>5951000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d68d995c-edad-11ea-814a-1b6716782575</td>\n",
       "      <td>1432761</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>2096000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d68d99fc-edad-11ea-8841-17e8848060ae</td>\n",
       "      <td>1207582</td>\n",
       "      <td>42</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>7882000</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d68d9a92-edad-11ea-9f3d-1f8682db006a</td>\n",
       "      <td>1674436</td>\n",
       "      <td>24</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>4870000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    loan_application_id  applicant_id  Months_loan_taken_for  \\\n",
       "0  d68d975e-edad-11ea-8761-1d6f9c1ff461       1469590                      6   \n",
       "1  d68d989e-edad-11ea-b1d5-2bcf65006448       1203873                     48   \n",
       "2  d68d995c-edad-11ea-814a-1b6716782575       1432761                     12   \n",
       "3  d68d99fc-edad-11ea-8841-17e8848060ae       1207582                     42   \n",
       "4  d68d9a92-edad-11ea-9f3d-1f8682db006a       1674436                     24   \n",
       "\n",
       "                Purpose  Principal_loan_amount  \\\n",
       "0  electronic equipment                1169000   \n",
       "1  electronic equipment                5951000   \n",
       "2             education                2096000   \n",
       "3                  FF&E                7882000   \n",
       "4           new vehicle                4870000   \n",
       "\n",
       "   EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                                            4   \n",
       "1                                            2   \n",
       "2                                            2   \n",
       "3                                            2   \n",
       "4                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                                NaN                0   \n",
       "\n",
       "   Has_guarantor Other_EMI_plans  ...   Housing Years_at_current_residence  \\\n",
       "0              0             NaN  ...       own                          4   \n",
       "1              0             NaN  ...       own                          2   \n",
       "2              0             NaN  ...       own                          3   \n",
       "3              1             NaN  ...  for free                          4   \n",
       "4              0             NaN  ...  for free                          4   \n",
       "\n",
       "             Employment_status  Has_been_employed_for_at_least  \\\n",
       "0  skilled employee / official                         7 years   \n",
       "1  skilled employee / official                          1 year   \n",
       "2         unskilled - resident                         4 years   \n",
       "3  skilled employee / official                         4 years   \n",
       "4  skilled employee / official                          1 year   \n",
       "\n",
       "  Has_been_employed_for_at_most                              Telephone  \\\n",
       "0                           NaN  Registered under the applicant's name   \n",
       "1                       4 years                                    NaN   \n",
       "2                       7 years                                    NaN   \n",
       "3                       7 years                                    NaN   \n",
       "4                       4 years                                    NaN   \n",
       "\n",
       "   Foreign_worker Savings_account_balance  \\\n",
       "0               1                     NaN   \n",
       "1               1                     Low   \n",
       "2               1                     Low   \n",
       "3               1                     Low   \n",
       "4               1                     Low   \n",
       "\n",
       "   Balance_in_existing_bank_account_(lower_limit_of_bucket)  \\\n",
       "0                                                NaN          \n",
       "1                                                  0          \n",
       "2                                                NaN          \n",
       "3                                                NaN          \n",
       "4                                                NaN          \n",
       "\n",
       "  Balance_in_existing_bank_account_(upper_limit_of_bucket)  \n",
       "0                                                  0        \n",
       "1                                              2 lac        \n",
       "2                                                NaN        \n",
       "3                                                  0        \n",
       "4                                                  0        \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 27)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 0 to 999\n",
      "Data columns (total 27 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype \n",
      "---  ------                                                    --------------  ----- \n",
      " 0   loan_application_id                                       1000 non-null   object\n",
      " 1   applicant_id                                              1000 non-null   int64 \n",
      " 2   Months_loan_taken_for                                     1000 non-null   int64 \n",
      " 3   Purpose                                                   988 non-null    object\n",
      " 4   Principal_loan_amount                                     1000 non-null   int64 \n",
      " 5   EMI_rate_in_percentage_of_disposable_income               1000 non-null   int64 \n",
      " 6   Property                                                  846 non-null    object\n",
      " 7   Has_coapplicant                                           1000 non-null   int64 \n",
      " 8   Has_guarantor                                             1000 non-null   int64 \n",
      " 9   Other_EMI_plans                                           186 non-null    object\n",
      " 10  Number_of_existing_loans_at_this_bank                     1000 non-null   int64 \n",
      " 11  Loan_history                                              1000 non-null   object\n",
      " 12  high_risk_applicant                                       1000 non-null   int64 \n",
      " 13  Primary_applicant_age_in_years                            1000 non-null   int64 \n",
      " 14  Gender                                                    1000 non-null   object\n",
      " 15  Marital_status                                            1000 non-null   object\n",
      " 16  Number_of_dependents                                      1000 non-null   int64 \n",
      " 17  Housing                                                   1000 non-null   object\n",
      " 18  Years_at_current_residence                                1000 non-null   int64 \n",
      " 19  Employment_status                                         1000 non-null   object\n",
      " 20  Has_been_employed_for_at_least                            938 non-null    object\n",
      " 21  Has_been_employed_for_at_most                             747 non-null    object\n",
      " 22  Telephone                                                 404 non-null    object\n",
      " 23  Foreign_worker                                            1000 non-null   int64 \n",
      " 24  Savings_account_balance                                   817 non-null    object\n",
      " 25  Balance_in_existing_bank_account_(lower_limit_of_bucket)  332 non-null    object\n",
      " 26  Balance_in_existing_bank_account_(upper_limit_of_bucket)  543 non-null    object\n",
      "dtypes: int64(12), object(15)\n",
      "memory usage: 218.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() #information of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#some of colomns are having some nullvalues like Purpose,Property,Other_EMI_plans and more,,, \"Other_EMI_plans\" colomn has more than 50% null value so i will remove that collomn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>applicant_id</th>\n",
       "      <th>Months_loan_taken_for</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Has_guarantor</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "      <th>Primary_applicant_age_in_years</th>\n",
       "      <th>Number_of_dependents</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Foreign_worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.514763e+06</td>\n",
       "      <td>20.903000</td>\n",
       "      <td>3.271258e+06</td>\n",
       "      <td>2.973000</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.052000</td>\n",
       "      <td>1.407000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>35.546000</td>\n",
       "      <td>1.155000</td>\n",
       "      <td>2.845000</td>\n",
       "      <td>0.963000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.286764e+05</td>\n",
       "      <td>12.058814</td>\n",
       "      <td>2.822737e+06</td>\n",
       "      <td>1.118715</td>\n",
       "      <td>0.198389</td>\n",
       "      <td>0.222138</td>\n",
       "      <td>0.577654</td>\n",
       "      <td>0.458487</td>\n",
       "      <td>11.375469</td>\n",
       "      <td>0.362086</td>\n",
       "      <td>1.103718</td>\n",
       "      <td>0.188856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.105364e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.321398e+06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.365500e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.529114e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.319500e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.707752e+06</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.972250e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.903505e+06</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>1.842400e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       applicant_id  Months_loan_taken_for  Principal_loan_amount  \\\n",
       "count  1.000000e+03            1000.000000           1.000000e+03   \n",
       "mean   1.514763e+06              20.903000           3.271258e+06   \n",
       "std    2.286764e+05              12.058814           2.822737e+06   \n",
       "min    1.105364e+06               4.000000           2.500000e+05   \n",
       "25%    1.321398e+06              12.000000           1.365500e+06   \n",
       "50%    1.529114e+06              18.000000           2.319500e+06   \n",
       "75%    1.707752e+06              24.000000           3.972250e+06   \n",
       "max    1.903505e+06              72.000000           1.842400e+07   \n",
       "\n",
       "       EMI_rate_in_percentage_of_disposable_income  Has_coapplicant  \\\n",
       "count                                  1000.000000      1000.000000   \n",
       "mean                                      2.973000         0.041000   \n",
       "std                                       1.118715         0.198389   \n",
       "min                                       1.000000         0.000000   \n",
       "25%                                       2.000000         0.000000   \n",
       "50%                                       3.000000         0.000000   \n",
       "75%                                       4.000000         0.000000   \n",
       "max                                       4.000000         1.000000   \n",
       "\n",
       "       Has_guarantor  Number_of_existing_loans_at_this_bank  \\\n",
       "count    1000.000000                            1000.000000   \n",
       "mean        0.052000                               1.407000   \n",
       "std         0.222138                               0.577654   \n",
       "min         0.000000                               1.000000   \n",
       "25%         0.000000                               1.000000   \n",
       "50%         0.000000                               1.000000   \n",
       "75%         0.000000                               2.000000   \n",
       "max         1.000000                               4.000000   \n",
       "\n",
       "       high_risk_applicant  Primary_applicant_age_in_years  \\\n",
       "count          1000.000000                     1000.000000   \n",
       "mean              0.300000                       35.546000   \n",
       "std               0.458487                       11.375469   \n",
       "min               0.000000                       19.000000   \n",
       "25%               0.000000                       27.000000   \n",
       "50%               0.000000                       33.000000   \n",
       "75%               1.000000                       42.000000   \n",
       "max               1.000000                       75.000000   \n",
       "\n",
       "       Number_of_dependents  Years_at_current_residence  Foreign_worker  \n",
       "count           1000.000000                 1000.000000     1000.000000  \n",
       "mean               1.155000                    2.845000        0.963000  \n",
       "std                0.362086                    1.103718        0.188856  \n",
       "min                1.000000                    1.000000        0.000000  \n",
       "25%                1.000000                    2.000000        1.000000  \n",
       "50%                1.000000                    3.000000        1.000000  \n",
       "75%                1.000000                    4.000000        1.000000  \n",
       "max                2.000000                    4.000000        1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is information of dataset's mean, std , min, perceltile etc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this dataset have no duplicate value this is the good thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_application_id                                           0\n",
       "applicant_id                                                  0\n",
       "Months_loan_taken_for                                         0\n",
       "Purpose                                                      12\n",
       "Principal_loan_amount                                         0\n",
       "EMI_rate_in_percentage_of_disposable_income                   0\n",
       "Property                                                    154\n",
       "Has_coapplicant                                               0\n",
       "Has_guarantor                                                 0\n",
       "Other_EMI_plans                                             814\n",
       "Number_of_existing_loans_at_this_bank                         0\n",
       "Loan_history                                                  0\n",
       "high_risk_applicant                                           0\n",
       "Primary_applicant_age_in_years                                0\n",
       "Gender                                                        0\n",
       "Marital_status                                                0\n",
       "Number_of_dependents                                          0\n",
       "Housing                                                       0\n",
       "Years_at_current_residence                                    0\n",
       "Employment_status                                             0\n",
       "Has_been_employed_for_at_least                               62\n",
       "Has_been_employed_for_at_most                               253\n",
       "Telephone                                                   596\n",
       "Foreign_worker                                                0\n",
       "Savings_account_balance                                     183\n",
       "Balance_in_existing_bank_account_(lower_limit_of_bucket)    668\n",
       "Balance_in_existing_bank_account_(upper_limit_of_bucket)    457\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#here we can see there are some null value so i will replace the null value with their mean(if the columns are numerical) and mode(if the columns are cetagorical).and remove some columns which have 50% or more than 50% null value  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19c2c320>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Purpose'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Purpose columns is categorical so i will replace the null value with there mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Purpose'].fillna(df['Purpose'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19c2c390>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Property'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Property columns is also categorical so i will replace the null value with there mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Property'].fillna(df['Property'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19d60160>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Has_been_employed_for_at_least'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Has_been_employed_for_at_least' columns is also categorical so i will replace the null value with there mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Has_been_employed_for_at_least'].fillna(df['Has_been_employed_for_at_least'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19dca320>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Has_been_employed_for_at_most'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Has_been_employed_for_at_most'].fillna(df['Has_been_employed_for_at_most'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19cdd860>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Property'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Property'].fillna(df['Property'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19e78080>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Savings_account_balance'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Savings_account_balance'].fillna(df['Savings_account_balance'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#in above all the columns are categorical.thats why i replaced the null value with there mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['loan_application_id'],inplace=True)\n",
    "df.drop(columns=['applicant_id'],inplace=True)\n",
    "#this two columns has not a good relation with our terget variable so i decided to drop this two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Has_guarantor</th>\n",
       "      <th>Other_EMI_plans</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>...</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Has_been_employed_for_at_most</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "      <th>Balance_in_existing_bank_account_(lower_limit_of_bucket)</th>\n",
       "      <th>Balance_in_existing_bank_account_(upper_limit_of_bucket)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1169000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>...</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Registered under the applicant's name</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>5951000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>...</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>2096000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>...</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>7882000</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>...</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>4870000</td>\n",
       "      <td>3</td>\n",
       "      <td>car or other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>...</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Months_loan_taken_for               Purpose  Principal_loan_amount  \\\n",
       "0                      6  electronic equipment                1169000   \n",
       "1                     48  electronic equipment                5951000   \n",
       "2                     12             education                2096000   \n",
       "3                     42                  FF&E                7882000   \n",
       "4                     24           new vehicle                4870000   \n",
       "\n",
       "   EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                                            4   \n",
       "1                                            2   \n",
       "2                                            2   \n",
       "3                                            2   \n",
       "4                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                       car or other                0   \n",
       "\n",
       "   Has_guarantor Other_EMI_plans  Number_of_existing_loans_at_this_bank  \\\n",
       "0              0             NaN                                      2   \n",
       "1              0             NaN                                      1   \n",
       "2              0             NaN                                      1   \n",
       "3              1             NaN                                      1   \n",
       "4              0             NaN                                      2   \n",
       "\n",
       "                             Loan_history  ...   Housing  \\\n",
       "0   critical/pending loans at other banks  ...       own   \n",
       "1  existing loans paid back duly till now  ...       own   \n",
       "2   critical/pending loans at other banks  ...       own   \n",
       "3  existing loans paid back duly till now  ...  for free   \n",
       "4   delay in paying off loans in the past  ...  for free   \n",
       "\n",
       "   Years_at_current_residence            Employment_status  \\\n",
       "0                           4  skilled employee / official   \n",
       "1                           2  skilled employee / official   \n",
       "2                           3         unskilled - resident   \n",
       "3                           4  skilled employee / official   \n",
       "4                           4  skilled employee / official   \n",
       "\n",
       "  Has_been_employed_for_at_least  Has_been_employed_for_at_most  \\\n",
       "0                        7 years                        4 years   \n",
       "1                         1 year                        4 years   \n",
       "2                        4 years                        7 years   \n",
       "3                        4 years                        7 years   \n",
       "4                         1 year                        4 years   \n",
       "\n",
       "                               Telephone  Foreign_worker  \\\n",
       "0  Registered under the applicant's name               1   \n",
       "1                                    NaN               1   \n",
       "2                                    NaN               1   \n",
       "3                                    NaN               1   \n",
       "4                                    NaN               1   \n",
       "\n",
       "  Savings_account_balance  \\\n",
       "0                     Low   \n",
       "1                     Low   \n",
       "2                     Low   \n",
       "3                     Low   \n",
       "4                     Low   \n",
       "\n",
       "  Balance_in_existing_bank_account_(lower_limit_of_bucket)  \\\n",
       "0                                                NaN         \n",
       "1                                                  0         \n",
       "2                                                NaN         \n",
       "3                                                NaN         \n",
       "4                                                NaN         \n",
       "\n",
       "  Balance_in_existing_bank_account_(upper_limit_of_bucket)  \n",
       "0                                                  0        \n",
       "1                                              2 lac        \n",
       "2                                                NaN        \n",
       "3                                                  0        \n",
       "4                                                  0        \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #now this is our data (25 columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Months_loan_taken_for                                         0\n",
       "Purpose                                                       0\n",
       "Principal_loan_amount                                         0\n",
       "EMI_rate_in_percentage_of_disposable_income                   0\n",
       "Property                                                      0\n",
       "Has_coapplicant                                               0\n",
       "Has_guarantor                                                 0\n",
       "Other_EMI_plans                                             814\n",
       "Number_of_existing_loans_at_this_bank                         0\n",
       "Loan_history                                                  0\n",
       "high_risk_applicant                                           0\n",
       "Primary_applicant_age_in_years                                0\n",
       "Gender                                                        0\n",
       "Marital_status                                                0\n",
       "Number_of_dependents                                          0\n",
       "Housing                                                       0\n",
       "Years_at_current_residence                                    0\n",
       "Employment_status                                             0\n",
       "Has_been_employed_for_at_least                                0\n",
       "Has_been_employed_for_at_most                                 0\n",
       "Telephone                                                   596\n",
       "Foreign_worker                                                0\n",
       "Savings_account_balance                                       0\n",
       "Balance_in_existing_bank_account_(lower_limit_of_bucket)    668\n",
       "Balance_in_existing_bank_account_(upper_limit_of_bucket)    457\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() #so here 4 columns has 50% or more than 50% null value so i will drop those columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Other_EMI_plans'],inplace=True)\n",
    "df.drop(columns=['Telephone'],inplace=True)\n",
    "df.drop(columns=['Balance_in_existing_bank_account_(lower_limit_of_bucket)'],inplace=True)\n",
    "df.drop(columns=['Balance_in_existing_bank_account_(upper_limit_of_bucket)'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Has_guarantor</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "      <th>...</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Number_of_dependents</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Has_been_employed_for_at_most</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1169000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>5951000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>2096000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>7882000</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>4870000</td>\n",
       "      <td>3</td>\n",
       "      <td>car or other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Months_loan_taken_for               Purpose  Principal_loan_amount  \\\n",
       "0                      6  electronic equipment                1169000   \n",
       "1                     48  electronic equipment                5951000   \n",
       "2                     12             education                2096000   \n",
       "3                     42                  FF&E                7882000   \n",
       "4                     24           new vehicle                4870000   \n",
       "\n",
       "   EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                                            4   \n",
       "1                                            2   \n",
       "2                                            2   \n",
       "3                                            2   \n",
       "4                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                       car or other                0   \n",
       "\n",
       "   Has_guarantor  Number_of_existing_loans_at_this_bank  \\\n",
       "0              0                                      2   \n",
       "1              0                                      1   \n",
       "2              0                                      1   \n",
       "3              1                                      1   \n",
       "4              0                                      2   \n",
       "\n",
       "                             Loan_history  high_risk_applicant  ...  Gender  \\\n",
       "0   critical/pending loans at other banks                    0  ...    male   \n",
       "1  existing loans paid back duly till now                    1  ...  female   \n",
       "2   critical/pending loans at other banks                    0  ...    male   \n",
       "3  existing loans paid back duly till now                    0  ...    male   \n",
       "4   delay in paying off loans in the past                    1  ...    male   \n",
       "\n",
       "               Marital_status Number_of_dependents   Housing  \\\n",
       "0                      single                    1       own   \n",
       "1  divorced/separated/married                    1       own   \n",
       "2                      single                    2       own   \n",
       "3                      single                    2  for free   \n",
       "4                      single                    2  for free   \n",
       "\n",
       "  Years_at_current_residence            Employment_status  \\\n",
       "0                          4  skilled employee / official   \n",
       "1                          2  skilled employee / official   \n",
       "2                          3         unskilled - resident   \n",
       "3                          4  skilled employee / official   \n",
       "4                          4  skilled employee / official   \n",
       "\n",
       "  Has_been_employed_for_at_least Has_been_employed_for_at_most Foreign_worker  \\\n",
       "0                        7 years                       4 years              1   \n",
       "1                         1 year                       4 years              1   \n",
       "2                        4 years                       7 years              1   \n",
       "3                        4 years                       7 years              1   \n",
       "4                         1 year                       4 years              1   \n",
       "\n",
       "   Savings_account_balance  \n",
       "0                      Low  \n",
       "1                      Low  \n",
       "2                      Low  \n",
       "3                      Low  \n",
       "4                      Low  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()#this dataset have no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Months_loan_taken_for                          0\n",
       "Purpose                                        0\n",
       "Principal_loan_amount                          0\n",
       "EMI_rate_in_percentage_of_disposable_income    0\n",
       "Property                                       0\n",
       "Has_coapplicant                                0\n",
       "Has_guarantor                                  0\n",
       "Number_of_existing_loans_at_this_bank          0\n",
       "Loan_history                                   0\n",
       "high_risk_applicant                            0\n",
       "Primary_applicant_age_in_years                 0\n",
       "Gender                                         0\n",
       "Marital_status                                 0\n",
       "Number_of_dependents                           0\n",
       "Housing                                        0\n",
       "Years_at_current_residence                     0\n",
       "Employment_status                              0\n",
       "Has_been_employed_for_at_least                 0\n",
       "Has_been_employed_for_at_most                  0\n",
       "Foreign_worker                                 0\n",
       "Savings_account_balance                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ed0860>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#here we can the corelation of all the variable \n",
    "#\"Months_loan_taken_for\" and \"Principal_loan_amount\" have good corelation so  we can remove one columns out of two or we can take both of columns ..lets see later i will think about it  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Months_loan_taken_for                          0.214927\n",
       "Principal_loan_amount                          0.154739\n",
       "EMI_rate_in_percentage_of_disposable_income    0.072404\n",
       "Has_coapplicant                                0.062728\n",
       "Has_guarantor                                 -0.055039\n",
       "Number_of_existing_loans_at_this_bank         -0.045732\n",
       "high_risk_applicant                            1.000000\n",
       "Primary_applicant_age_in_years                -0.091127\n",
       "Number_of_dependents                          -0.003015\n",
       "Years_at_current_residence                     0.002967\n",
       "Foreign_worker                                 0.082079\n",
       "Name: high_risk_applicant, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"high_risk_applicant\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all variables corelation with the target variable(\"\"high_risk_applicant\"\")\n",
    "#here we can see \"Has_guarantor\",\"Number_of_existing_loans_at_this_bank\",\"Primary_applicant_age_in_years\", \"Number_of_dependents\" has negative corelation with our target variable. so i decided drop this cols bcz this are not important cols."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Has_guarantor'],inplace=True)\n",
    "df.drop(columns=['Number_of_existing_loans_at_this_bank'],inplace=True)\n",
    "df.drop(columns=['Primary_applicant_age_in_years'],inplace=True)\n",
    "df.drop(columns=['Number_of_dependents'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Has_been_employed_for_at_most</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1169000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>5951000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>2096000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>7882000</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>4870000</td>\n",
       "      <td>3</td>\n",
       "      <td>car or other</td>\n",
       "      <td>0</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Months_loan_taken_for               Purpose  Principal_loan_amount  \\\n",
       "0                      6  electronic equipment                1169000   \n",
       "1                     48  electronic equipment                5951000   \n",
       "2                     12             education                2096000   \n",
       "3                     42                  FF&E                7882000   \n",
       "4                     24           new vehicle                4870000   \n",
       "\n",
       "   EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                                            4   \n",
       "1                                            2   \n",
       "2                                            2   \n",
       "3                                            2   \n",
       "4                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                       car or other                0   \n",
       "\n",
       "                             Loan_history  high_risk_applicant  Gender  \\\n",
       "0   critical/pending loans at other banks                    0    male   \n",
       "1  existing loans paid back duly till now                    1  female   \n",
       "2   critical/pending loans at other banks                    0    male   \n",
       "3  existing loans paid back duly till now                    0    male   \n",
       "4   delay in paying off loans in the past                    1    male   \n",
       "\n",
       "               Marital_status   Housing  Years_at_current_residence  \\\n",
       "0                      single       own                           4   \n",
       "1  divorced/separated/married       own                           2   \n",
       "2                      single       own                           3   \n",
       "3                      single  for free                           4   \n",
       "4                      single  for free                           4   \n",
       "\n",
       "             Employment_status Has_been_employed_for_at_least  \\\n",
       "0  skilled employee / official                        7 years   \n",
       "1  skilled employee / official                         1 year   \n",
       "2         unskilled - resident                        4 years   \n",
       "3  skilled employee / official                        4 years   \n",
       "4  skilled employee / official                         1 year   \n",
       "\n",
       "  Has_been_employed_for_at_most  Foreign_worker Savings_account_balance  \n",
       "0                       4 years               1                     Low  \n",
       "1                       4 years               1                     Low  \n",
       "2                       7 years               1                     Low  \n",
       "3                       7 years               1                     Low  \n",
       "4                       4 years               1                     Low  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19fb3470>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Months_loan_taken_for'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a145828>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFFRJREFUeJzt3X+MHGd9x/HPhySkbo46AYet66S9oLqRiA8MXgUqBNojFEKCEmgLOKIhJoGDilAquaKBSoSCUNMWA6VQkCFWktLmEhF+uI4ppIEjRSLAXTA5h0BxUgN2LJv8wOGIlerSb/+4OW673N3O7szu3jx+v6STd2aenef7zKw/Hs/NzDoiBABI15MGXQAAoLcIegBIHEEPAIkj6AEgcQQ9ACSOoAeAxBH0AJA4gh4AEkfQA0DiThx0AZK0Zs2aGB4e7lt/v/jFL3TKKaf0rb9eS208UnpjSm08EmNaCaamph6MiNPbtVsRQT88PKzJycm+9TcxMaFGo9G3/nottfFI6Y0ptfFIjGklsP2jPO04dQMAiSPoASBxBD0AJI6gB4DEEfQAkDiCHgASR9ADQOIIegBIHEEPAIlbEXfGAvhVw1fd2vV7919zYYmVoOo4ogeAxBH0AJA4gh4AEkfQA0DiCHoASBxX3aBvOrmKZOvIrLY0tecqEqB7bY/obe+wfcT23qZ5N9nek/3st70nmz9s+1jTsk/0sngAQHt5juivk/RRSTfMz4iI186/tr1N0tGm9vdFxMayCgQAFNM26CPiDtvDiy2zbUmvkfTicssCAJSl6C9jXyjpcET8sGneWba/Y/trtl9YcP0AgIIcEe0bzR3R74qIDS3zPy5pX0Rsy6ZPljQUEQ/Z3iTp85LOiYhHF1nnmKQxSarVapvGx8cLDiW/mZkZDQ0N9a2/XqvKeKYPHm3fKFNbJR0+tjA9sm51Dyrqn272USfbq1U/tldVPnedqNqYRkdHpyKi3q5d10Fv+0RJByVtiogDS7xvQtJfRMTkcuuv1+sxOblsk1JV7Zve26nKeDq96mbb9MKZxapfddPNPlrpz7qpyueuE1Ubk+1cQV/k1M1LJH2/OeRtn277hOz1MyStl3R/gT4AAAXlubzyRknfkHS27QO2r8gWbZZ0Y0vzF0m62/Z3JX1G0lsi4uEyCwYAdCbPVTeXLDF/yyLzbpF0S/GyAABl4REIAJA4gh4AEkfQA0DiCHoASBxBDwCJI+gBIHEEPQAkjqAHgMQR9ACQOIIeABJH0ANA4gh6AEgcQQ8AiSPoASBxBD0AJI6gB4DEEfQAkDiCHgASR9ADQOLyfDn4DttHbO9tmvce2wdt78l+Lmha9k7b+2z/wPbLelU4ACCfPEf010k6f5H5H4qIjdnPbkmy/UxJmyWdk73nn2yfUFaxAIDOtQ36iLhD0sM513expPGIeDwi/lvSPknnFqgPAFBQkXP0V9q+Ozu1c1o2b52knzS1OZDNAwAMiCOifSN7WNKuiNiQTdckPSgpJL1P0tqIuNz2xyR9IyI+nbW7VtLuiLhlkXWOSRqTpFqttml8fLyUAeUxMzOjoaGhvvXXa1UZz/TBo7nb1lZJh48tTI+sW92Divqnm33UyfZq1Y/tVZXPXSeqNqbR0dGpiKi3a3diNyuPiMPzr21/UtKubPKApDObmp4h6YEl1rFd0nZJqtfr0Wg0uimlKxMTE+pnf71WlfFsuerW3G23jsxq2/TCx3P/6xo9qKh/utlHnWyvVv3YXlX53HUixTFJXZ66sb22afJVkuavyNkpabPtk22fJWm9pG8VKxEAUETbI3rbN0pqSFpj+4CkqyU1bG/U3Kmb/ZLeLEkRcY/tmyV9T9KspLdGxBO9KR0AkEfboI+ISxaZfe0y7d8v6f1FigIAlIc7YwEgcQQ9ACSOoAeAxBH0AJA4gh4AEkfQA0DiCHoASBxBDwCJI+gBIHEEPQAkjqAHgMQR9ACQOIIeABJH0ANA4gh6AEgcQQ8AiSPoASBxBD0AJI6gB4DEtQ162ztsH7G9t2ne39v+vu27bX/O9qnZ/GHbx2zvyX4+0cviAQDt5Tmiv07S+S3zbpO0ISKeJem/JL2zadl9EbEx+3lLOWUCALrVNugj4g5JD7fM+3JEzGaTd0o6owe1AQBKUMY5+sslfbFp+izb37H9NdsvLGH9AIACHBHtG9nDknZFxIaW+X8lqS7pDyMibJ8saSgiHrK9SdLnJZ0TEY8uss4xSWOSVKvVNo2PjxcdS24zMzMaGhrqW3+9VpXxTB88mrttbZV0+NjC9Mi61T2oqH+62UedbK9W/dheVfncdaJqYxodHZ2KiHq7did224HtyyS9QtJ5kf1rERGPS3o8ez1l+z5JvydpsvX9EbFd0nZJqtfr0Wg0ui2lYxMTE+pnf71WlfFsuerW3G23jsxq2/TCx3P/6xo9qKh/utlHnWyvVv3YXlX53HUixTFJXZ66sX2+pL+UdFFEPNY0/3TbJ2SvnyFpvaT7yygUANCdtkf0tm+U1JC0xvYBSVdr7iqbkyXdZluS7syusHmRpPfanpX0hKS3RMTDi64YANAXbYM+Ii5ZZPa1S7S9RdItRYsCAJSHO2MBIHEEPQAkjqAHgMQR9ACQOIIeABJH0ANA4gh6AEgcQQ8Aiev6WTfA8WK4yDNnrrmwxEqA7nBEDwCJI+gBIHEEPQAkjqAHgMQR9ACQOIIeABJH0ANA4gh6AEgcQQ8AiSPoASBxuYLe9g7bR2zvbZr3VNu32f5h9udp2Xzb/ojtfbbvtv3cXhUPAGgv7xH9dZLOb5l3laTbI2K9pNuzaUl6uaT12c+YpI8XLxMA0K1cQR8Rd0h6uGX2xZKuz15fL+mVTfNviDl3SjrV9toyigUAdK7IOfpaRBySpOzPp2fz10n6SVO7A9k8AMAAOCLyNbSHJe2KiA3Z9M8i4tSm5Y9ExGm2b5X0NxHx9Wz+7ZLeERFTLesb09ypHdVqtU3j4+MlDCefmZkZDQ0N9a2/XqvKeKYPHs3dtrZKOnxsYXpk3eoeVJRPJ3W3mq+7m31URr+9VJXPXSeqNqbR0dGpiKi3a1fkefSHba+NiEPZqZkj2fwDks5saneGpAda3xwR2yVtl6R6vR6NRqNAKZ2ZmJhQP/vrtaqMZ0sHz3XfOjKrbdMLH8/9r2v0oKJ8Oqm71Xzd3eyjMvrtpap87jqR4pikYqdudkq6LHt9maQvNM1/fXb1zfMlHZ0/xQMA6L9cR/S2b5TUkLTG9gFJV0u6RtLNtq+Q9GNJr86a75Z0gaR9kh6T9IaSawYAdCBX0EfEJUssOm+RtiHprUWKAgCUhztjASBxBD0AJI6gB4DEEfQAkDiCHgASR9ADQOIIegBIXJFHIADArxgu8uiGay4ssRLM44geABJH0ANA4gh6AEgcQQ8AiSPoASBxBD0AJI6gB4DEEfQAkDiCHgASR9ADQOIIegBIXNfPurF9tqSbmmY9Q9K7JZ0q6U2SfprNf1dE7O66QgBAIV0HfUT8QNJGSbJ9gqSDkj4n6Q2SPhQRHyilQgBAIWWdujlP0n0R8aOS1gcAKElZQb9Z0o1N01favtv2DtunldQHAKALjohiK7CfLOkBSedExGHbNUkPSgpJ75O0NiIuX+R9Y5LGJKlWq20aHx8vVEcnZmZmNDQ0VHg90wePdv3ekXWrC/c/r6zx9Fon26u2Sjp8bGG6zO3VqTL2czf7aKV8vpay1JhWet3LqcrfpXmjo6NTEVFv166MoL9Y0lsj4qWLLBuWtCsiNiy3jnq9HpOTk4Xq6MTExIQajUbh9ayUL1goazy91sn22joyq23TC79CGuQXUpSxn7vZRyvl87WUpca00uteTlX+Ls2znSvoyzh1c4maTtvYXtu07FWS9pbQBwCgS4W+StD2r0v6A0lvbpr9d7Y3au7Uzf6WZQCAPisU9BHxmKSntcy7tFBFAIBScWcsACSOoAeAxBH0AJA4gh4AEkfQA0DiCl11A1RFkZt4gKrjiB4AEkfQA0DiCHoASBxBDwCJI+gBIHEEPQAkjqAHgMQR9ACQOIIeABJH0ANA4ngEApCgoo98GPR3t6JcHNEDQOIIegBIXOFTN7b3S/q5pCckzUZE3fZTJd0kaVhzXxD+moh4pGhfAIDOlXVEPxoRGyOink1fJen2iFgv6fZsGgAwAL06dXOxpOuz19dLemWP+gEAtFFG0IekL9uesj2WzatFxCFJyv58egn9AAC64IgotgL7tyLiAdtPl3SbpLdJ2hkRpza1eSQiTmt535ikMUmq1WqbxsfHC9XRiZmZGQ0NDRVez/TBo12/d2Td6sL9zytrPL3WyfaqrZIOH1uYLrq9iuyrIubr7mYfDapmKd/2XmpMK+XvRTeq8ndp3ujo6FTTKfMlFQ76/7cy+z2SZiS9SVIjIg7ZXitpIiLOXup99Xo9JicnS6ujnYmJCTUajcLrKXKtcpnXKXc6nkHV3Um/W0dmtW164VqBottrUF8lOF93N5+5QX79YZ7tvdSYqvD5WqrfsrKhX2znCvpCp25sn2L7KfOvJb1U0l5JOyVdljW7TNIXivQDAOhe0csra5I+Z3t+Xf8aEf9u+9uSbrZ9haQfS3p1wX4AAF0qFPQRcb+kZy8y/yFJ5xVZNwCgHNwZCwCJI+gBIHEEPQAkjqAHgMQR9ACQOIIeABLHN0wBPTR/t+bWkVltGeCdrji+cUQPAIkj6AEgcZy6QUcG+aAtAN3hiB4AEkfQA0DiCHoASBxBDwCJI+gBIHEEPQAkjqAHgMQR9ACQOIIeABLXddDbPtP2V23fa/se22/P5r/H9kHbe7KfC8orFwDQqSKPQJiVtDUi7rL9FElTtm/Lln0oIj5QvDwAQFFdB31EHJJ0KHv9c9v3SlpXVmEAgHKUco7e9rCk50j6ZjbrStt3295h+7Qy+gAAdMcRUWwF9pCkr0l6f0R81nZN0oOSQtL7JK2NiMsXed+YpDFJqtVqm8bHxwvV0YmZmRkNDQ0VXs/0waNdv3dk3erC/c/rdDxF6u6X2irp8LGF6aLba9Bjbh3PSpdney/1uRvU34sy+i0rG/pldHR0KiLq7doVCnrbJ0naJelLEfHBRZYPS9oVERuWW0+9Xo/Jycmu6+jUxMSEGo1G4fUUeWTv/msuLNz/vE7HU4VHDW8dmdW26YUzi0W316DH3DqelS7P9l7qczeovxdl9FtWNvSL7VxBX+SqG0u6VtK9zSFve21Ts1dJ2tttHwCA4oocYrxA0qWSpm3vyea9S9Iltjdq7tTNfklvLlQhAKCQIlfdfF2SF1m0u/tyAABl485YAEgcQQ8AiSPoASBxBD0AJI6gB4DEVecODgDJG/SNbaki6AEc9+b/gdk6MqstXfxjU+ad7r3AqRsASBxBDwCJI+gBIHEEPQAkjqAHgMQR9ACQOIIeABLHdfSoBG6kAbpH0FdQa+h1e5MHgONDEkHf6dFeczCu9DvaAKCoJIK+ijgVAaBf+GUsACSuZ0f0ts+X9A+STpD0qYi4pld9AShXnv9x8ruh6ujJEb3tEyR9TNLLJT1T0iW2n9mLvgAAy+vVqZtzJe2LiPsj4n8kjUu6uEd9AQCW0atTN+sk/aRp+oCk5/Wor0L4pSiAoorkSD+u/HNElL9S+9WSXhYRb8ymL5V0bkS8ranNmKSxbPJsST8ovZClrZH0YB/767XUxiOlN6bUxiMxppXgdyLi9HaNenVEf0DSmU3TZ0h6oLlBRGyXtL1H/S/L9mRE1AfRdy+kNh4pvTGlNh6JMVVJr87Rf1vSettn2X6ypM2SdvaoLwDAMnpyRB8Rs7avlPQlzV1euSMi7ulFXwCA5fXsOvqI2C1pd6/WX9BAThn1UGrjkdIbU2rjkRhTZfTkl7EAgJWDRyAAQOKSDXrbO2wfsb13ieUN20dt78l+3t3vGjth+0zbX7V9r+17bL99kTa2/RHb+2zfbfu5g6g1r5xjqsx+sv1rtr9l+7vZeP56kTYn274p20fftD3c/0rzyzmmLbZ/2rSP3jiIWjth+wTb37G9a5FlldpHeaT89MrrJH1U0g3LtPnPiHhFf8opbFbS1oi4y/ZTJE3Zvi0ivtfU5uWS1mc/z5P0ca3QG9UyecYkVWc/PS7pxRExY/skSV+3/cWIuLOpzRWSHomI37W9WdLfSnrtIIrNKc+YJOmmiLhyAPV16+2S7pX0G4ssq9o+aivZI/qIuEPSw4OuoywRcSgi7spe/1xzH9J1Lc0ulnRDzLlT0qm21/a51Nxyjqkysu0+k02elP20/hLsYknXZ68/I+k82+5TiR3LOaZKsX2GpAslfWqJJpXaR3kkG/Q5/X72X9Iv2j5n0MXklf1X8jmSvtmyaLFHT1QiOJcZk1Sh/ZSdEtgj6Yik2yJiyX0UEbOSjkp6Wn+r7EyOMUnSH2WnCz9j+8xFlq8kH5b0Dkn/u8Tyyu2jdo7noL9Lc7cPP1vSP0r6/IDrycX2kKRbJP15RDzauniRt6z4o682Y6rUfoqIJyJio+buBj/X9oaWJpXbRznG9G+ShiPiWZL+QwtHwyuO7VdIOhIRU8s1W2Teit5H7Ry3QR8Rj87/lzS75v8k22sGXNaysnOkt0j6l4j47CJN2j56YqVpN6Yq7idJioifSZqQdH7Lol/uI9snSlqtipxiXGpMEfFQRDyeTX5S0qY+l9aJF0i6yPZ+zT1V98W2P93SprL7aCnHbdDb/s358262z9XctnhosFUtLav1Wkn3RsQHl2i2U9Lrs6tvni/paEQc6luRHcozpirtJ9un2z41e71K0kskfb+l2U5Jl2Wv/1jSV2IF38ySZ0wtvwe6SHO/a1mRIuKdEXFGRAxr7tEsX4mIP2lpVql9lEeyV93YvlFSQ9Ia2wckXa25XyQpIj6huR34p7ZnJR2TtHmF78wXSLpU0nR2vlSS3iXpt6Vfjmm3pAsk7ZP0mKQ3DKDOTuQZU5X201pJ13vui3eeJOnmiNhl+72SJiNip+b+Yftn2/s0d5S4eXDl5pJnTH9m+yLNXUX1sKQtA6u2SxXfR21xZywAJO64PXUDAMcLgh4AEkfQA0DiCHoASBxBDwCJI+gBIHEEPQAkjqAHgMT9H+xw40iidV4WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Months_loan_taken_for_log']=np.log(df['Months_loan_taken_for'])\n",
    "df['Months_loan_taken_for_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Months_loan_taken_for'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19fb3438>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Principal_loan_amount'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Principal_loan_amount' is a numerical col and it is right skewed so we have to normalize the distribution using log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2440b8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WIR1LknQIQ5nRV9VckncD/8n86ZXXVNW9wziWJOnQhnYefVV9CvjUsPZ/mFkXS1R9cmwW57gszbFZpVTV8r0kSYctPwJBkhpn0A9YkmuSPJHkngVtf5rky0nuTvLpJD89yhpHYbFxWXDfHySpJMePorZRW+I58ydJHuk9Z+5Ocs4oaxyVpZ43Sd6T5GtJ7k3y56Oq73Bh0A/etcDZB7R9pKpeV1WnAZ8E/njNqxq9azl4XEhyEvBrwENrXdAYuZZFxga4qqpO6/1Zr+93XcsBY5NkC/NX2r+uql4L/MUI6jqsGPQDVlV3AN85oO27CzaP4YCLx9aDxcal5yrg/azDMXnOIcZm3VtibH4P2FFV3+/1eWLNCzvMGPRrJMkVSR4GLmJ9zugPkuQ84JGq+tKoaxlT7+4t+V2T5LhRFzNGXg28McldST6b5BdHXdC4M+jXSFV9qKpOAq4H3j3qekYtyYuAD+E/ekv5O+CVwGnAY8CVoy1nrBwJHAecCfwhcGOSxT52RT0G/dr7J+A3R13EGHglcDLwpST7mP+YjC8kecVIqxoTVfV4Vf2wqn4E/D3znwirefuBm2re54EfMf/5N1qCQb8GkmxasHke8NVR1TIuqmpvVb28qiarapL5F+/rq+qbIy5tLCQ5YcHmbwAHna20jv0b8CaAJK8GjqKhDzkbhqFdGbteJbkBmAaOT7IfuBw4J8lrmJ95fAN45+gqHI3FxqWqrh5tVeNhiefMdJLTmH+Teh/wuyMrcISWGJtrgGt6p1z+ANhaXvl5SF4ZK0mNc+lGkhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lj/Bw65UVC2DHWSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Principal_loan_amount_log']=np.log(df['Principal_loan_amount'])\n",
    "df['Principal_loan_amount_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now the distribution is normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Principal_loan_amount'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Purpose</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Has_been_employed_for_at_most</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "      <th>Months_loan_taken_for_log</th>\n",
       "      <th>Principal_loan_amount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>13.971659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>3.871201</td>\n",
       "      <td>15.599070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>education</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>14.555541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>15.880092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>new vehicle</td>\n",
       "      <td>3</td>\n",
       "      <td>car or other</td>\n",
       "      <td>0</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>15.398604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Purpose  EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0  electronic equipment                                            4   \n",
       "1  electronic equipment                                            2   \n",
       "2             education                                            2   \n",
       "3                  FF&E                                            2   \n",
       "4           new vehicle                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                       car or other                0   \n",
       "\n",
       "                             Loan_history  high_risk_applicant  Gender  \\\n",
       "0   critical/pending loans at other banks                    0    male   \n",
       "1  existing loans paid back duly till now                    1  female   \n",
       "2   critical/pending loans at other banks                    0    male   \n",
       "3  existing loans paid back duly till now                    0    male   \n",
       "4   delay in paying off loans in the past                    1    male   \n",
       "\n",
       "               Marital_status   Housing  Years_at_current_residence  \\\n",
       "0                      single       own                           4   \n",
       "1  divorced/separated/married       own                           2   \n",
       "2                      single       own                           3   \n",
       "3                      single  for free                           4   \n",
       "4                      single  for free                           4   \n",
       "\n",
       "             Employment_status Has_been_employed_for_at_least  \\\n",
       "0  skilled employee / official                        7 years   \n",
       "1  skilled employee / official                         1 year   \n",
       "2         unskilled - resident                        4 years   \n",
       "3  skilled employee / official                        4 years   \n",
       "4  skilled employee / official                         1 year   \n",
       "\n",
       "  Has_been_employed_for_at_most  Foreign_worker Savings_account_balance  \\\n",
       "0                       4 years               1                     Low   \n",
       "1                       4 years               1                     Low   \n",
       "2                       7 years               1                     Low   \n",
       "3                       7 years               1                     Low   \n",
       "4                       4 years               1                     Low   \n",
       "\n",
       "   Months_loan_taken_for_log  Principal_loan_amount_log  \n",
       "0                   1.791759                  13.971659  \n",
       "1                   3.871201                  15.599070  \n",
       "2                   2.484907                  14.555541  \n",
       "3                   3.737670                  15.880092  \n",
       "4                   3.178054                  15.398604  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so this is my final data after cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['high_risk_applicant']).values\n",
    "y = df['high_risk_applicant'].values #devided the data into two part x and y.x is independent variable and y is dependent variable or target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['electronic equipment', 4, 'real estate', ..., 'Low',\n",
       "        1.791759469228055, 13.971659240454205],\n",
       "       ['electronic equipment', 2, 'real estate', ..., 'Low',\n",
       "        3.871201010907891, 15.59906983062699],\n",
       "       ['education', 2, 'real estate', ..., 'Low', 2.4849066497880004,\n",
       "        14.55554132442307],\n",
       "       ...,\n",
       "       ['electronic equipment', 4, 'car or other', ..., 'Low',\n",
       "        2.4849066497880004, 13.597354548161103],\n",
       "       ['electronic equipment', 4, 'car or other', ..., 'Low',\n",
       "        3.8066624897703196, 14.427989835456765],\n",
       "       ['used vehicle', 3, 'car or other', ..., 'Medium',\n",
       "        3.8066624897703196, 15.33633581204177]], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data set into train and test 80% for train and 20% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['new vehicle', 4, 'car or other', ..., 'Medium',\n",
       "        3.58351893845611, 14.867031239098534],\n",
       "       ['new vehicle', 4,\n",
       "        'building society savings agreement/life insurance', ..., 'Low',\n",
       "        3.1780538303479458, 14.954304636265215],\n",
       "       ['new vehicle', 4,\n",
       "        'building society savings agreement/life insurance', ...,\n",
       "        'Medium', 4.0943445622221, 15.818071055308152],\n",
       "       ...,\n",
       "       ['education', 1, 'real estate', ..., 'Low', 2.1972245773362196,\n",
       "        15.158897418072888],\n",
       "       ['FF&E', 2, 'real estate', ..., 'Low', 2.8903717578961645,\n",
       "        14.471993754152628],\n",
       "       ['business', 1,\n",
       "        'building society savings agreement/life insurance', ...,\n",
       "        'Medium', 3.58351893845611, 16.103692420647576]], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so there are lots of cetagorical veriable so i import LabelEncoder and apply it on all the cetagorical veriable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,14):\n",
    "    X_train[:,i]=labelencoder_X.fit_transform(X_train[:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 3, 1, ..., 2, 3.58351893845611, 14.867031239098534],\n",
       "       [6, 3, 0, ..., 1, 3.1780538303479458, 14.954304636265215],\n",
       "       [6, 3, 0, ..., 2, 4.0943445622221, 15.818071055308152],\n",
       "       ...,\n",
       "       [4, 0, 2, ..., 1, 2.1972245773362196, 15.158897418072888],\n",
       "       [0, 1, 2, ..., 1, 2.8903717578961645, 14.471993754152628],\n",
       "       [1, 0, 0, ..., 2, 3.58351893845611, 16.103692420647576]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_train values are already in 0 and 1 so i didnt apply labelencoder in y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,14):\n",
    "    X_test[:,i]=labelencoder_X.fit_transform(X_test[:,i]) #same thing do in X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 3, 0, ..., 1, 3.58351893845611, 15.191502026088267],\n",
       "       [6, 0, 2, ..., 2, 2.1972245773362196, 15.090035018241155],\n",
       "       [0, 2, 2, ..., 1, 2.8903717578961645, 14.737783361515977],\n",
       "       ...,\n",
       "       [2, 0, 0, ..., 1, 1.791759469228055, 13.745088093667729],\n",
       "       [6, 1, 1, ..., 2, 3.58351893845611, 15.905644729182853],\n",
       "       [5, 1, 1, ..., 2, 1.791759469228055, 14.112647789186811]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the accuracy of decision tree is:  0.66\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "DTClassifier=DecisionTreeClassifier(criterion='entropy',random_state=0)\n",
    "DTClassifier.fit(X_train,y_train)\n",
    "y_pred=DTClassifier.predict(X_test)\n",
    "from sklearn import metrics\n",
    "print(\"the accuracy of decision tree is: \", metrics.accuracy_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.67"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.655"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier()\n",
    "xgb.fit(X_train,y_train)\n",
    "y_pred = xgb.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.655"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "NBClassifier=GaussianNB()\n",
    "NBClassifier.fit(X_train,y_train)\n",
    "y_pred = NBClassifier.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
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