!excelR/assignments/Gists/SVM- Salary Data.ipynb

SVM- Salary Data.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Q1) Prepare a classification model using SVM for salary data "
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:42.383141Z",
          "start_time": "2022-01-30T17:08:39.030193Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Import required libraries\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom sklearn.preprocessing import MinMaxScaler, LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score, confusion_matrix",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:42.555025Z",
          "start_time": "2022-01-30T17:08:42.389129Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "data = pd.read_csv('SalaryData_Train.csv')\ndata.head()",
      "execution_count": 2,
      "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>age</th>\n      <th>workclass</th>\n      <th>education</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>38</td>\n      <td>Private</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>53</td>\n      <td>Private</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>28</td>\n      <td>Private</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age          workclass   education  educationno        maritalstatus  \\\n0   39          State-gov   Bachelors           13        Never-married   \n1   50   Self-emp-not-inc   Bachelors           13   Married-civ-spouse   \n2   38            Private     HS-grad            9             Divorced   \n3   53            Private        11th            7   Married-civ-spouse   \n4   28            Private   Bachelors           13   Married-civ-spouse   \n\n           occupation    relationship    race      sex  capitalgain  \\\n0        Adm-clerical   Not-in-family   White     Male         2174   \n1     Exec-managerial         Husband   White     Male            0   \n2   Handlers-cleaners   Not-in-family   White     Male            0   \n3   Handlers-cleaners         Husband   Black     Male            0   \n4      Prof-specialty            Wife   Black   Female            0   \n\n   capitalloss  hoursperweek          native  Salary  \n0            0            40   United-States   <=50K  \n1            0            13   United-States   <=50K  \n2            0            40   United-States   <=50K  \n3            0            40   United-States   <=50K  \n4            0            40            Cuba   <=50K  "
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:42.618377Z",
          "start_time": "2022-01-30T17:08:42.560022Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "data.shape",
      "execution_count": 3,
      "outputs": [
        {
          "data": {
            "text/plain": "(30161, 14)"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:42.790392Z",
          "start_time": "2022-01-30T17:08:42.624371Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "data.info()",
      "execution_count": 4,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 30161 entries, 0 to 30160\nData columns (total 14 columns):\nage              30161 non-null int64\nworkclass        30161 non-null object\neducation        30161 non-null object\neducationno      30161 non-null int64\nmaritalstatus    30161 non-null object\noccupation       30161 non-null object\nrelationship     30161 non-null object\nrace             30161 non-null object\nsex              30161 non-null object\ncapitalgain      30161 non-null int64\ncapitalloss      30161 non-null int64\nhoursperweek     30161 non-null int64\nnative           30161 non-null object\nSalary           30161 non-null object\ndtypes: int64(5), object(9)\nmemory usage: 3.2+ MB\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:42.950337Z",
          "start_time": "2022-01-30T17:08:42.801383Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Checking for the duplicate rows\ndata.duplicated().sum()",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "text/plain": "3258"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:43.078257Z",
          "start_time": "2022-01-30T17:08:42.958329Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Dropping the duplicate rows\ndata= data.drop_duplicates()\ndata.reset_index(drop=True, inplace=True)\ndata.head()",
      "execution_count": 6,
      "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>age</th>\n      <th>workclass</th>\n      <th>education</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>38</td>\n      <td>Private</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>53</td>\n      <td>Private</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>28</td>\n      <td>Private</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age          workclass   education  educationno        maritalstatus  \\\n0   39          State-gov   Bachelors           13        Never-married   \n1   50   Self-emp-not-inc   Bachelors           13   Married-civ-spouse   \n2   38            Private     HS-grad            9             Divorced   \n3   53            Private        11th            7   Married-civ-spouse   \n4   28            Private   Bachelors           13   Married-civ-spouse   \n\n           occupation    relationship    race      sex  capitalgain  \\\n0        Adm-clerical   Not-in-family   White     Male         2174   \n1     Exec-managerial         Husband   White     Male            0   \n2   Handlers-cleaners   Not-in-family   White     Male            0   \n3   Handlers-cleaners         Husband   Black     Male            0   \n4      Prof-specialty            Wife   Black   Female            0   \n\n   capitalloss  hoursperweek          native  Salary  \n0            0            40   United-States   <=50K  \n1            0            13   United-States   <=50K  \n2            0            40   United-States   <=50K  \n3            0            40   United-States   <=50K  \n4            0            40            Cuba   <=50K  "
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:43.110239Z",
          "start_time": "2022-01-30T17:08:43.084250Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# No of rows and columns \ndata.shape",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/plain": "(26903, 14)"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:43.269604Z",
          "start_time": "2022-01-30T17:08:43.118230Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Describing the data\ndata.describe()",
      "execution_count": 8,
      "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>age</th>\n      <th>educationno</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>count</td>\n      <td>26903.000000</td>\n      <td>26903.000000</td>\n      <td>26903.000000</td>\n      <td>26903.000000</td>\n      <td>26903.000000</td>\n    </tr>\n    <tr>\n      <td>mean</td>\n      <td>39.049140</td>\n      <td>10.144296</td>\n      <td>1215.443259</td>\n      <td>98.427499</td>\n      <td>41.164591</td>\n    </tr>\n    <tr>\n      <td>std</td>\n      <td>13.188825</td>\n      <td>2.620154</td>\n      <td>7805.366384</td>\n      <td>425.493211</td>\n      <td>12.347051</td>\n    </tr>\n    <tr>\n      <td>min</td>\n      <td>17.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <td>25%</td>\n      <td>29.000000</td>\n      <td>9.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <td>50%</td>\n      <td>38.000000</td>\n      <td>10.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <td>75%</td>\n      <td>48.000000</td>\n      <td>13.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>45.000000</td>\n    </tr>\n    <tr>\n      <td>max</td>\n      <td>90.000000</td>\n      <td>16.000000</td>\n      <td>99999.000000</td>\n      <td>4356.000000</td>\n      <td>99.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                age   educationno   capitalgain   capitalloss  hoursperweek\ncount  26903.000000  26903.000000  26903.000000  26903.000000  26903.000000\nmean      39.049140     10.144296   1215.443259     98.427499     41.164591\nstd       13.188825      2.620154   7805.366384    425.493211     12.347051\nmin       17.000000      1.000000      0.000000      0.000000      1.000000\n25%       29.000000      9.000000      0.000000      0.000000     40.000000\n50%       38.000000     10.000000      0.000000      0.000000     40.000000\n75%       48.000000     13.000000      0.000000      0.000000     45.000000\nmax       90.000000     16.000000  99999.000000   4356.000000     99.000000"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:43.379583Z",
          "start_time": "2022-01-30T17:08:43.276597Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Converting categorical to Numeric\nle = LabelEncoder()\ndata['salary_num'] = le.fit_transform(data['Salary'])\ndata.head()",
      "execution_count": 9,
      "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>age</th>\n      <th>workclass</th>\n      <th>education</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n      <th>salary_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>38</td>\n      <td>Private</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>53</td>\n      <td>Private</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>28</td>\n      <td>Private</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age          workclass   education  educationno        maritalstatus  \\\n0   39          State-gov   Bachelors           13        Never-married   \n1   50   Self-emp-not-inc   Bachelors           13   Married-civ-spouse   \n2   38            Private     HS-grad            9             Divorced   \n3   53            Private        11th            7   Married-civ-spouse   \n4   28            Private   Bachelors           13   Married-civ-spouse   \n\n           occupation    relationship    race      sex  capitalgain  \\\n0        Adm-clerical   Not-in-family   White     Male         2174   \n1     Exec-managerial         Husband   White     Male            0   \n2   Handlers-cleaners   Not-in-family   White     Male            0   \n3   Handlers-cleaners         Husband   Black     Male            0   \n4      Prof-specialty            Wife   Black   Female            0   \n\n   capitalloss  hoursperweek          native  Salary  salary_num  \n0            0            40   United-States   <=50K           0  \n1            0            13   United-States   <=50K           0  \n2            0            40   United-States   <=50K           0  \n3            0            40   United-States   <=50K           0  \n4            0            40            Cuba   <=50K           0  "
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:43.535488Z",
          "start_time": "2022-01-30T17:08:43.383583Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Checking the correlation between the features\ndata.corr()",
      "execution_count": 10,
      "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>age</th>\n      <th>educationno</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>salary_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>age</td>\n      <td>1.000000</td>\n      <td>0.038048</td>\n      <td>0.077235</td>\n      <td>0.052684</td>\n      <td>0.072529</td>\n      <td>0.223502</td>\n    </tr>\n    <tr>\n      <td>educationno</td>\n      <td>0.038048</td>\n      <td>1.000000</td>\n      <td>0.126085</td>\n      <td>0.080025</td>\n      <td>0.145491</td>\n      <td>0.332178</td>\n    </tr>\n    <tr>\n      <td>capitalgain</td>\n      <td>0.077235</td>\n      <td>0.126085</td>\n      <td>1.000000</td>\n      <td>-0.036023</td>\n      <td>0.079403</td>\n      <td>0.228865</td>\n    </tr>\n    <tr>\n      <td>capitalloss</td>\n      <td>0.052684</td>\n      <td>0.080025</td>\n      <td>-0.036023</td>\n      <td>1.000000</td>\n      <td>0.049093</td>\n      <td>0.153287</td>\n    </tr>\n    <tr>\n      <td>hoursperweek</td>\n      <td>0.072529</td>\n      <td>0.145491</td>\n      <td>0.079403</td>\n      <td>0.049093</td>\n      <td>1.000000</td>\n      <td>0.225812</td>\n    </tr>\n    <tr>\n      <td>salary_num</td>\n      <td>0.223502</td>\n      <td>0.332178</td>\n      <td>0.228865</td>\n      <td>0.153287</td>\n      <td>0.225812</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                   age  educationno  capitalgain  capitalloss  hoursperweek  \\\nage           1.000000     0.038048     0.077235     0.052684      0.072529   \neducationno   0.038048     1.000000     0.126085     0.080025      0.145491   \ncapitalgain   0.077235     0.126085     1.000000    -0.036023      0.079403   \ncapitalloss   0.052684     0.080025    -0.036023     1.000000      0.049093   \nhoursperweek  0.072529     0.145491     0.079403     0.049093      1.000000   \nsalary_num    0.223502     0.332178     0.228865     0.153287      0.225812   \n\n              salary_num  \nage             0.223502  \neducationno     0.332178  \ncapitalgain     0.228865  \ncapitalloss     0.153287  \nhoursperweek    0.225812  \nsalary_num      1.000000  "
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:43.662412Z",
          "start_time": "2022-01-30T17:08:43.542487Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Salary_num Correlation W.R.T all other features\ndata.corr()['salary_num']",
      "execution_count": 11,
      "outputs": [
        {
          "data": {
            "text/plain": "age             0.223502\neducationno     0.332178\ncapitalgain     0.228865\ncapitalloss     0.153287\nhoursperweek    0.225812\nsalary_num      1.000000\nName: salary_num, dtype: float64"
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:43.993080Z",
          "start_time": "2022-01-30T17:08:43.671406Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Checking whether education and educationno columns are same or not \ndata1 = data.copy()\ndata1['edu_merge'] = data['education'] + ' = ' + (data['educationno'].astype(str))\ndata1['edu_merge'].head()\ndata1.head()",
      "execution_count": 12,
      "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>age</th>\n      <th>workclass</th>\n      <th>education</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n      <th>salary_num</th>\n      <th>edu_merge</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n      <td>Bachelors = 13</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n      <td>Bachelors = 13</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>38</td>\n      <td>Private</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n      <td>HS-grad = 9</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>53</td>\n      <td>Private</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n      <td>11th = 7</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>28</td>\n      <td>Private</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n      <td>Bachelors = 13</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age          workclass   education  educationno        maritalstatus  \\\n0   39          State-gov   Bachelors           13        Never-married   \n1   50   Self-emp-not-inc   Bachelors           13   Married-civ-spouse   \n2   38            Private     HS-grad            9             Divorced   \n3   53            Private        11th            7   Married-civ-spouse   \n4   28            Private   Bachelors           13   Married-civ-spouse   \n\n           occupation    relationship    race      sex  capitalgain  \\\n0        Adm-clerical   Not-in-family   White     Male         2174   \n1     Exec-managerial         Husband   White     Male            0   \n2   Handlers-cleaners   Not-in-family   White     Male            0   \n3   Handlers-cleaners         Husband   Black     Male            0   \n4      Prof-specialty            Wife   Black   Female            0   \n\n   capitalloss  hoursperweek          native  Salary  salary_num  \\\n0            0            40   United-States   <=50K           0   \n1            0            13   United-States   <=50K           0   \n2            0            40   United-States   <=50K           0   \n3            0            40   United-States   <=50K           0   \n4            0            40            Cuba   <=50K           0   \n\n         edu_merge  \n0   Bachelors = 13  \n1   Bachelors = 13  \n2      HS-grad = 9  \n3         11th = 7  \n4   Bachelors = 13  "
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:44.024057Z",
          "start_time": "2022-01-30T17:08:44.001073Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Checking unique values in the column\ndata1['edu_merge'].unique()",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/plain": "array([' Bachelors = 13', ' HS-grad = 9', ' 11th = 7', ' Masters = 14',\n       ' 9th = 5', ' Some-college = 10', ' Assoc-acdm = 12',\n       ' 7th-8th = 4', ' Doctorate = 16', ' Assoc-voc = 11',\n       ' Prof-school = 15', ' 5th-6th = 3', ' 10th = 6', ' Preschool = 1',\n       ' 12th = 8', ' 1st-4th = 2'], dtype=object)"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-29T15:03:52.647448Z",
          "start_time": "2022-01-29T15:03:52.042461Z"
        }
      },
      "cell_type": "markdown",
      "source": "### we can drop education column as it is a numerical representation of column educationno"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:08:44.163976Z",
          "start_time": "2022-01-30T17:08:44.028059Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Dropping education column\ndata = data.drop(['education'],axis=1)\ndata.head()",
      "execution_count": 14,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n      <th>salary_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>38</td>\n      <td>Private</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>53</td>\n      <td>Private</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>28</td>\n      <td>Private</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age          workclass  educationno        maritalstatus  \\\n0   39          State-gov           13        Never-married   \n1   50   Self-emp-not-inc           13   Married-civ-spouse   \n2   38            Private            9             Divorced   \n3   53            Private            7   Married-civ-spouse   \n4   28            Private           13   Married-civ-spouse   \n\n           occupation    relationship    race      sex  capitalgain  \\\n0        Adm-clerical   Not-in-family   White     Male         2174   \n1     Exec-managerial         Husband   White     Male            0   \n2   Handlers-cleaners   Not-in-family   White     Male            0   \n3   Handlers-cleaners         Husband   Black     Male            0   \n4      Prof-specialty            Wife   Black   Female            0   \n\n   capitalloss  hoursperweek          native  Salary  salary_num  \n0            0            40   United-States   <=50K           0  \n1            0            13   United-States   <=50K           0  \n2            0            40   United-States   <=50K           0  \n3            0            40   United-States   <=50K           0  \n4            0            40            Cuba   <=50K           0  "
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:09.794572Z",
          "start_time": "2022-01-30T17:08:44.170967Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#plotting features\nsns.pairplot(data)\nplt.title('pair plots to understand correlation')",
      "execution_count": 15,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0.5, 1, 'pair plots to understand correlation')"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1080x1080 with 42 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:10.090392Z",
          "start_time": "2022-01-30T17:09:09.799574Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "sns.countplot(data['salary_num'])",
      "execution_count": 16,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x20733dbff88>"
          },
          "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"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:11.325348Z",
          "start_time": "2022-01-30T17:09:10.094387Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Boxplots of all features\nfor column in data:\n    plt.figure()\n    if data[column].dtype == 'int64':\n        data.boxplot([column])",
      "execution_count": 17,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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93jbUz0eANw7+WPmYoXp+wPK69ccGyy4/Ad474c+2j+XfAK4f1PC+qrphkr4k8FJ6SWqWM3BJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhr1v2hMx3Nv7YwAAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:11.419289Z",
          "start_time": "2022-01-30T17:09:11.332345Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Transformation\n\nmmscaler = MinMaxScaler()\ncols = ['age', 'educationno', 'capitalgain','capitalloss']\ndata[cols] = mmscaler.fit_transform(data[cols])\ndata.head()",
      "execution_count": 18,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n      <th>salary_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.301370</td>\n      <td>State-gov</td>\n      <td>0.800000</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.02174</td>\n      <td>0.0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.452055</td>\n      <td>Self-emp-not-inc</td>\n      <td>0.800000</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.287671</td>\n      <td>Private</td>\n      <td>0.533333</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.493151</td>\n      <td>Private</td>\n      <td>0.400000</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.150685</td>\n      <td>Private</td>\n      <td>0.800000</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age          workclass  educationno        maritalstatus  \\\n0  0.301370          State-gov     0.800000        Never-married   \n1  0.452055   Self-emp-not-inc     0.800000   Married-civ-spouse   \n2  0.287671            Private     0.533333             Divorced   \n3  0.493151            Private     0.400000   Married-civ-spouse   \n4  0.150685            Private     0.800000   Married-civ-spouse   \n\n           occupation    relationship    race      sex  capitalgain  \\\n0        Adm-clerical   Not-in-family   White     Male      0.02174   \n1     Exec-managerial         Husband   White     Male      0.00000   \n2   Handlers-cleaners   Not-in-family   White     Male      0.00000   \n3   Handlers-cleaners         Husband   Black     Male      0.00000   \n4      Prof-specialty            Wife   Black   Female      0.00000   \n\n   capitalloss  hoursperweek          native  Salary  salary_num  \n0          0.0            40   United-States   <=50K           0  \n1          0.0            13   United-States   <=50K           0  \n2          0.0            40   United-States   <=50K           0  \n3          0.0            40   United-States   <=50K           0  \n4          0.0            40            Cuba   <=50K           0  "
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:11.482641Z",
          "start_time": "2022-01-30T17:09:11.425286Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Converting all categorical columns to numeric\n#data_final= pd.get_dummies(data, drop_first= True)\n#data_final.head()",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:11.703537Z",
          "start_time": "2022-01-30T17:09:11.486026Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Converting all categorical columns to numeric using label encoder\nle = LabelEncoder()\ncols = ['workclass','maritalstatus', 'occupation', 'relationship', 'race', 'sex','hoursperweek', 'native']\nfor i in cols:\n    data[i] = le.fit_transform(data[i])\ndata.head()",
      "execution_count": 20,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n      <th>salary_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.301370</td>\n      <td>5</td>\n      <td>0.800000</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.02174</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.452055</td>\n      <td>4</td>\n      <td>0.800000</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>12</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.287671</td>\n      <td>2</td>\n      <td>0.533333</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.493151</td>\n      <td>2</td>\n      <td>0.400000</td>\n      <td>2</td>\n      <td>5</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.150685</td>\n      <td>2</td>\n      <td>0.800000</td>\n      <td>2</td>\n      <td>9</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>4</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age  workclass  educationno  maritalstatus  occupation  relationship  \\\n0  0.301370          5     0.800000              4           0             1   \n1  0.452055          4     0.800000              2           3             0   \n2  0.287671          2     0.533333              0           5             1   \n3  0.493151          2     0.400000              2           5             0   \n4  0.150685          2     0.800000              2           9             5   \n\n   race  sex  capitalgain  capitalloss  hoursperweek  native  Salary  \\\n0     4    1      0.02174          0.0            39      37   <=50K   \n1     4    1      0.00000          0.0            12      37   <=50K   \n2     4    1      0.00000          0.0            39      37   <=50K   \n3     2    1      0.00000          0.0            39      37   <=50K   \n4     2    0      0.00000          0.0            39       4   <=50K   \n\n   salary_num  \n0           0  \n1           0  \n2           0  \n3           0  \n4           0  "
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:11.719528Z",
          "start_time": "2022-01-30T17:09:11.708532Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Creating a copy of data\ndata_final = data.copy()",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:11.845500Z",
          "start_time": "2022-01-30T17:09:11.723526Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Splitting data\nX = data_final.drop(['Salary','salary_num'],axis=1)\nX.head()",
      "execution_count": 22,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.301370</td>\n      <td>5</td>\n      <td>0.800000</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.02174</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.452055</td>\n      <td>4</td>\n      <td>0.800000</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>12</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.287671</td>\n      <td>2</td>\n      <td>0.533333</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.493151</td>\n      <td>2</td>\n      <td>0.400000</td>\n      <td>2</td>\n      <td>5</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.150685</td>\n      <td>2</td>\n      <td>0.800000</td>\n      <td>2</td>\n      <td>9</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age  workclass  educationno  maritalstatus  occupation  relationship  \\\n0  0.301370          5     0.800000              4           0             1   \n1  0.452055          4     0.800000              2           3             0   \n2  0.287671          2     0.533333              0           5             1   \n3  0.493151          2     0.400000              2           5             0   \n4  0.150685          2     0.800000              2           9             5   \n\n   race  sex  capitalgain  capitalloss  hoursperweek  native  \n0     4    1      0.02174          0.0            39      37  \n1     4    1      0.00000          0.0            12      37  \n2     4    1      0.00000          0.0            39      37  \n3     2    1      0.00000          0.0            39      37  \n4     2    0      0.00000          0.0            39       4  "
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:11.940501Z",
          "start_time": "2022-01-30T17:09:11.859488Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "y= data_final['salary_num']\ny.head()",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "text/plain": "0    0\n1    0\n2    0\n3    0\n4    0\nName: salary_num, dtype: int32"
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:09:12.019543Z",
          "start_time": "2022-01-30T17:09:11.952488Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Splitting data into X_train, X_test, y_train, y_test\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## SVC Algorithm"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Using Grid search CV"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:07.431717Z",
          "start_time": "2022-01-30T17:09:12.029537Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Grid search CV to find better parameters based on SVC\nclf = SVC()\nparam_grid = [{'kernel':['poly'],'gamma':[0.5],'degree': [1],'C':[0.1] }] # Used rbc method and few values of C, degree and gamma as well. Have taken random values as it is taking long time to run\ngsv = GridSearchCV(clf,param_grid,cv=10)\ngsv.fit(X_train,y_train)",
      "execution_count": 25,
      "outputs": [
        {
          "data": {
            "text/plain": "GridSearchCV(cv=10, error_score='raise-deprecating',\n             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n                           decision_function_shape='ovr', degree=3,\n                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n                           probability=False, random_state=None, shrinking=True,\n                           tol=0.001, verbose=False),\n             iid='warn', n_jobs=None,\n             param_grid=[{'C': [0.1], 'degree': [1], 'gamma': [0.5],\n                          'kernel': ['poly']}],\n             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n             scoring=None, verbose=0)"
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:07.447707Z",
          "start_time": "2022-01-30T17:13:07.436715Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# As per grid sreach CV best parameters and score\ngsv.best_params_ , gsv.best_score_ ",
      "execution_count": 26,
      "outputs": [
        {
          "data": {
            "text/plain": "({'C': 0.1, 'degree': 1, 'gamma': 0.5, 'kernel': 'poly'}, 0.7959297463061054)"
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Final SVC model"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:32.174592Z",
          "start_time": "2022-01-30T17:13:07.459701Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Creating a final model with the best parameters\nclf = SVC(C= 0.1, degree= 1, gamma= 0.5, kernel= 'poly')\nclf.fit(X_train , y_train)",
      "execution_count": 27,
      "outputs": [
        {
          "data": {
            "text/plain": "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n    decision_function_shape='ovr', degree=1, gamma=0.5, kernel='poly',\n    max_iter=-1, probability=False, random_state=None, shrinking=True,\n    tol=0.001, verbose=False)"
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-29T18:36:44.335955Z",
          "start_time": "2022-01-29T18:36:44.310969Z"
        }
      },
      "cell_type": "markdown",
      "source": "### Predictions and model accuracy"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:33.561599Z",
          "start_time": "2022-01-30T17:13:32.182583Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Predicting the values \ny_pred = clf.predict(X_test)\n#Accuracy of model\nacc = accuracy_score(y_test, y_pred) * 100\nprint(\"Accuracy =\", acc)\n# Confusion matrix\nconfusion_matrix(y_test, y_pred)",
      "execution_count": 28,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Accuracy = 79.61345474818808\n"
        },
        {
          "data": {
            "text/plain": "array([[3872,  108],\n       [ 989,  412]], dtype=int64)"
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Testing the model/ prediction using test data"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:33.733980Z",
          "start_time": "2022-01-30T17:13:33.568592Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#importing amd reading test data\ntest_data = pd.read_csv('SalaryData_Test.csv')\ntest_data.head()",
      "execution_count": 29,
      "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>age</th>\n      <th>workclass</th>\n      <th>education</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>25</td>\n      <td>Private</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Never-married</td>\n      <td>Machine-op-inspct</td>\n      <td>Own-child</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>38</td>\n      <td>Private</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Farming-fishing</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>50</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>28</td>\n      <td>Local-gov</td>\n      <td>Assoc-acdm</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Protective-serv</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>44</td>\n      <td>Private</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>7688</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>34</td>\n      <td>Private</td>\n      <td>10th</td>\n      <td>6</td>\n      <td>Never-married</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age   workclass      education  educationno        maritalstatus  \\\n0   25     Private           11th            7        Never-married   \n1   38     Private        HS-grad            9   Married-civ-spouse   \n2   28   Local-gov     Assoc-acdm           12   Married-civ-spouse   \n3   44     Private   Some-college           10   Married-civ-spouse   \n4   34     Private           10th            6        Never-married   \n\n           occupation    relationship    race    sex  capitalgain  \\\n0   Machine-op-inspct       Own-child   Black   Male            0   \n1     Farming-fishing         Husband   White   Male            0   \n2     Protective-serv         Husband   White   Male            0   \n3   Machine-op-inspct         Husband   Black   Male         7688   \n4       Other-service   Not-in-family   White   Male            0   \n\n   capitalloss  hoursperweek          native  Salary  \n0            0            40   United-States   <=50K  \n1            0            50   United-States   <=50K  \n2            0            40   United-States    >50K  \n3            0            40   United-States    >50K  \n4            0            30   United-States   <=50K  "
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:33.779954Z",
          "start_time": "2022-01-30T17:13:33.737980Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Cleaning test data\ntest_data = test_data.drop(['education'], axis=1)\ntest_data.head()",
      "execution_count": 30,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>25</td>\n      <td>Private</td>\n      <td>7</td>\n      <td>Never-married</td>\n      <td>Machine-op-inspct</td>\n      <td>Own-child</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>38</td>\n      <td>Private</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Farming-fishing</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>50</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>28</td>\n      <td>Local-gov</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Protective-serv</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>44</td>\n      <td>Private</td>\n      <td>10</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>7688</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>34</td>\n      <td>Private</td>\n      <td>6</td>\n      <td>Never-married</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age   workclass  educationno        maritalstatus          occupation  \\\n0   25     Private            7        Never-married   Machine-op-inspct   \n1   38     Private            9   Married-civ-spouse     Farming-fishing   \n2   28   Local-gov           12   Married-civ-spouse     Protective-serv   \n3   44     Private           10   Married-civ-spouse   Machine-op-inspct   \n4   34     Private            6        Never-married       Other-service   \n\n     relationship    race    sex  capitalgain  capitalloss  hoursperweek  \\\n0       Own-child   Black   Male            0            0            40   \n1         Husband   White   Male            0            0            50   \n2         Husband   White   Male            0            0            40   \n3         Husband   Black   Male         7688            0            40   \n4   Not-in-family   White   Male            0            0            30   \n\n           native  Salary  \n0   United-States   <=50K  \n1   United-States   <=50K  \n2   United-States    >50K  \n3   United-States    >50K  \n4   United-States   <=50K  "
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:33.905019Z",
          "start_time": "2022-01-30T17:13:33.787949Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Transformation\nmmscaler = MinMaxScaler()\ncols = ['age', 'educationno', 'capitalgain','capitalloss']\ntest_data[cols] = mmscaler.fit_transform(test_data[cols])\ntest_data.head()",
      "execution_count": 31,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.109589</td>\n      <td>Private</td>\n      <td>0.400000</td>\n      <td>Never-married</td>\n      <td>Machine-op-inspct</td>\n      <td>Own-child</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.287671</td>\n      <td>Private</td>\n      <td>0.533333</td>\n      <td>Married-civ-spouse</td>\n      <td>Farming-fishing</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>50</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.150685</td>\n      <td>Local-gov</td>\n      <td>0.733333</td>\n      <td>Married-civ-spouse</td>\n      <td>Protective-serv</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.369863</td>\n      <td>Private</td>\n      <td>0.600000</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0.076881</td>\n      <td>0.0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.232877</td>\n      <td>Private</td>\n      <td>0.333333</td>\n      <td>Never-married</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>30</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age   workclass  educationno        maritalstatus          occupation  \\\n0  0.109589     Private     0.400000        Never-married   Machine-op-inspct   \n1  0.287671     Private     0.533333   Married-civ-spouse     Farming-fishing   \n2  0.150685   Local-gov     0.733333   Married-civ-spouse     Protective-serv   \n3  0.369863     Private     0.600000   Married-civ-spouse   Machine-op-inspct   \n4  0.232877     Private     0.333333        Never-married       Other-service   \n\n     relationship    race    sex  capitalgain  capitalloss  hoursperweek  \\\n0       Own-child   Black   Male     0.000000          0.0            40   \n1         Husband   White   Male     0.000000          0.0            50   \n2         Husband   White   Male     0.000000          0.0            40   \n3         Husband   Black   Male     0.076881          0.0            40   \n4   Not-in-family   White   Male     0.000000          0.0            30   \n\n           native  Salary  \n0   United-States   <=50K  \n1   United-States   <=50K  \n2   United-States    >50K  \n3   United-States    >50K  \n4   United-States   <=50K  "
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:34.045997Z",
          "start_time": "2022-01-30T17:13:33.909018Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Converting all categorical columns to numeric using label encoder\nle = LabelEncoder()\ncols = ['workclass','maritalstatus', 'occupation', 'relationship', 'race', 'sex','hoursperweek', 'native']\nfor i in cols:\n    test_data[i] = le.fit_transform(test_data[i])\ntest_data.head()",
      "execution_count": 32,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.109589</td>\n      <td>2</td>\n      <td>0.400000</td>\n      <td>4</td>\n      <td>6</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.287671</td>\n      <td>2</td>\n      <td>0.533333</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>49</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.150685</td>\n      <td>1</td>\n      <td>0.733333</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.369863</td>\n      <td>2</td>\n      <td>0.600000</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.076881</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.232877</td>\n      <td>2</td>\n      <td>0.333333</td>\n      <td>4</td>\n      <td>7</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>29</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age  workclass  educationno  maritalstatus  occupation  relationship  \\\n0  0.109589          2     0.400000              4           6             3   \n1  0.287671          2     0.533333              2           4             0   \n2  0.150685          1     0.733333              2          10             0   \n3  0.369863          2     0.600000              2           6             0   \n4  0.232877          2     0.333333              4           7             1   \n\n   race  sex  capitalgain  capitalloss  hoursperweek  native  Salary  \n0     2    1     0.000000          0.0            39      37   <=50K  \n1     4    1     0.000000          0.0            49      37   <=50K  \n2     4    1     0.000000          0.0            39      37    >50K  \n3     2    1     0.076881          0.0            39      37    >50K  \n4     4    1     0.000000          0.0            29      37   <=50K  "
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:34.170022Z",
          "start_time": "2022-01-30T17:13:34.050993Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Removing the duplicates\ntest_data = test_data.drop_duplicates()\ntest_data.reset_index(drop=True, inplace=True)\ntest_data.head()",
      "execution_count": 33,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.109589</td>\n      <td>2</td>\n      <td>0.400000</td>\n      <td>4</td>\n      <td>6</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.287671</td>\n      <td>2</td>\n      <td>0.533333</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>49</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.150685</td>\n      <td>1</td>\n      <td>0.733333</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.369863</td>\n      <td>2</td>\n      <td>0.600000</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.076881</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.232877</td>\n      <td>2</td>\n      <td>0.333333</td>\n      <td>4</td>\n      <td>7</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>29</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age  workclass  educationno  maritalstatus  occupation  relationship  \\\n0  0.109589          2     0.400000              4           6             3   \n1  0.287671          2     0.533333              2           4             0   \n2  0.150685          1     0.733333              2          10             0   \n3  0.369863          2     0.600000              2           6             0   \n4  0.232877          2     0.333333              4           7             1   \n\n   race  sex  capitalgain  capitalloss  hoursperweek  native  Salary  \n0     2    1     0.000000          0.0            39      37   <=50K  \n1     4    1     0.000000          0.0            49      37   <=50K  \n2     4    1     0.000000          0.0            39      37    >50K  \n3     2    1     0.076881          0.0            39      37    >50K  \n4     4    1     0.000000          0.0            29      37   <=50K  "
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:34.248970Z",
          "start_time": "2022-01-30T17:13:34.174019Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Splitting data\nX_test_new = test_data.iloc[:,:-1]\nX_test_new.head()",
      "execution_count": 34,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.109589</td>\n      <td>2</td>\n      <td>0.400000</td>\n      <td>4</td>\n      <td>6</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.287671</td>\n      <td>2</td>\n      <td>0.533333</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>49</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.150685</td>\n      <td>1</td>\n      <td>0.733333</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.369863</td>\n      <td>2</td>\n      <td>0.600000</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.076881</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.232877</td>\n      <td>2</td>\n      <td>0.333333</td>\n      <td>4</td>\n      <td>7</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>29</td>\n      <td>37</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age  workclass  educationno  maritalstatus  occupation  relationship  \\\n0  0.109589          2     0.400000              4           6             3   \n1  0.287671          2     0.533333              2           4             0   \n2  0.150685          1     0.733333              2          10             0   \n3  0.369863          2     0.600000              2           6             0   \n4  0.232877          2     0.333333              4           7             1   \n\n   race  sex  capitalgain  capitalloss  hoursperweek  native  \n0     2    1     0.000000          0.0            39      37  \n1     4    1     0.000000          0.0            49      37  \n2     4    1     0.000000          0.0            39      37  \n3     2    1     0.076881          0.0            39      37  \n4     4    1     0.000000          0.0            29      37  "
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:37.697881Z",
          "start_time": "2022-01-30T17:13:34.252969Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Predictions\ny_pred_test = clf.predict(X_test_new)",
      "execution_count": 35,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:13:37.728873Z",
          "start_time": "2022-01-30T17:13:37.700890Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Predicted values\npd.DataFrame(y_pred_test)",
      "execution_count": 36,
      "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>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>14125</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>14126</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>14127</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>14128</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>14129</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>14130 rows × 1 columns</p>\n</div>",
            "text/plain": "       0\n0      0\n1      0\n2      0\n3      0\n4      0\n...   ..\n14125  0\n14126  0\n14127  0\n14128  0\n14129  0\n\n[14130 rows x 1 columns]"
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:16:43.230572Z",
          "start_time": "2022-01-30T17:16:43.180605Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Converting categorical to Numeric\nle = LabelEncoder()\ntest_data['salary_num'] = le.fit_transform(test_data['Salary'])\ntest_data.head()",
      "execution_count": 39,
      "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>age</th>\n      <th>workclass</th>\n      <th>educationno</th>\n      <th>maritalstatus</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capitalgain</th>\n      <th>capitalloss</th>\n      <th>hoursperweek</th>\n      <th>native</th>\n      <th>Salary</th>\n      <th>salary_num</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.109589</td>\n      <td>2</td>\n      <td>0.400000</td>\n      <td>4</td>\n      <td>6</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.287671</td>\n      <td>2</td>\n      <td>0.533333</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>49</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.150685</td>\n      <td>1</td>\n      <td>0.733333</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&gt;50K</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.369863</td>\n      <td>2</td>\n      <td>0.600000</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.076881</td>\n      <td>0.0</td>\n      <td>39</td>\n      <td>37</td>\n      <td>&gt;50K</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.232877</td>\n      <td>2</td>\n      <td>0.333333</td>\n      <td>4</td>\n      <td>7</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>29</td>\n      <td>37</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        age  workclass  educationno  maritalstatus  occupation  relationship  \\\n0  0.109589          2     0.400000              4           6             3   \n1  0.287671          2     0.533333              2           4             0   \n2  0.150685          1     0.733333              2          10             0   \n3  0.369863          2     0.600000              2           6             0   \n4  0.232877          2     0.333333              4           7             1   \n\n   race  sex  capitalgain  capitalloss  hoursperweek  native  Salary  \\\n0     2    1     0.000000          0.0            39      37   <=50K   \n1     4    1     0.000000          0.0            49      37   <=50K   \n2     4    1     0.000000          0.0            39      37    >50K   \n3     2    1     0.076881          0.0            39      37    >50K   \n4     4    1     0.000000          0.0            29      37   <=50K   \n\n   salary_num  \n0           0  \n1           0  \n2           1  \n3           1  \n4           0  "
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-01-30T17:16:50.743429Z",
          "start_time": "2022-01-30T17:16:50.692464Z"
        },
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Accuracy of predicions\nacc = accuracy_score(test_data['salary_num'], y_pred_test) * 100\nprint(\"Accuracy =\", acc)\n# Confusion matrix\nconfusion_matrix(test_data['salary_num'], y_pred_test)",
      "execution_count": 40,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Accuracy = 80.02123142250531\n"
        },
        {
          "data": {
            "text/plain": "array([[10301,   319],\n       [ 2504,  1006]], dtype=int64)"
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
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