Kumar_Hemjeet_assement_notebook (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:50.449027639Z",
     "start_time": "2023-09-21T16:11:50.126379575Z"
    }
   },
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
    "from sklearn.preprocessing import LabelEncoder,MinMaxScaler,OrdinalEncoder,OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import *\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\n",
    "\n",
    "#----------------columns to select-----------------#\n",
    "column_names = [\n",
    "    \"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\",\n",
    "    \"occupation\", \"relationship\", \"race\", \"sex\", \"capital-gain\", \"capital-loss\",\n",
    "    \"hours-per-week\", \"native-country\", \"income\"\n",
    "]\n",
    "\n",
    "#---------------read data----------------#\n",
    "data = pd.read_csv(url, names=column_names, sep=',\\s*', engine='python')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.213565007Z",
     "start_time": "2023-09-21T16:11:50.140358269Z"
    }
   },
   "id": "e406cd7101bc4845"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "   age         workclass  fnlwgt  education  education-num  \\\n0   39         State-gov   77516  Bachelors             13   \n1   50  Self-emp-not-inc   83311  Bachelors             13   \n2   38           Private  215646    HS-grad              9   \n3   53           Private  234721       11th              7   \n4   28           Private  338409  Bachelors             13   \n\n       marital-status         occupation   relationship   race     sex  \\\n0       Never-married       Adm-clerical  Not-in-family  White    Male   \n1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n\n   capital-gain  capital-loss  hours-per-week native-country income  \n0          2174             0              40  United-States  <=50K  \n1             0             0              13  United-States  <=50K  \n2             0             0              40  United-States  <=50K  \n3             0             0              40  United-States  <=50K  \n4             0             0              40           Cuba  <=50K  ",
      "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>fnlwgt</th>\n      <th>education</th>\n      <th>education-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capital-gain</th>\n      <th>capital-loss</th>\n      <th>hours-per-week</th>\n      <th>native-country</th>\n      <th>income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>77516</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      <th>1</th>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>83311</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      <th>2</th>\n      <td>38</td>\n      <td>Private</td>\n      <td>215646</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      <th>3</th>\n      <td>53</td>\n      <td>Private</td>\n      <td>234721</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      <th>4</th>\n      <td>28</td>\n      <td>Private</td>\n      <td>338409</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>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.214334600Z",
     "start_time": "2023-09-21T16:11:50.705355942Z"
    }
   },
   "id": "e4eae0ea8b7e0283"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(32561, 15)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#--check rows of the data--#\n",
    "data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.214621943Z",
     "start_time": "2023-09-21T16:11:50.705772869Z"
    }
   },
   "id": "2a84723e968699c2"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "age               0\nworkclass         0\nfnlwgt            0\neducation         0\neducation-num     0\nmarital-status    0\noccupation        0\nrelationship      0\nrace              0\nsex               0\ncapital-gain      0\ncapital-loss      0\nhours-per-week    0\nnative-country    0\nincome            0\ndtype: int64"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check all null values\n",
    "data.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.214886062Z",
     "start_time": "2023-09-21T16:11:50.705963940Z"
    }
   },
   "id": "326e108cfe5a477b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "No Null values"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d36ff938ddb13165"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\ncount  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \nmean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \nstd       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \nmin       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \nmax       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n\n       hours-per-week  \ncount    32561.000000  \nmean        40.437456  \nstd         12.347429  \nmin          1.000000  \n25%         40.000000  \n50%         40.000000  \n75%         45.000000  \nmax         99.000000  ",
      "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>fnlwgt</th>\n      <th>education-num</th>\n      <th>capital-gain</th>\n      <th>capital-loss</th>\n      <th>hours-per-week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32561.000000</td>\n      <td>3.256100e+04</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>38.581647</td>\n      <td>1.897784e+05</td>\n      <td>10.080679</td>\n      <td>1077.648844</td>\n      <td>87.303830</td>\n      <td>40.437456</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>13.640433</td>\n      <td>1.055500e+05</td>\n      <td>2.572720</td>\n      <td>7385.292085</td>\n      <td>402.960219</td>\n      <td>12.347429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.000000</td>\n      <td>1.228500e+04</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      <th>25%</th>\n      <td>28.000000</td>\n      <td>1.178270e+05</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      <th>50%</th>\n      <td>37.000000</td>\n      <td>1.783560e+05</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      <th>75%</th>\n      <td>48.000000</td>\n      <td>2.370510e+05</td>\n      <td>12.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>45.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>90.000000</td>\n      <td>1.484705e+06</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>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get  statistics of numerical columns\n",
    "data.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.215279665Z",
     "start_time": "2023-09-21T16:11:50.706147387Z"
    }
   },
   "id": "15787a1559172597"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<=50K    75.919044\n>50K     24.080956\nName: income, dtype: float64"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check tdistribution of income (target) variable\n",
    "100*data['income'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.215493509Z",
     "start_time": "2023-09-21T16:11:50.723872807Z"
    }
   },
   "id": "3dba2149ca963370"
  },
  {
   "cell_type": "markdown",
   "source": [
    "It's a case of class imabalance. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f486de71ec67ba7d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## EDA"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "739b40fdf5e162e"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='age', ylabel='Density'>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#----distribution of age----------#\n",
    "sns.distplot(data['age'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.216599729Z",
     "start_time": "2023-09-21T16:11:50.727098631Z"
    }
   },
   "id": "3562390dcf56e426"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Most of the people belong to a age group of 20 to 60"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "de5e556a2ba65aaa"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## workclass feature"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cdc92ea564f2cad"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "Private             69.703019\nSelf-emp-not-inc     7.803814\nLocal-gov            6.427935\n?                    5.638647\nState-gov            3.986364\nSelf-emp-inc         3.427413\nFederal-gov          2.948312\nWithout-pay          0.042996\nNever-worked         0.021498\nName: workclass, dtype: float64"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of workclass feature\n",
    "100*data['workclass'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.216883174Z",
     "start_time": "2023-09-21T16:11:51.182639960Z"
    }
   },
   "id": "cdbe13e87a9033a"
  },
  {
   "cell_type": "markdown",
   "source": [
    "There seem to \"?\" in the data. It might be due to some error. We need to replace such values with Null values."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5eef4623d0a860d6"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "#replace ? with Null values\n",
    "data['workclass']=data['workclass'].replace('?',np.nan)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.217026565Z",
     "start_time": "2023-09-21T16:11:51.188008180Z"
    }
   },
   "id": "ffa6f06f72cc552f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "we have replaced \"?\" with NaNs lets the distribution again"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "862b14454ec37bf7"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "Private             69.703019\nSelf-emp-not-inc     7.803814\nLocal-gov            6.427935\nnan                  5.638647\nState-gov            3.986364\nSelf-emp-inc         3.427413\nFederal-gov          2.948312\nWithout-pay          0.042996\nNever-worked         0.021498\nName: workclass, dtype: float64"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*data['workclass'].astype(str).value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.217234948Z",
     "start_time": "2023-09-21T16:11:51.205950060Z"
    }
   },
   "id": "d885ba305f586574"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We used str function to check the presence of Null. As we ca we can see the \"?\" has been replace by NaNs. We need to impute it or drop it? If we chose to drop we will be losing around 6% of the data or we can impute with the mode. Let's impute it."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3c820940c27b1f51"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "#-----------------replacing Null with mode-------#\n",
    "data['workclass']=data['workclass'].fillna(data['workclass'].mode()[0])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.217324578Z",
     "start_time": "2023-09-21T16:11:51.216114880Z"
    }
   },
   "id": "26775aeafe0f735"
  },
  {
   "cell_type": "markdown",
   "source": [
    "we have imputed the data let's check the distrbution again"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "384d15be38e6b04c"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "Private             75.341666\nSelf-emp-not-inc     7.803814\nLocal-gov            6.427935\nState-gov            3.986364\nSelf-emp-inc         3.427413\nFederal-gov          2.948312\nWithout-pay          0.042996\nNever-worked         0.021498\nName: workclass, dtype: float64"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*data['workclass'].astype(str).value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.217532831Z",
     "start_time": "2023-09-21T16:11:51.257342554Z"
    }
   },
   "id": "4d17dba0a4536a8"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Seems perfect. However, there are few categories who contribution is less than 1% . Such distribution can create skewness in the data and will make model difficult to generalize. We can condense the categories to balance out the distribution. We can condense the categories into Private and Non-private"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5d7c4028f55e8433"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "#condensing the categories  into\n",
    "data['workclass']=data['workclass'].apply(lambda x:'Non-Private' if x!='Private' else x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.217641406Z",
     "start_time": "2023-09-21T16:11:51.257635899Z"
    }
   },
   "id": "315f7a5d49e9fe0d"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "Private        75.341666\nNon-Private    24.658334\nName: workclass, dtype: float64"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check distribution after condensing\n",
    "100*data['workclass'].astype(str).value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.217921505Z",
     "start_time": "2023-09-21T16:11:51.257769531Z"
    }
   },
   "id": "4f77e21c264df01a"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We have condesned the workclass into Private and Non-private to reduce our skewness. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6330270e2be360fe"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## fnlwgt feature"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6dc78a36e0f67187"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='fnlwgt', ylabel='Density'>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fnlwgt variable. \n",
    "sns.distplot(data['fnlwgt'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.218860299Z",
     "start_time": "2023-09-21T16:11:51.301384053Z"
    }
   },
   "id": "f00f1e5fff32d785"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The data of this conitnuous seems a little skewed. Let's deep dive check the outliers"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "40c380c95949cfd"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "count    3.256100e+04\nmean     1.897784e+05\nstd      1.055500e+05\nmin      1.228500e+04\n25%      1.178270e+05\n50%      1.783560e+05\n75%      2.370510e+05\nmax      1.484705e+06\nName: fnlwgt, dtype: float64"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the stats of the feature\n",
    "data['fnlwgt'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.219104931Z",
     "start_time": "2023-09-21T16:11:51.725270219Z"
    }
   },
   "id": "90d1209dc5325cdb"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Maximum values of the feature seems to be little out of range. Let's see how many such values are there. Let's plot the box plot"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6fad39b8d95586e6"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#----box plot----#\n",
    "sns.boxplot(data['fnlwgt'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.219389528Z",
     "start_time": "2023-09-21T16:11:51.725496145Z"
    }
   },
   "id": "41e642765d060f73"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Only one variable is lying in e+06 range. We can remove that value or we can use upto 99 percentile of feature"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a7a481df02097598"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "510072.0"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check 99 percentile of the feature\n",
    "data['fnlwgt'].quantile(0.99)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.219629051Z",
     "start_time": "2023-09-21T16:11:51.791589558Z"
    }
   },
   "id": "541eb9f81bde27b3"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can use the values as we will also retain the 99% of the data. We are removing top 1% of the data"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2b780d502c13b343"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "#---removing the max value from the feature-------#\n",
    "data=data[data['fnlwgt']<=510072.0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.219718550Z",
     "start_time": "2023-09-21T16:11:51.833288612Z"
    }
   },
   "id": "3f21ea4198e6f020"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "count     32236.00000\nmean     185289.50760\nstd       94900.04477\nmin       12285.00000\n25%      117500.50000\n50%      177473.50000\n75%      234634.75000\nmax      510072.00000\nName: fnlwgt, dtype: float64"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fnlwgt'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.219967720Z",
     "start_time": "2023-09-21T16:11:51.833487097Z"
    }
   },
   "id": "fbda119a5cc024c1"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's check the distribution now"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "eb4c8f3d3cb127b6"
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='fnlwgt', ylabel='Density'>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['fnlwgt'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.220434883Z",
     "start_time": "2023-09-21T16:11:51.833689479Z"
    }
   },
   "id": "8b1f9fbf3b82e0e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now distribution is somewhat balanced now"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "90c3a4903df2d207"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Education feature"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a06e435c9200f50c"
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "HS-grad         32.249659\nSome-college    22.403524\nBachelors       16.431939\nMasters          5.292220\nAssoc-voc        4.265418\n11th             3.607768\nAssoc-acdm       3.297556\n10th             2.850850\n7th-8th          1.988460\nProf-school      1.780618\n9th              1.569674\n12th             1.312198\nDoctorate        1.274972\n5th-6th          1.014394\n1st-4th          0.508748\nPreschool        0.152004\nName: education, dtype: float64"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of education feature\n",
    "100*data['education'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.220668254Z",
     "start_time": "2023-09-21T16:11:52.105272223Z"
    }
   },
   "id": "c2f1f0af5d5a0cca"
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['HS-grad', 'Some-college', 'Bachelors', 'Masters', 'Assoc-voc', '11th',\n       'Assoc-acdm', '10th', '7th-8th', 'Prof-school', '9th', '12th',\n       'Doctorate', '5th-6th', '1st-4th', 'Preschool'],\n      dtype='object')"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['education'].value_counts(normalize=True).keys()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.220881426Z",
     "start_time": "2023-09-21T16:11:52.105526574Z"
    }
   },
   "id": "c7a4504be5e211e9"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The distribution isn't uniform here. Few categories in the feature have even less than 5% of distribution. This won't do good for out model. Let's see if we can condense the categories. We can condense the categories having less than 5% contribution into category \"other-education\""
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2dde3d2d7690c8d6"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "data['education']=data['education'].apply(lambda x:'other-education' if x in ['Assoc-voc', '11th', 'Assoc-acdm', '10th', '7th-8th', 'Prof-school', '9th', '12th',\n",
    "                                                                              'Doctorate', '5th-6th', '1st-4th', 'Preschool'] else x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.220981535Z",
     "start_time": "2023-09-21T16:11:52.105694471Z"
    }
   },
   "id": "eb1fe610828be47f"
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "HS-grad            32.249659\nother-education    23.622658\nSome-college       22.403524\nBachelors          16.431939\nMasters             5.292220\nName: education, dtype: float64"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*data['education'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.221255272Z",
     "start_time": "2023-09-21T16:11:52.105818645Z"
    }
   },
   "id": "e68c576f1f3442f5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Education Num"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5c10bc99c991ec53"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='education-num', ylabel='Density'>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of the feature education-num\n",
    "sns.distplot(data['education-num'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.221599683Z",
     "start_time": "2023-09-21T16:11:52.111791808Z"
    }
   },
   "id": "ecaac51180488524"
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "count    32236.000000\nmean        10.084595\nstd          2.570455\nmin          1.000000\n25%          9.000000\n50%         10.000000\n75%         12.000000\nmax         16.000000\nName: education-num, dtype: float64"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#basic stats of the feature\n",
    "data['education-num'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.221864884Z",
     "start_time": "2023-09-21T16:11:52.410583535Z"
    }
   },
   "id": "5884a8a23e7e6079"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The distribution seems okay and can be used as continuous variable. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2849e8ca64754790"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Marital status"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "906fae06b3713fe2"
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "Married-civ-spouse       46.032386\nNever-married            32.777019\nDivorced                 13.661745\nSeparated                 3.123837\nWidowed                   3.058692\nMarried-spouse-absent     1.278074\nMarried-AF-spouse         0.068247\nName: marital-status, dtype: float64"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of the marital-status feature\n",
    "100*data['marital-status'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.222153940Z",
     "start_time": "2023-09-21T16:11:52.453381544Z"
    }
   },
   "id": "6a45c7d91797f07a"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The distribution here looks un even and we can furthure condense it to balance. We can condense it into 'Married-civ-spouse', 'Never-married', 'Divorced' and others"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ed30eab4e4c5efb9"
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "#condensing into the discussed categories\n",
    "data['marital-status']=data['marital-status'].apply(lambda x:'others' if x in ['Separated',\n",
    "                                                                               'Widowed', 'Married-spouse-absent', 'Married-AF-spouse'] else x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.222251845Z",
     "start_time": "2023-09-21T16:11:52.453624303Z"
    }
   },
   "id": "ade9574fd3aff256"
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "Married-civ-spouse    46.032386\nNever-married         32.777019\nDivorced              13.661745\nothers                 7.528850\nName: marital-status, dtype: float64"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*data['marital-status'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.222506386Z",
     "start_time": "2023-09-21T16:11:52.453764888Z"
    }
   },
   "id": "bdaecbeace245fa8"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, the distribution looks somewhat even"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ccd4b3ea905a44e3"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Occupation"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d09dac3535ed4d97"
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "Prof-specialty       12.755925\nCraft-repair         12.563593\nExec-managerial      12.504653\nAdm-clerical         11.558506\nSales                11.235885\nOther-service        10.112917\nMachine-op-inspct     6.163916\n?                     5.667577\nTransport-moving      4.885842\nHandlers-cleaners     4.184762\nFarming-fishing       3.046284\nTech-support          2.844646\nProtective-serv       1.991562\nPriv-house-serv       0.456012\nArmed-Forces          0.027919\nName: occupation, dtype: float64"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of occupation\n",
    "100*data['occupation'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.222877026Z",
     "start_time": "2023-09-21T16:11:52.453926263Z"
    }
   },
   "id": "d05e6174da1a197e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We need to replace \"?\" with NaN. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e161fc7b5c24fed0"
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "#replacing ? with NaN\n",
    "data['occupation']=data['occupation'].replace(\"?\",np.nan)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.223032730Z",
     "start_time": "2023-09-21T16:11:52.497272999Z"
    }
   },
   "id": "e6a20de34b57cc15"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We need to impute NaN furthure. We can impute with Mode."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6cfe11af89e44c55"
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "#replacing NaN with the mode of the feature\n",
    "data['occupation']=data['occupation'].fillna(data['occupation'].mode()[0])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.223178225Z",
     "start_time": "2023-09-21T16:11:52.497573647Z"
    }
   },
   "id": "d9b7b2262ad09e46"
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "Prof-specialty       18.423502\nCraft-repair         12.563593\nExec-managerial      12.504653\nAdm-clerical         11.558506\nSales                11.235885\nOther-service        10.112917\nMachine-op-inspct     6.163916\nTransport-moving      4.885842\nHandlers-cleaners     4.184762\nFarming-fishing       3.046284\nTech-support          2.844646\nProtective-serv       1.991562\nPriv-house-serv       0.456012\nArmed-Forces          0.027919\nName: occupation, dtype: float64"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution after imputation\n",
    "100*data['occupation'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.223627684Z",
     "start_time": "2023-09-21T16:11:52.497740672Z"
    }
   },
   "id": "88689c15913ec50e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can futhure condense the category into already exieting category i.e.Other-service."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "83ff84b6dffd114c"
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "#condensing the categories\n",
    "data['occupation']=data['occupation'].apply(lambda x:'Other-service' if x in ['Machine-op-inspct', 'Transport-moving',\n",
    "                                                                              'Handlers-cleaners', 'Farming-fishing','Tech-support',\n",
    "                                                                              'Protective-serv', 'Priv-house-serv', 'Armed-Forces'] else x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.223783207Z",
     "start_time": "2023-09-21T16:11:52.497947663Z"
    }
   },
   "id": "97730d204fde4013"
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "Other-service      33.713860\nProf-specialty     18.423502\nCraft-repair       12.563593\nExec-managerial    12.504653\nAdm-clerical       11.558506\nSales              11.235885\nName: occupation, dtype: float64"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distrbution after condensing\n",
    "100*data['occupation'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.224028290Z",
     "start_time": "2023-09-21T16:11:52.498121342Z"
    }
   },
   "id": "5b6fa9cb18c04c8e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Relationship"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b411a71aafeaa5b2"
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "Husband           40.544733\nNot-in-family     25.511850\nOwn-child         15.554039\nUnmarried         10.559623\nWife               4.823799\nOther-relative     3.005956\nName: relationship, dtype: float64"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of relationship feature\n",
    "100*data['relationship'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.225118650Z",
     "start_time": "2023-09-21T16:11:52.498399537Z"
    }
   },
   "id": "8034e13392ad5c32"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can furthure condense the categories"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "fbbe0c051f79619a"
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "#condensing the categories\n",
    "data['relationship']=data['relationship'].apply(lambda x: 'others' if x in ['Wife',\n",
    "                                                                            'Other-relative'] else x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.225253695Z",
     "start_time": "2023-09-21T16:11:52.506018358Z"
    }
   },
   "id": "bad3c2af8b2b5d6e"
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "Husband          40.544733\nNot-in-family    25.511850\nOwn-child        15.554039\nUnmarried        10.559623\nothers            7.829756\nName: relationship, dtype: float64"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution after condensing\n",
    "100*data['relationship'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.225473701Z",
     "start_time": "2023-09-21T16:11:52.553326276Z"
    }
   },
   "id": "bd71a3bb6f02db28"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The distribution looks better now"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c02329b48f4d6246"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## race"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "728be35cd3232e50"
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "White                 85.572031\nBlack                  9.399429\nAsian-Pac-Islander     3.223105\nAmer-Indian-Eskimo     0.964760\nOther                  0.840675\nName: race, dtype: float64"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of race feature\n",
    "100*data['race'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.225714706Z",
     "start_time": "2023-09-21T16:11:52.553545410Z"
    }
   },
   "id": "cccad6aaf704c13c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We need to condense the categories as this is very imbalanced one and can cause overfitting issues in the model. We will condense it into two categories White and Non-white"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "355eff2721f91537"
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "#condensing the categories \n",
    "data['race']=data['race'].apply(lambda x:'Non-White' if x!='White' else x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.225813142Z",
     "start_time": "2023-09-21T16:11:52.553771147Z"
    }
   },
   "id": "e2b5698df22fad02"
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "White        85.572031\nNon-White    14.427969\nName: race, dtype: float64"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution after condensing\n",
    "100*data['race'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.226106696Z",
     "start_time": "2023-09-21T16:11:52.553879211Z"
    }
   },
   "id": "7b2dd1a82d57e3e2"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Sex"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3e49835eecc7755f"
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "Male      66.891053\nFemale    33.108947\nName: sex, dtype: float64"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of sex feature\n",
    "100*data['sex'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.226344926Z",
     "start_time": "2023-09-21T16:11:52.553997544Z"
    }
   },
   "id": "37a556c799e98d35"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The distribution looks fine"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "81f81ec00710d034"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## native country"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8235238cc70526c0"
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "United-States                 89.654424\nMexico                         1.923316\n?                              1.786822\nPhilippines                    0.611118\nGermany                        0.421889\nCanada                         0.375357\nPuerto-Rico                    0.347438\nIndia                          0.310212\nEl-Salvador                    0.307110\nCuba                           0.294702\nEngland                        0.279191\nJamaica                        0.251272\nSouth                          0.248170\nChina                          0.232659\nItaly                          0.223353\nDominican-Republic             0.217149\nVietnam                        0.207842\nGuatemala                      0.189229\nJapan                          0.186127\nPoland                         0.186127\nColumbia                       0.176821\nTaiwan                         0.158208\nHaiti                          0.136493\nIran                           0.133391\nPortugal                       0.114779\nPeru                           0.093064\nNicaragua                      0.093064\nFrance                         0.089962\nGreece                         0.089962\nEcuador                        0.086859\nIreland                        0.074451\nHong                           0.062042\nCambodia                       0.058940\nLaos                           0.055838\nThailand                       0.055838\nTrinadad&Tobago                0.055838\nYugoslavia                     0.046532\nOutlying-US(Guam-USVI-etc)     0.043430\nHonduras                       0.040328\nHungary                        0.040328\nScotland                       0.037225\nHoland-Netherlands             0.003102\nName: native-country, dtype: float64"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of native-country feature\n",
    "100*data['native-country'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.226646576Z",
     "start_time": "2023-09-21T16:11:52.597396509Z"
    }
   },
   "id": "b7e26498e0eed3ea"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Most of the people in th data belong to US. We can condense the category into US and other-country. We also have \"?\" in the data. We need to replace the value with NaN and impute it."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8ac945a39f22e5b8"
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "#replacing ? with NaN\n",
    "data['native-country']=data['native-country'].replace(\"?\",np.nan)\n",
    "\n",
    "#imputing Null with mode of the feature\n",
    "data['native-country']=data['native-country'].fillna(data['native-country'].mode()[0])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.226759279Z",
     "start_time": "2023-09-21T16:11:52.597683852Z"
    }
   },
   "id": "ff4f2325d935264"
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "United-States                 91.441246\nMexico                         1.923316\nPhilippines                    0.611118\nGermany                        0.421889\nCanada                         0.375357\nPuerto-Rico                    0.347438\nIndia                          0.310212\nEl-Salvador                    0.307110\nCuba                           0.294702\nEngland                        0.279191\nJamaica                        0.251272\nSouth                          0.248170\nChina                          0.232659\nItaly                          0.223353\nDominican-Republic             0.217149\nVietnam                        0.207842\nGuatemala                      0.189229\nJapan                          0.186127\nPoland                         0.186127\nColumbia                       0.176821\nTaiwan                         0.158208\nHaiti                          0.136493\nIran                           0.133391\nPortugal                       0.114779\nPeru                           0.093064\nNicaragua                      0.093064\nFrance                         0.089962\nGreece                         0.089962\nEcuador                        0.086859\nIreland                        0.074451\nHong                           0.062042\nCambodia                       0.058940\nLaos                           0.055838\nThailand                       0.055838\nTrinadad&Tobago                0.055838\nYugoslavia                     0.046532\nOutlying-US(Guam-USVI-etc)     0.043430\nHonduras                       0.040328\nHungary                        0.040328\nScotland                       0.037225\nHoland-Netherlands             0.003102\nName: native-country, dtype: float64"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution after imputation\n",
    "100*data['native-country'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.227071909Z",
     "start_time": "2023-09-21T16:11:52.597930267Z"
    }
   },
   "id": "459aaeaa90530653"
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "#condense the category into us and other-country\n",
    "data['native-country']=data['native-country'].apply(lambda x:'other-countries' if x!='United-States' else x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.227165687Z",
     "start_time": "2023-09-21T16:11:52.598272955Z"
    }
   },
   "id": "dcaf02dab97d696f"
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "United-States      91.441246\nother-countries     8.558754\nName: native-country, dtype: float64"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution after condensing\n",
    "100*data['native-country'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.227378969Z",
     "start_time": "2023-09-21T16:11:52.598764052Z"
    }
   },
   "id": "2ff643f99e6df90b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The distribution looks great now. But since this feature has 91% people from US only this will create imabalance problem in data and we won't be considering this feature for our model building as the distribution is highly skewed"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c4270d0b0a8f6595"
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "#drop native country column\n",
    "data.drop('native-country',axis=1,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.227460513Z",
     "start_time": "2023-09-21T16:11:52.599035525Z"
    }
   },
   "id": "79438513f35413c5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## hours per week"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7a05b14de9f26ff1"
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot distribution and box plots\n",
    "fig,ax=plt.subplots(1,2)\n",
    "sns.distplot(data['hours-per-week'],ax=ax[0])\n",
    "sns.boxplot(data['hours-per-week'],ax=ax[1])\n",
    "plt.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.227792270Z",
     "start_time": "2023-09-21T16:11:52.641376842Z"
    }
   },
   "id": "71b63d9916433db8"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The distribution looks closer to gaussian. Let's see if we have any outliers"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "21997b4918f5d092"
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "count    32236.000000\nmean        40.442921\nstd         12.355288\nmin          1.000000\n25%         40.000000\n50%         40.000000\n75%         45.000000\nmax         99.000000\nName: hours-per-week, dtype: float64"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['hours-per-week'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.228079152Z",
     "start_time": "2023-09-21T16:11:53.021262875Z"
    }
   },
   "id": "dafae4c7a846ed0d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Distribution looks good and we are good to go"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bef334dcf8568f4e"
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "#Dropping capital-gain capital-loss as most of the values are zero.\n",
    "data.drop(['capital-gain','capital-loss'],axis=1,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.228172649Z",
     "start_time": "2023-09-21T16:11:53.026318614Z"
    }
   },
   "id": "62242f2baf33fce4"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Preprocessing"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c3114047cb72fdac"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**In this section we will do preprocessing of the data. We will first split the data into train and test. We will use 80-20 split**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c0af9d8b470fa30b"
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [],
   "source": [
    "#lets encode target variable to 0 and 1\n",
    "data['income']=data['income'].map({'<=50K':0,\">50K\":1})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.228250486Z",
     "start_time": "2023-09-21T16:11:53.036581540Z"
    }
   },
   "id": "66ecb6a11cd98a16"
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "#declare dependent and independent variables\n",
    "X=data.drop('income',axis=1)\n",
    "y=data['income']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.228348160Z",
     "start_time": "2023-09-21T16:11:53.043283038Z"
    }
   },
   "id": "9a4eb91c4146ef09"
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "#split the data into train and test\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2,stratify=y,random_state=42)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.228449271Z",
     "start_time": "2023-09-21T16:11:53.085285614Z"
    }
   },
   "id": "6aad222f2c61e497"
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "       age    workclass  fnlwgt  education  education-num      marital-status  \\\n5381    26      Private   58065  Bachelors             13       Never-married   \n17182   57      Private  206343    HS-grad              9              others   \n16517   31      Private  198069    HS-grad              9       Never-married   \n2117    45  Non-Private  135776    Masters             14            Divorced   \n2579    47      Private  188330    HS-grad              9  Married-civ-spouse   \n\n           occupation   relationship   race     sex  hours-per-week  \n5381     Adm-clerical  Not-in-family  White  Female              20  \n17182   Other-service  Not-in-family  White    Male              40  \n16517   Other-service      Own-child  White    Male              24  \n2117   Prof-specialty  Not-in-family  White  Female              30  \n2579    Other-service        Husband  White    Male              25  ",
      "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>fnlwgt</th>\n      <th>education</th>\n      <th>education-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>hours-per-week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5381</th>\n      <td>26</td>\n      <td>Private</td>\n      <td>58065</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>Female</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>17182</th>\n      <td>57</td>\n      <td>Private</td>\n      <td>206343</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>others</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>16517</th>\n      <td>31</td>\n      <td>Private</td>\n      <td>198069</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Never-married</td>\n      <td>Other-service</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>2117</th>\n      <td>45</td>\n      <td>Non-Private</td>\n      <td>135776</td>\n      <td>Masters</td>\n      <td>14</td>\n      <td>Divorced</td>\n      <td>Prof-specialty</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>2579</th>\n      <td>47</td>\n      <td>Private</td>\n      <td>188330</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Other-service</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.228984923Z",
     "start_time": "2023-09-21T16:11:53.085509728Z"
    }
   },
   "id": "212e14620d27ccb"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We will ordinal encode the education variable and th rest categorical variable will be encoded as One-hot"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "39bf8a065c0789dc"
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [
    "#ordinal encodng of education feature\n",
    "ordinal=OrdinalEncoder(categories=[['other-education', 'Some-college','HS-grad','Bachelors','Masters']])\n",
    "x_train['education']=ordinal.fit_transform(x_train[['education']])\n",
    "x_test['education']=ordinal.transform(x_test[['education']])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.229173239Z",
     "start_time": "2023-09-21T16:11:53.085787512Z"
    }
   },
   "id": "ce05514038b4ac86"
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "       age    workclass  fnlwgt  education  education-num      marital-status  \\\n5381    26      Private   58065        3.0             13       Never-married   \n17182   57      Private  206343        2.0              9              others   \n16517   31      Private  198069        2.0              9       Never-married   \n2117    45  Non-Private  135776        4.0             14            Divorced   \n2579    47      Private  188330        2.0              9  Married-civ-spouse   \n\n           occupation   relationship   race     sex  hours-per-week  \n5381     Adm-clerical  Not-in-family  White  Female              20  \n17182   Other-service  Not-in-family  White    Male              40  \n16517   Other-service      Own-child  White    Male              24  \n2117   Prof-specialty  Not-in-family  White  Female              30  \n2579    Other-service        Husband  White    Male              25  ",
      "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>fnlwgt</th>\n      <th>education</th>\n      <th>education-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>hours-per-week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5381</th>\n      <td>26</td>\n      <td>Private</td>\n      <td>58065</td>\n      <td>3.0</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>Female</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>17182</th>\n      <td>57</td>\n      <td>Private</td>\n      <td>206343</td>\n      <td>2.0</td>\n      <td>9</td>\n      <td>others</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>16517</th>\n      <td>31</td>\n      <td>Private</td>\n      <td>198069</td>\n      <td>2.0</td>\n      <td>9</td>\n      <td>Never-married</td>\n      <td>Other-service</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>2117</th>\n      <td>45</td>\n      <td>Non-Private</td>\n      <td>135776</td>\n      <td>4.0</td>\n      <td>14</td>\n      <td>Divorced</td>\n      <td>Prof-specialty</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>2579</th>\n      <td>47</td>\n      <td>Private</td>\n      <td>188330</td>\n      <td>2.0</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Other-service</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.229637135Z",
     "start_time": "2023-09-21T16:11:53.129287167Z"
    }
   },
   "id": "1b4bf9af857cc4e0"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can do label encoding for private class they are only two categoris"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "211ce397a55d6cc5"
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [
    "#converting working class and sex feature into binary\n",
    "label=LabelEncoder()\n",
    "cat=['workclass','sex']\n",
    "for i in cat:\n",
    "    x_train[i]=label.fit_transform(x_train[i])\n",
    "    x_test[i]=label.transform(x_test[i])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.229743025Z",
     "start_time": "2023-09-21T16:11:53.129548451Z"
    }
   },
   "id": "ee6c6598df32b0b7"
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "       age  workclass  fnlwgt  education  education-num      marital-status  \\\n5381    26          1   58065        3.0             13       Never-married   \n17182   57          1  206343        2.0              9              others   \n16517   31          1  198069        2.0              9       Never-married   \n2117    45          0  135776        4.0             14            Divorced   \n2579    47          1  188330        2.0              9  Married-civ-spouse   \n\n           occupation   relationship   race  sex  hours-per-week  \n5381     Adm-clerical  Not-in-family  White    0              20  \n17182   Other-service  Not-in-family  White    1              40  \n16517   Other-service      Own-child  White    1              24  \n2117   Prof-specialty  Not-in-family  White    0              30  \n2579    Other-service        Husband  White    1              25  ",
      "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>fnlwgt</th>\n      <th>education</th>\n      <th>education-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>hours-per-week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5381</th>\n      <td>26</td>\n      <td>1</td>\n      <td>58065</td>\n      <td>3.0</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>0</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>17182</th>\n      <td>57</td>\n      <td>1</td>\n      <td>206343</td>\n      <td>2.0</td>\n      <td>9</td>\n      <td>others</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>1</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>16517</th>\n      <td>31</td>\n      <td>1</td>\n      <td>198069</td>\n      <td>2.0</td>\n      <td>9</td>\n      <td>Never-married</td>\n      <td>Other-service</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>1</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>2117</th>\n      <td>45</td>\n      <td>0</td>\n      <td>135776</td>\n      <td>4.0</td>\n      <td>14</td>\n      <td>Divorced</td>\n      <td>Prof-specialty</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>0</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>2579</th>\n      <td>47</td>\n      <td>1</td>\n      <td>188330</td>\n      <td>2.0</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Other-service</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>1</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.230283125Z",
     "start_time": "2023-09-21T16:11:53.129684858Z"
    }
   },
   "id": "d04ae8d5eee27e3d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "One-Hot encoding the rest of the categorical columns"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a725f64b4e0f0a3c"
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [],
   "source": [
    "#categorical and numerical columns\n",
    "cat_cols=['marital-status','occupation','relationship','race']\n",
    "num_cols=['age','fnlwgt','education','education-num','hours-per-week']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.230378625Z",
     "start_time": "2023-09-21T16:11:53.129840943Z"
    }
   },
   "id": "23a9f39e465c50a4"
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "#create a columns Transformer\n",
    "ct=ColumnTransformer([\n",
    "    ('On-hot-encode',OneHotEncoder(handle_unknown='ignore'),cat_cols),\n",
    "    ('scaling',MinMaxScaler(),num_cols)\n",
    "])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.230454739Z",
     "start_time": "2023-09-21T16:11:53.130035641Z"
    }
   },
   "id": "307471f5b63901ff"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Logistic Regression"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c52bde28fc7eb78"
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [],
   "source": [
    "#create a Pipeline and build a model\n",
    "pipeline=Pipeline([\n",
    "    ('ct',ct),\n",
    "    ('model',LogisticRegression())\n",
    "])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:53.230539900Z",
     "start_time": "2023-09-21T16:11:53.130135600Z"
    }
   },
   "id": "e62725b89d2203b6"
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "data": {
      "text/plain": "Pipeline(steps=[('ct',\n                 ColumnTransformer(transformers=[('On-hot-encode',\n                                                  OneHotEncoder(handle_unknown='ignore'),\n                                                  ['marital-status',\n                                                   'occupation', 'relationship',\n                                                   'race']),\n                                                 ('scaling', MinMaxScaler(),\n                                                  ['age', 'fnlwgt', 'education',\n                                                   'education-num',\n                                                   'hours-per-week'])])),\n                ('model', LogisticRegression())])",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                                  [&#x27;marital-status&#x27;,\n                                                   &#x27;occupation&#x27;, &#x27;relationship&#x27;,\n                                                   &#x27;race&#x27;]),\n                                                 (&#x27;scaling&#x27;, MinMaxScaler(),\n                                                  [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;,\n                                                   &#x27;education-num&#x27;,\n                                                   &#x27;hours-per-week&#x27;])])),\n                (&#x27;model&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                                  [&#x27;marital-status&#x27;,\n                                                   &#x27;occupation&#x27;, &#x27;relationship&#x27;,\n                                                   &#x27;race&#x27;]),\n                                                 (&#x27;scaling&#x27;, MinMaxScaler(),\n                                                  [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;,\n                                                   &#x27;education-num&#x27;,\n                                                   &#x27;hours-per-week&#x27;])])),\n                (&#x27;model&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                 [&#x27;marital-status&#x27;, &#x27;occupation&#x27;,\n                                  &#x27;relationship&#x27;, &#x27;race&#x27;]),\n                                (&#x27;scaling&#x27;, MinMaxScaler(),\n                                 [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;, &#x27;education-num&#x27;,\n                                  &#x27;hours-per-week&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">On-hot-encode</label><div class=\"sk-toggleable__content\"><pre>[&#x27;marital-status&#x27;, &#x27;occupation&#x27;, &#x27;relationship&#x27;, &#x27;race&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaling</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;, &#x27;education-num&#x27;, &#x27;hours-per-week&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.373249551Z",
     "start_time": "2023-09-21T16:11:53.130234306Z"
    }
   },
   "id": "9cb1b255a3141ba3"
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [],
   "source": [
    "#create a function to evaluate model\n",
    "def evaluate(model,data,y_true):\n",
    "    \"\"\"\n",
    "    :param model: trained model\n",
    "    :param data: test data\n",
    "    :param y_true: actual y\n",
    "    :return: accuracy,classification_report and confusion matrix\n",
    "    \"\"\"\n",
    "    y_pred=model.predict(data)\n",
    "    accuracy=accuracy_score(y_true,y_pred)\n",
    "    report=classification_report(y_true,y_pred)\n",
    "    cnf_matrix=confusion_matrix(y_true,y_pred)\n",
    "    #train accuracy\n",
    "    print('-----------o-------------')\n",
    "    print('Accuracy:',accuracy*100)\n",
    "    print('------------o------------')\n",
    "    print('Classfication report:\\n', report)\n",
    "    print('------------o------------')\n",
    "    print('Logistic regression Confusion Matrix:\\n',cnf_matrix)\n",
    "    print('----------------o--------------')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.915563352Z",
     "start_time": "2023-09-21T16:11:53.231400866Z"
    }
   },
   "id": "a7bba297ed87615a"
  },
  {
   "cell_type": "markdown",
   "source": [
    "As this is a class imbalance problem. We should look at the f1-score for better understanding."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "73d5216201ddc359"
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------o-------------\n",
      "Accuracy: 82.76330076004344\n",
      "------------o------------\n",
      "Classfication report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.92      0.89     19575\n",
      "           1       0.69      0.52      0.59      6213\n",
      "\n",
      "    accuracy                           0.83     25788\n",
      "   macro avg       0.77      0.72      0.74     25788\n",
      "weighted avg       0.82      0.83      0.82     25788\n",
      "\n",
      "------------o------------\n",
      "Logistic regression Confusion Matrix:\n",
      " [[18105  1470]\n",
      " [ 2975  3238]]\n",
      "----------------o--------------\n"
     ]
    }
   ],
   "source": [
    "#training score\n",
    "evaluate(pipeline,x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.915897934Z",
     "start_time": "2023-09-21T16:11:53.277257002Z"
    }
   },
   "id": "24a0dbd85c2a3fdc"
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------o-------------\n",
      "Accuracy: 81.94789081885855\n",
      "------------o------------\n",
      "Classfication report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.91      0.88      4894\n",
      "           1       0.66      0.52      0.58      1554\n",
      "\n",
      "    accuracy                           0.82      6448\n",
      "   macro avg       0.76      0.72      0.73      6448\n",
      "weighted avg       0.81      0.82      0.81      6448\n",
      "\n",
      "------------o------------\n",
      "Logistic regression Confusion Matrix:\n",
      " [[4471  423]\n",
      " [ 741  813]]\n",
      "----------------o--------------\n"
     ]
    }
   ],
   "source": [
    "#testing scores\n",
    "evaluate(pipeline,x_test,y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.916043308Z",
     "start_time": "2023-09-21T16:11:53.321358423Z"
    }
   },
   "id": "adfd287b2fec3de6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "* We can also see that model isn't overfitting. This is a case of Class imabalnce problem , so we need to adjust the weights of our class"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "39cb9f53788e64b2"
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "Pipeline(steps=[('ct',\n                 ColumnTransformer(transformers=[('On-hot-encode',\n                                                  OneHotEncoder(handle_unknown='ignore'),\n                                                  ['marital-status',\n                                                   'occupation', 'relationship',\n                                                   'race']),\n                                                 ('scaling', MinMaxScaler(),\n                                                  ['age', 'fnlwgt', 'education',\n                                                   'education-num',\n                                                   'hours-per-week'])])),\n                ('model', LogisticRegression(class_weight={0: 1, 1: 1.5}))])",
      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                                  [&#x27;marital-status&#x27;,\n                                                   &#x27;occupation&#x27;, &#x27;relationship&#x27;,\n                                                   &#x27;race&#x27;]),\n                                                 (&#x27;scaling&#x27;, MinMaxScaler(),\n                                                  [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;,\n                                                   &#x27;education-num&#x27;,\n                                                   &#x27;hours-per-week&#x27;])])),\n                (&#x27;model&#x27;, LogisticRegression(class_weight={0: 1, 1: 1.5}))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                                  [&#x27;marital-status&#x27;,\n                                                   &#x27;occupation&#x27;, &#x27;relationship&#x27;,\n                                                   &#x27;race&#x27;]),\n                                                 (&#x27;scaling&#x27;, MinMaxScaler(),\n                                                  [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;,\n                                                   &#x27;education-num&#x27;,\n                                                   &#x27;hours-per-week&#x27;])])),\n                (&#x27;model&#x27;, LogisticRegression(class_weight={0: 1, 1: 1.5}))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                 [&#x27;marital-status&#x27;, &#x27;occupation&#x27;,\n                                  &#x27;relationship&#x27;, &#x27;race&#x27;]),\n                                (&#x27;scaling&#x27;, MinMaxScaler(),\n                                 [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;, &#x27;education-num&#x27;,\n                                  &#x27;hours-per-week&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">On-hot-encode</label><div class=\"sk-toggleable__content\"><pre>[&#x27;marital-status&#x27;, &#x27;occupation&#x27;, &#x27;relationship&#x27;, &#x27;race&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaling</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;, &#x27;education-num&#x27;, &#x27;hours-per-week&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(class_weight={0: 1, 1: 1.5})</pre></div></div></div></div></div></div></div>"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a Pipeline and build a model\n",
    "pipeline_wt_balance=Pipeline([\n",
    "    ('ct',ct),\n",
    "    ('model',LogisticRegression(class_weight={0:1,1:1.5}))\n",
    "])\n",
    "pipeline_wt_balance.fit(x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.916637811Z",
     "start_time": "2023-09-21T16:11:53.365186649Z"
    }
   },
   "id": "abdf8716733a48ae"
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------o-------------\n",
      "Accuracy: 82.37164572669458\n",
      "------------o------------\n",
      "Classfication report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.88      0.88     19575\n",
      "           1       0.63      0.64      0.64      6213\n",
      "\n",
      "    accuracy                           0.82     25788\n",
      "   macro avg       0.76      0.76      0.76     25788\n",
      "weighted avg       0.82      0.82      0.82     25788\n",
      "\n",
      "------------o------------\n",
      "Logistic regression Confusion Matrix:\n",
      " [[17262  2313]\n",
      " [ 2233  3980]]\n",
      "----------------o--------------\n"
     ]
    }
   ],
   "source": [
    "#training score\n",
    "evaluate(pipeline_wt_balance,x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.916806400Z",
     "start_time": "2023-09-21T16:11:53.477237537Z"
    }
   },
   "id": "c671146db2e3bbc9"
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------o-------------\n",
      "Accuracy: 81.42059553349877\n",
      "------------o------------\n",
      "Classfication report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.88      4894\n",
      "           1       0.61      0.64      0.63      1554\n",
      "\n",
      "    accuracy                           0.81      6448\n",
      "   macro avg       0.75      0.76      0.75      6448\n",
      "weighted avg       0.82      0.81      0.82      6448\n",
      "\n",
      "------------o------------\n",
      "Logistic regression Confusion Matrix:\n",
      " [[4249  645]\n",
      " [ 553 1001]]\n",
      "----------------o--------------\n"
     ]
    }
   ],
   "source": [
    "#test score\n",
    "evaluate(pipeline_wt_balance,x_test,y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.916961553Z",
     "start_time": "2023-09-21T16:11:53.496880309Z"
    }
   },
   "id": "b6cfa7e8755093"
  },
  {
   "cell_type": "markdown",
   "source": [
    "*Adjusting our weights has improved the f1-score.*"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3437866fcd44f4b6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## XGBoost"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4968699693c2a530"
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "Pipeline(steps=[('ct',\n                 ColumnTransformer(transformers=[('On-hot-encode',\n                                                  OneHotEncoder(handle_unknown='ignore'),\n                                                  ['marital-status',\n                                                   'occupation', 'relationship',\n                                                   'race']),\n                                                 ('scaling', MinMaxScaler(),\n                                                  ['age', 'fnlwgt', 'education',\n                                                   'education-num',\n                                                   'hours-per-week'])])),\n                ('model',\n                 XGBClassifier(base_score=None, booster=None, callbacks=None,\n                               class_wieght={0: 1, 1: 1.5},\n                               c...\n                               feature_types=None, gamma=None, grow_policy=None,\n                               importance_type=None,\n                               interaction_constraints=None, learning_rate=None,\n                               max_bin=None, max_cat_threshold=None,\n                               max_cat_to_onehot=None, max_delta_step=None,\n                               max_depth=2, max_leaves=None,\n                               min_child_weight=None, missing=nan,\n                               monotone_constraints=None, multi_strategy=None,\n                               n_estimators=100, n_jobs=None,\n                               num_parallel_tree=None, ...))])",
      "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                                  [&#x27;marital-status&#x27;,\n                                                   &#x27;occupation&#x27;, &#x27;relationship&#x27;,\n                                                   &#x27;race&#x27;]),\n                                                 (&#x27;scaling&#x27;, MinMaxScaler(),\n                                                  [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;,\n                                                   &#x27;education-num&#x27;,\n                                                   &#x27;hours-per-week&#x27;])])),\n                (&#x27;model&#x27;,\n                 XGBClassifier(base_score=None, booster=None, callbacks=None,\n                               class_wieght={0: 1, 1: 1.5},\n                               c...\n                               feature_types=None, gamma=None, grow_policy=None,\n                               importance_type=None,\n                               interaction_constraints=None, learning_rate=None,\n                               max_bin=None, max_cat_threshold=None,\n                               max_cat_to_onehot=None, max_delta_step=None,\n                               max_depth=2, max_leaves=None,\n                               min_child_weight=None, missing=nan,\n                               monotone_constraints=None, multi_strategy=None,\n                               n_estimators=100, n_jobs=None,\n                               num_parallel_tree=None, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                                  [&#x27;marital-status&#x27;,\n                                                   &#x27;occupation&#x27;, &#x27;relationship&#x27;,\n                                                   &#x27;race&#x27;]),\n                                                 (&#x27;scaling&#x27;, MinMaxScaler(),\n                                                  [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;,\n                                                   &#x27;education-num&#x27;,\n                                                   &#x27;hours-per-week&#x27;])])),\n                (&#x27;model&#x27;,\n                 XGBClassifier(base_score=None, booster=None, callbacks=None,\n                               class_wieght={0: 1, 1: 1.5},\n                               c...\n                               feature_types=None, gamma=None, grow_policy=None,\n                               importance_type=None,\n                               interaction_constraints=None, learning_rate=None,\n                               max_bin=None, max_cat_threshold=None,\n                               max_cat_to_onehot=None, max_delta_step=None,\n                               max_depth=2, max_leaves=None,\n                               min_child_weight=None, missing=nan,\n                               monotone_constraints=None, multi_strategy=None,\n                               n_estimators=100, n_jobs=None,\n                               num_parallel_tree=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;On-hot-encode&#x27;,\n                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n                                 [&#x27;marital-status&#x27;, &#x27;occupation&#x27;,\n                                  &#x27;relationship&#x27;, &#x27;race&#x27;]),\n                                (&#x27;scaling&#x27;, MinMaxScaler(),\n                                 [&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;, &#x27;education-num&#x27;,\n                                  &#x27;hours-per-week&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">On-hot-encode</label><div class=\"sk-toggleable__content\"><pre>[&#x27;marital-status&#x27;, &#x27;occupation&#x27;, &#x27;relationship&#x27;, &#x27;race&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaling</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education&#x27;, &#x27;education-num&#x27;, &#x27;hours-per-week&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              class_wieght={0: 1, 1: 1.5}, colsample_bylevel=None,\n              colsample_bynode=None, colsample_bytree=None, device=None,\n              early_stopping_rounds=None, enable_categorical=False,\n              eval_metric=None, feature_types=None, gamma=None,\n              grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=2, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None,\n              num_parallel_tree=None, ...)</pre></div></div></div></div></div></div></div>"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a Pipeline and build a xgboost\n",
    "pipeline_xgb=Pipeline([\n",
    "    ('ct',ct),\n",
    "    ('model',XGBClassifier(n_estimators=100,max_depth=2,class_wieght={0:1,1:1.5}))\n",
    "])\n",
    "pipeline_xgb.fit(x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.917875669Z",
     "start_time": "2023-09-21T16:11:53.521298772Z"
    }
   },
   "id": "cb2d123fc0fc52fd"
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------o-------------\n",
      "Accuracy: 84.07786567395688\n",
      "------------o------------\n",
      "Classfication report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.93      0.90     19575\n",
      "           1       0.72      0.56      0.63      6213\n",
      "\n",
      "    accuracy                           0.84     25788\n",
      "   macro avg       0.79      0.75      0.76     25788\n",
      "weighted avg       0.83      0.84      0.83     25788\n",
      "\n",
      "------------o------------\n",
      "Logistic regression Confusion Matrix:\n",
      " [[18180  1395]\n",
      " [ 2711  3502]]\n",
      "----------------o--------------\n"
     ]
    }
   ],
   "source": [
    "evaluate(pipeline_xgb,x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.927982760Z",
     "start_time": "2023-09-21T16:11:53.749155091Z"
    }
   },
   "id": "8a6680f3edd1f0b9"
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------o-------------\n",
      "Accuracy: 83.00248138957816\n",
      "------------o------------\n",
      "Classfication report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.92      0.89      4894\n",
      "           1       0.68      0.56      0.61      1554\n",
      "\n",
      "    accuracy                           0.83      6448\n",
      "   macro avg       0.77      0.74      0.75      6448\n",
      "weighted avg       0.82      0.83      0.82      6448\n",
      "\n",
      "------------o------------\n",
      "Logistic regression Confusion Matrix:\n",
      " [[4489  405]\n",
      " [ 691  863]]\n",
      "----------------o--------------\n"
     ]
    }
   ],
   "source": [
    "#testing score\n",
    "evaluate(pipeline_xgb,x_test,y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:11:54.928219397Z",
     "start_time": "2023-09-21T16:11:53.800537254Z"
    }
   },
   "id": "f9e6fc805760d596"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Roc-Auc Curve"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a9a84f42d8cbdeda"
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f60e79bd750>"
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc-auc curve for both the model\n",
    "disp=RocCurveDisplay.from_estimator(pipeline_xgb,x_test,y_test,name='XGboost')\n",
    "RocCurveDisplay.from_estimator(pipeline_wt_balance,x_test,y_test,ax=disp.ax_,name='LogisticRegression')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-21T16:13:17.714116648Z",
     "start_time": "2023-09-21T16:13:17.445756347Z"
    }
   },
   "id": "d68353c6c27a5015"
  },
  {
   "cell_type": "markdown",
   "source": [
    "* From the above we can see that AUC is higher for XGboost model. We should chose this model as this is giving better generalization than logistic model."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9e5e791c3e72cb8d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Business Problem**\n",
    "The business problem we are trying to solve is predicting whether an individual's income exceeds $50K per year based on census data. We built two models in our analyis LogisticRegression and XGboost\n",
    "\n",
    "* LogisticRegression has given a reasonable performance based on its AUC score\n",
    "* XGboost is a boosting (ensemble) technique which performs generally better than other machine learning algorithms. The model has shown better performance in terms f1-score, auc score. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a8ea713e939c1948"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "2ae34c410dea24b5"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}