Skip to content
Permalink
master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Classification_Project/Untitled.ipynb

Go to file
 
 
Cannot retrieve contributors at this time
3251 lines (3251 sloc) 180 KB
Raw Blame
Open in GitHub Desktop
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# importing the librarys "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"\n",
"import numpy as np\n",
"\n",
"import seaborn as sns\n",
"\n",
"from matplotlib import pyplot as plt\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from sklearn import svm\n",
"from sklearn.metrics import confusion_matrix\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# reading the data from the cvs files "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"Read_data = pd.read_csv(r\"C:\\Users\\baejr\\OneDrive\\Desktop\\web dev\\income_evaluation.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>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",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>27</td>\n",
" <td>Private</td>\n",
" <td>257302</td>\n",
" <td>Assoc-acdm</td>\n",
" <td>12</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Tech-support</td>\n",
" <td>Wife</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>40</td>\n",
" <td>Private</td>\n",
" <td>154374</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>58</td>\n",
" <td>Private</td>\n",
" <td>151910</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Widowed</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Female</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>32559</th>\n",
" <td>22</td>\n",
" <td>Private</td>\n",
" <td>201490</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Never-married</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>52</td>\n",
" <td>Self-emp-inc</td>\n",
" <td>287927</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Wife</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>15024</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education-num \\\n",
"0 39 State-gov 77516 Bachelors 13 \n",
"1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
"2 38 Private 215646 HS-grad 9 \n",
"3 53 Private 234721 11th 7 \n",
"4 28 Private 338409 Bachelors 13 \n",
"... ... ... ... ... ... \n",
"32556 27 Private 257302 Assoc-acdm 12 \n",
"32557 40 Private 154374 HS-grad 9 \n",
"32558 58 Private 151910 HS-grad 9 \n",
"32559 22 Private 201490 HS-grad 9 \n",
"32560 52 Self-emp-inc 287927 HS-grad 9 \n",
"\n",
" marital-status occupation relationship race \\\n",
"0 Never-married Adm-clerical Not-in-family White \n",
"1 Married-civ-spouse Exec-managerial Husband White \n",
"2 Divorced Handlers-cleaners Not-in-family White \n",
"3 Married-civ-spouse Handlers-cleaners Husband Black \n",
"4 Married-civ-spouse Prof-specialty Wife Black \n",
"... ... ... ... ... \n",
"32556 Married-civ-spouse Tech-support Wife White \n",
"32557 Married-civ-spouse Machine-op-inspct Husband White \n",
"32558 Widowed Adm-clerical Unmarried White \n",
"32559 Never-married Adm-clerical Own-child White \n",
"32560 Married-civ-spouse Exec-managerial Wife White \n",
"\n",
" sex capital-gain capital-loss hours-per-week native-country \\\n",
"0 Male 2174 0 40 United-States \n",
"1 Male 0 0 13 United-States \n",
"2 Male 0 0 40 United-States \n",
"3 Male 0 0 40 United-States \n",
"4 Female 0 0 40 Cuba \n",
"... ... ... ... ... ... \n",
"32556 Female 0 0 38 United-States \n",
"32557 Male 0 0 40 United-States \n",
"32558 Female 0 0 40 United-States \n",
"32559 Male 0 0 20 United-States \n",
"32560 Female 15024 0 40 United-States \n",
"\n",
" income \n",
"0 <=50K \n",
"1 <=50K \n",
"2 <=50K \n",
"3 <=50K \n",
"4 <=50K \n",
"... ... \n",
"32556 <=50K \n",
"32557 >50K \n",
"32558 <=50K \n",
"32559 <=50K \n",
"32560 >50K \n",
"\n",
"[32561 rows x 15 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Read_data\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cleaning the datasets"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Male</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" Male\n",
"0 1\n",
"1 1\n",
"2 1\n",
"3 1\n",
"4 0\n",
"... ...\n",
"32556 0\n",
"32557 1\n",
"32558 0\n",
"32559 1\n",
"32560 0\n",
"\n",
"[32561 rows x 1 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Read_data.columns = [x.strip() for x in Read_data.columns]\n",
"Gender = pd.get_dummies(Read_data[\"sex\"],drop_first = True)\n",
"Gender ## cleaning the sex data\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Adm-clerical</th>\n",
" <th>Armed-Forces</th>\n",
" <th>Craft-repair</th>\n",
" <th>Exec-managerial</th>\n",
" <th>Farming-fishing</th>\n",
" <th>Handlers-cleaners</th>\n",
" <th>Machine-op-inspct</th>\n",
" <th>Other-service</th>\n",
" <th>Priv-house-serv</th>\n",
" <th>Prof-specialty</th>\n",
" <th>Protective-serv</th>\n",
" <th>Sales</th>\n",
" <th>Tech-support</th>\n",
" <th>Transport-moving</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" Adm-clerical Armed-Forces Craft-repair Exec-managerial \\\n",
"0 1 0 0 0 \n",
"1 0 0 0 1 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"... ... ... ... ... \n",
"32556 0 0 0 0 \n",
"32557 0 0 0 0 \n",
"32558 1 0 0 0 \n",
"32559 1 0 0 0 \n",
"32560 0 0 0 1 \n",
"\n",
" Farming-fishing Handlers-cleaners Machine-op-inspct \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 1 0 \n",
"3 0 1 0 \n",
"4 0 0 0 \n",
"... ... ... ... \n",
"32556 0 0 0 \n",
"32557 0 0 1 \n",
"32558 0 0 0 \n",
"32559 0 0 0 \n",
"32560 0 0 0 \n",
"\n",
" Other-service Priv-house-serv Prof-specialty Protective-serv \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 1 0 \n",
"... ... ... ... ... \n",
"32556 0 0 0 0 \n",
"32557 0 0 0 0 \n",
"32558 0 0 0 0 \n",
"32559 0 0 0 0 \n",
"32560 0 0 0 0 \n",
"\n",
" Sales Tech-support Transport-moving \n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"... ... ... ... \n",
"32556 0 1 0 \n",
"32557 0 0 0 \n",
"32558 0 0 0 \n",
"32559 0 0 0 \n",
"32560 0 0 0 \n",
"\n",
"[32561 rows x 14 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Occupation = pd.get_dummies(Read_data[\"occupation\"],drop_first = True) \n",
"Occupation ## cleaning and occupation data "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cambodia</th>\n",
" <th>Canada</th>\n",
" <th>China</th>\n",
" <th>Columbia</th>\n",
" <th>Cuba</th>\n",
" <th>Dominican-Republic</th>\n",
" <th>Ecuador</th>\n",
" <th>El-Salvador</th>\n",
" <th>England</th>\n",
" <th>France</th>\n",
" <th>...</th>\n",
" <th>Portugal</th>\n",
" <th>Puerto-Rico</th>\n",
" <th>Scotland</th>\n",
" <th>South</th>\n",
" <th>Taiwan</th>\n",
" <th>Thailand</th>\n",
" <th>Trinadad&amp;Tobago</th>\n",
" <th>United-States</th>\n",
" <th>Vietnam</th>\n",
" <th>Yugoslavia</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" Cambodia Canada China Columbia Cuba Dominican-Republic \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 1 0 \n",
"... ... ... ... ... ... ... \n",
"32556 0 0 0 0 0 0 \n",
"32557 0 0 0 0 0 0 \n",
"32558 0 0 0 0 0 0 \n",
"32559 0 0 0 0 0 0 \n",
"32560 0 0 0 0 0 0 \n",
"\n",
" Ecuador El-Salvador England France ... Portugal \\\n",
"0 0 0 0 0 ... 0 \n",
"1 0 0 0 0 ... 0 \n",
"2 0 0 0 0 ... 0 \n",
"3 0 0 0 0 ... 0 \n",
"4 0 0 0 0 ... 0 \n",
"... ... ... ... ... ... ... \n",
"32556 0 0 0 0 ... 0 \n",
"32557 0 0 0 0 ... 0 \n",
"32558 0 0 0 0 ... 0 \n",
"32559 0 0 0 0 ... 0 \n",
"32560 0 0 0 0 ... 0 \n",
"\n",
" Puerto-Rico Scotland South Taiwan Thailand Trinadad&Tobago \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"... ... ... ... ... ... ... \n",
"32556 0 0 0 0 0 0 \n",
"32557 0 0 0 0 0 0 \n",
"32558 0 0 0 0 0 0 \n",
"32559 0 0 0 0 0 0 \n",
"32560 0 0 0 0 0 0 \n",
"\n",
" United-States Vietnam Yugoslavia \n",
"0 1 0 0 \n",
"1 1 0 0 \n",
"2 1 0 0 \n",
"3 1 0 0 \n",
"4 0 0 0 \n",
"... ... ... ... \n",
"32556 1 0 0 \n",
"32557 1 0 0 \n",
"32558 1 0 0 \n",
"32559 1 0 0 \n",
"32560 1 0 0 \n",
"\n",
"[32561 rows x 41 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Countries = pd.get_dummies(Read_data[\"native-country\"],drop_first = True) ## cleaning and get data for each country \n",
"Countries"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>&gt;50K</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" >50K\n",
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"... ...\n",
"32556 0\n",
"32557 1\n",
"32558 0\n",
"32559 0\n",
"32560 1\n",
"\n",
"[32561 rows x 1 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Income = pd.get_dummies(Read_data[\"income\"],drop_first = True)\n",
"Income ## cleaning the income data "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Married-AF-spouse</th>\n",
" <th>Married-civ-spouse</th>\n",
" <th>Married-spouse-absent</th>\n",
" <th>Never-married</th>\n",
" <th>Separated</th>\n",
" <th>Widowed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Married-AF-spouse Married-civ-spouse Married-spouse-absent \\\n",
"0 0 0 0 \n",
"1 0 1 0 \n",
"2 0 0 0 \n",
"3 0 1 0 \n",
"4 0 1 0 \n",
"... ... ... ... \n",
"32556 0 1 0 \n",
"32557 0 1 0 \n",
"32558 0 0 0 \n",
"32559 0 0 0 \n",
"32560 0 1 0 \n",
"\n",
" Never-married Separated Widowed \n",
"0 1 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"... ... ... ... \n",
"32556 0 0 0 \n",
"32557 0 0 0 \n",
"32558 0 0 1 \n",
"32559 1 0 0 \n",
"32560 0 0 0 \n",
"\n",
"[32561 rows x 6 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Marital_status = pd.get_dummies(Read_data[\"marital-status\"],drop_first = True)\n",
"## cleaning marital_status\n",
"Marital_status"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Not-in-family</th>\n",
" <th>Other-relative</th>\n",
" <th>Own-child</th>\n",
" <th>Unmarried</th>\n",
" <th>Wife</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" Not-in-family Other-relative Own-child Unmarried Wife\n",
"0 1 0 0 0 0\n",
"1 0 0 0 0 0\n",
"2 1 0 0 0 0\n",
"3 0 0 0 0 0\n",
"4 0 0 0 0 1\n",
"... ... ... ... ... ...\n",
"32556 0 0 0 0 1\n",
"32557 0 0 0 0 0\n",
"32558 0 0 0 1 0\n",
"32559 0 0 1 0 0\n",
"32560 0 0 0 0 1\n",
"\n",
"[32561 rows x 5 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Relationship = pd.get_dummies(Read_data[\"relationship\"],drop_first = True)\n",
"\n",
"Relationship ## cleaning the relationship "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Federal-gov</th>\n",
" <th>Local-gov</th>\n",
" <th>Never-worked</th>\n",
" <th>Private</th>\n",
" <th>Self-emp-inc</th>\n",
" <th>Self-emp-not-inc</th>\n",
" <th>State-gov</th>\n",
" <th>Without-pay</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Federal-gov Local-gov Never-worked Private Self-emp-inc \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 1 0 \n",
"3 0 0 0 1 0 \n",
"4 0 0 0 1 0 \n",
"... ... ... ... ... ... \n",
"32556 0 0 0 1 0 \n",
"32557 0 0 0 1 0 \n",
"32558 0 0 0 1 0 \n",
"32559 0 0 0 1 0 \n",
"32560 0 0 0 0 1 \n",
"\n",
" Self-emp-not-inc State-gov Without-pay \n",
"0 0 1 0 \n",
"1 1 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"... ... ... ... \n",
"32556 0 0 0 \n",
"32557 0 0 0 \n",
"32558 0 0 0 \n",
"32559 0 0 0 \n",
"32560 0 0 0 \n",
"\n",
"[32561 rows x 8 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Work_class = pd.get_dummies(Read_data[\"workclass\"],drop_first = True)\n",
"\n",
"Work_class ## cleaning the work class data "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Asian-Pac-Islander</th>\n",
" <th>Black</th>\n",
" <th>Other</th>\n",
" <th>White</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" Asian-Pac-Islander Black Other White\n",
"0 0 0 0 1\n",
"1 0 0 0 1\n",
"2 0 0 0 1\n",
"3 0 1 0 0\n",
"4 0 1 0 0\n",
"... ... ... ... ...\n",
"32556 0 0 0 1\n",
"32557 0 0 0 1\n",
"32558 0 0 0 1\n",
"32559 0 0 0 1\n",
"32560 0 0 0 1\n",
"\n",
"[32561 rows x 4 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Race = pd.get_dummies(Read_data[\"race\"],drop_first = True)\n",
"\n",
"Race ## cleaning the race data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# drope all uncleaned data from the dataset "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"Read_data.drop([\"workclass\",\"race\",\"relationship\",\"sex\",\"marital-status\",\"race\",\"native-country\",\"occupation\",\"native-country\",\"education\",\"income\"],axis=1, inplace= True)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>0</th>\n",
" <td>39</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>2174</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>27</td>\n",
" <td>257302</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>40</td>\n",
" <td>154374</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>58</td>\n",
" <td>151910</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>22</td>\n",
" <td>201490</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>52</td>\n",
" <td>287927</td>\n",
" <td>9</td>\n",
" <td>15024</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education-num capital-gain capital-loss hours-per-week\n",
"0 39 77516 13 2174 0 40\n",
"1 50 83311 13 0 0 13\n",
"2 38 215646 9 0 0 40\n",
"3 53 234721 7 0 0 40\n",
"4 28 338409 13 0 0 40\n",
"... ... ... ... ... ... ...\n",
"32556 27 257302 12 0 0 38\n",
"32557 40 154374 9 0 0 40\n",
"32558 58 151910 9 0 0 40\n",
"32559 22 201490 9 0 0 20\n",
"32560 52 287927 9 15024 0 40\n",
"\n",
"[32561 rows x 6 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Read_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adding the all cleaned data to the dataset "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"\n",
"Read_data = pd.concat([Gender,Occupation,Countries,Relationship,Work_class,Marital_status,Race,Read_data,Income],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Male</th>\n",
" <th>Adm-clerical</th>\n",
" <th>Armed-Forces</th>\n",
" <th>Craft-repair</th>\n",
" <th>Exec-managerial</th>\n",
" <th>Farming-fishing</th>\n",
" <th>Handlers-cleaners</th>\n",
" <th>Machine-op-inspct</th>\n",
" <th>Other-service</th>\n",
" <th>Priv-house-serv</th>\n",
" <th>...</th>\n",
" <th>Black</th>\n",
" <th>Other</th>\n",
" <th>White</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",
" <th>&gt;50K</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>2174</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>50</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>53</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32556</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>27</td>\n",
" <td>257302</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32557</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>154374</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32558</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>58</td>\n",
" <td>151910</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>22</td>\n",
" <td>201490</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>52</td>\n",
" <td>287927</td>\n",
" <td>9</td>\n",
" <td>15024</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>32561 rows × 86 columns</p>\n",
"</div>"
],
"text/plain": [
" Male Adm-clerical Armed-Forces Craft-repair Exec-managerial \\\n",
"0 1 1 0 0 0 \n",
"1 1 0 0 0 1 \n",
"2 1 0 0 0 0 \n",
"3 1 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"... ... ... ... ... ... \n",
"32556 0 0 0 0 0 \n",
"32557 1 0 0 0 0 \n",
"32558 0 1 0 0 0 \n",
"32559 1 1 0 0 0 \n",
"32560 0 0 0 0 1 \n",
"\n",
" Farming-fishing Handlers-cleaners Machine-op-inspct \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 1 0 \n",
"3 0 1 0 \n",
"4 0 0 0 \n",
"... ... ... ... \n",
"32556 0 0 0 \n",
"32557 0 0 1 \n",
"32558 0 0 0 \n",
"32559 0 0 0 \n",
"32560 0 0 0 \n",
"\n",
" Other-service Priv-house-serv ... Black Other White age \\\n",
"0 0 0 ... 0 0 1 39 \n",
"1 0 0 ... 0 0 1 50 \n",
"2 0 0 ... 0 0 1 38 \n",
"3 0 0 ... 1 0 0 53 \n",
"4 0 0 ... 1 0 0 28 \n",
"... ... ... ... ... ... ... ... \n",
"32556 0 0 ... 0 0 1 27 \n",
"32557 0 0 ... 0 0 1 40 \n",
"32558 0 0 ... 0 0 1 58 \n",
"32559 0 0 ... 0 0 1 22 \n",
"32560 0 0 ... 0 0 1 52 \n",
"\n",
" fnlwgt education-num capital-gain capital-loss hours-per-week \\\n",
"0 77516 13 2174 0 40 \n",
"1 83311 13 0 0 13 \n",
"2 215646 9 0 0 40 \n",
"3 234721 7 0 0 40 \n",
"4 338409 13 0 0 40 \n",
"... ... ... ... ... ... \n",
"32556 257302 12 0 0 38 \n",
"32557 154374 9 0 0 40 \n",
"32558 151910 9 0 0 40 \n",
"32559 201490 9 0 0 20 \n",
"32560 287927 9 15024 0 40 \n",
"\n",
" >50K \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
"32556 0 \n",
"32557 1 \n",
"32558 0 \n",
"32559 0 \n",
"32560 1 \n",
"\n",
"[32561 rows x 86 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Read_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get the correlation between the data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Male</th>\n",
" <th>Adm-clerical</th>\n",
" <th>Armed-Forces</th>\n",
" <th>Craft-repair</th>\n",
" <th>Exec-managerial</th>\n",
" <th>Farming-fishing</th>\n",
" <th>Handlers-cleaners</th>\n",
" <th>Machine-op-inspct</th>\n",
" <th>Other-service</th>\n",
" <th>Priv-house-serv</th>\n",
" <th>...</th>\n",
" <th>Black</th>\n",
" <th>Other</th>\n",
" <th>White</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",
" <th>&gt;50K</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Male</th>\n",
" <td>1.00</td>\n",
" <td>-0.26</td>\n",
" <td>0.01</td>\n",
" <td>0.22</td>\n",
" <td>0.04</td>\n",
" <td>0.10</td>\n",
" <td>0.09</td>\n",
" <td>0.03</td>\n",
" <td>-0.15</td>\n",
" <td>-0.09</td>\n",
" <td>...</td>\n",
" <td>-0.12</td>\n",
" <td>-0.01</td>\n",
" <td>0.10</td>\n",
" <td>0.09</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.05</td>\n",
" <td>0.05</td>\n",
" <td>0.23</td>\n",
" <td>0.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adm-clerical</th>\n",
" <td>-0.26</td>\n",
" <td>1.00</td>\n",
" <td>-0.01</td>\n",
" <td>-0.14</td>\n",
" <td>-0.14</td>\n",
" <td>-0.06</td>\n",
" <td>-0.08</td>\n",
" <td>-0.09</td>\n",
" <td>-0.12</td>\n",
" <td>-0.02</td>\n",
" <td>...</td>\n",
" <td>0.04</td>\n",
" <td>-0.01</td>\n",
" <td>-0.04</td>\n",
" <td>-0.04</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>-0.03</td>\n",
" <td>-0.02</td>\n",
" <td>-0.08</td>\n",
" <td>-0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armed-Forces</th>\n",
" <td>0.01</td>\n",
" <td>-0.01</td>\n",
" <td>1.00</td>\n",
" <td>-0.01</td>\n",
" <td>-0.01</td>\n",
" <td>-0.00</td>\n",
" <td>-0.00</td>\n",
" <td>-0.00</td>\n",
" <td>-0.01</td>\n",
" <td>-0.00</td>\n",
" <td>...</td>\n",
" <td>0.00</td>\n",
" <td>-0.00</td>\n",
" <td>-0.00</td>\n",
" <td>-0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>-0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>-0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Craft-repair</th>\n",
" <td>0.22</td>\n",
" <td>-0.14</td>\n",
" <td>-0.01</td>\n",
" <td>1.00</td>\n",
" <td>-0.14</td>\n",
" <td>-0.07</td>\n",
" <td>-0.08</td>\n",
" <td>-0.10</td>\n",
" <td>-0.13</td>\n",
" <td>-0.03</td>\n",
" <td>...</td>\n",
" <td>-0.05</td>\n",
" <td>-0.01</td>\n",
" <td>0.05</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>-0.14</td>\n",
" <td>-0.02</td>\n",
" <td>0.00</td>\n",
" <td>0.06</td>\n",
" <td>-0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exec-managerial</th>\n",
" <td>0.04</td>\n",
" <td>-0.14</td>\n",
" <td>-0.01</td>\n",
" <td>-0.14</td>\n",
" <td>1.00</td>\n",
" <td>-0.07</td>\n",
" <td>-0.08</td>\n",
" <td>-0.10</td>\n",
" <td>-0.13</td>\n",
" <td>-0.03</td>\n",
" <td>...</td>\n",
" <td>-0.05</td>\n",
" <td>-0.02</td>\n",
" <td>0.05</td>\n",
" <td>0.10</td>\n",
" <td>-0.02</td>\n",
" <td>0.20</td>\n",
" <td>0.06</td>\n",
" <td>0.05</td>\n",
" <td>0.14</td>\n",
" <td>0.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>education-num</th>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>-0.14</td>\n",
" <td>0.20</td>\n",
" <td>-0.10</td>\n",
" <td>-0.13</td>\n",
" <td>-0.16</td>\n",
" <td>-0.17</td>\n",
" <td>-0.07</td>\n",
" <td>...</td>\n",
" <td>-0.08</td>\n",
" <td>-0.04</td>\n",
" <td>0.05</td>\n",
" <td>0.04</td>\n",
" <td>-0.04</td>\n",
" <td>1.00</td>\n",
" <td>0.12</td>\n",
" <td>0.08</td>\n",
" <td>0.15</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>capital-gain</th>\n",
" <td>0.05</td>\n",
" <td>-0.03</td>\n",
" <td>-0.00</td>\n",
" <td>-0.02</td>\n",
" <td>0.06</td>\n",
" <td>-0.01</td>\n",
" <td>-0.02</td>\n",
" <td>-0.03</td>\n",
" <td>-0.04</td>\n",
" <td>-0.01</td>\n",
" <td>...</td>\n",
" <td>-0.02</td>\n",
" <td>-0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.08</td>\n",
" <td>0.00</td>\n",
" <td>0.12</td>\n",
" <td>1.00</td>\n",
" <td>-0.03</td>\n",
" <td>0.08</td>\n",
" <td>0.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>capital-loss</th>\n",
" <td>0.05</td>\n",
" <td>-0.02</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.05</td>\n",
" <td>-0.01</td>\n",
" <td>-0.02</td>\n",
" <td>-0.02</td>\n",
" <td>-0.04</td>\n",
" <td>-0.01</td>\n",
" <td>...</td>\n",
" <td>-0.02</td>\n",
" <td>-0.01</td>\n",
" <td>0.02</td>\n",
" <td>0.06</td>\n",
" <td>-0.01</td>\n",
" <td>0.08</td>\n",
" <td>-0.03</td>\n",
" <td>1.00</td>\n",
" <td>0.05</td>\n",
" <td>0.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hours-per-week</th>\n",
" <td>0.23</td>\n",
" <td>-0.08</td>\n",
" <td>0.00</td>\n",
" <td>0.06</td>\n",
" <td>0.14</td>\n",
" <td>0.09</td>\n",
" <td>-0.04</td>\n",
" <td>0.01</td>\n",
" <td>-0.16</td>\n",
" <td>-0.04</td>\n",
" <td>...</td>\n",
" <td>-0.05</td>\n",
" <td>-0.01</td>\n",
" <td>0.05</td>\n",
" <td>0.07</td>\n",
" <td>-0.02</td>\n",
" <td>0.15</td>\n",
" <td>0.08</td>\n",
" <td>0.05</td>\n",
" <td>1.00</td>\n",
" <td>0.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>&gt;50K</th>\n",
" <td>0.22</td>\n",
" <td>-0.09</td>\n",
" <td>-0.01</td>\n",
" <td>-0.01</td>\n",
" <td>0.21</td>\n",
" <td>-0.05</td>\n",
" <td>-0.09</td>\n",
" <td>-0.07</td>\n",
" <td>-0.16</td>\n",
" <td>-0.04</td>\n",
" <td>...</td>\n",
" <td>-0.09</td>\n",
" <td>-0.03</td>\n",
" <td>0.09</td>\n",
" <td>0.23</td>\n",
" <td>-0.01</td>\n",
" <td>0.34</td>\n",
" <td>0.22</td>\n",
" <td>0.15</td>\n",
" <td>0.23</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>86 rows × 86 columns</p>\n",
"</div>"
],
"text/plain": [
" Male Adm-clerical Armed-Forces Craft-repair \\\n",
" Male 1.00 -0.26 0.01 0.22 \n",
" Adm-clerical -0.26 1.00 -0.01 -0.14 \n",
" Armed-Forces 0.01 -0.01 1.00 -0.01 \n",
" Craft-repair 0.22 -0.14 -0.01 1.00 \n",
" Exec-managerial 0.04 -0.14 -0.01 -0.14 \n",
"... ... ... ... ... \n",
"education-num 0.01 0.00 0.00 -0.14 \n",
"capital-gain 0.05 -0.03 -0.00 -0.02 \n",
"capital-loss 0.05 -0.02 0.01 0.00 \n",
"hours-per-week 0.23 -0.08 0.00 0.06 \n",
" >50K 0.22 -0.09 -0.01 -0.01 \n",
"\n",
" Exec-managerial Farming-fishing Handlers-cleaners \\\n",
" Male 0.04 0.10 0.09 \n",
" Adm-clerical -0.14 -0.06 -0.08 \n",
" Armed-Forces -0.01 -0.00 -0.00 \n",
" Craft-repair -0.14 -0.07 -0.08 \n",
" Exec-managerial 1.00 -0.07 -0.08 \n",
"... ... ... ... \n",
"education-num 0.20 -0.10 -0.13 \n",
"capital-gain 0.06 -0.01 -0.02 \n",
"capital-loss 0.05 -0.01 -0.02 \n",
"hours-per-week 0.14 0.09 -0.04 \n",
" >50K 0.21 -0.05 -0.09 \n",
"\n",
" Machine-op-inspct Other-service Priv-house-serv ... \\\n",
" Male 0.03 -0.15 -0.09 ... \n",
" Adm-clerical -0.09 -0.12 -0.02 ... \n",
" Armed-Forces -0.00 -0.01 -0.00 ... \n",
" Craft-repair -0.10 -0.13 -0.03 ... \n",
" Exec-managerial -0.10 -0.13 -0.03 ... \n",
"... ... ... ... ... \n",
"education-num -0.16 -0.17 -0.07 ... \n",
"capital-gain -0.03 -0.04 -0.01 ... \n",
"capital-loss -0.02 -0.04 -0.01 ... \n",
"hours-per-week 0.01 -0.16 -0.04 ... \n",
" >50K -0.07 -0.16 -0.04 ... \n",
"\n",
" Black Other White age fnlwgt education-num \\\n",
" Male -0.12 -0.01 0.10 0.09 0.03 0.01 \n",
" Adm-clerical 0.04 -0.01 -0.04 -0.04 0.01 0.00 \n",
" Armed-Forces 0.00 -0.00 -0.00 -0.01 0.00 0.00 \n",
" Craft-repair -0.05 -0.01 0.05 0.01 0.01 -0.14 \n",
" Exec-managerial -0.05 -0.02 0.05 0.10 -0.02 0.20 \n",
"... ... ... ... ... ... ... \n",
"education-num -0.08 -0.04 0.05 0.04 -0.04 1.00 \n",
"capital-gain -0.02 -0.00 0.01 0.08 0.00 0.12 \n",
"capital-loss -0.02 -0.01 0.02 0.06 -0.01 0.08 \n",
"hours-per-week -0.05 -0.01 0.05 0.07 -0.02 0.15 \n",
" >50K -0.09 -0.03 0.09 0.23 -0.01 0.34 \n",
"\n",
" capital-gain capital-loss hours-per-week >50K \n",
" Male 0.05 0.05 0.23 0.22 \n",
" Adm-clerical -0.03 -0.02 -0.08 -0.09 \n",
" Armed-Forces -0.00 0.01 0.00 -0.01 \n",
" Craft-repair -0.02 0.00 0.06 -0.01 \n",
" Exec-managerial 0.06 0.05 0.14 0.21 \n",
"... ... ... ... ... \n",
"education-num 0.12 0.08 0.15 0.34 \n",
"capital-gain 1.00 -0.03 0.08 0.22 \n",
"capital-loss -0.03 1.00 0.05 0.15 \n",
"hours-per-week 0.08 0.05 1.00 0.23 \n",
" >50K 0.22 0.15 0.23 1.00 \n",
"\n",
"[86 rows x 86 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Correlation = Read_data.corr().round(2)\n",
"Correlation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get the high correlation feature only "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"Correlation.columns = [x.strip() for x in Read_data.columns]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[' Male',\n",
" ' Exec-managerial',\n",
" ' Prof-specialty',\n",
" ' Married-civ-spouse',\n",
" 'age',\n",
" 'education-num',\n",
" 'capital-gain',\n",
" 'hours-per-week',\n",
" ' >50K']"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"High_Correlation = Correlation[Correlation[\">50K\"]>0.15].index.tolist()\n",
"High_Correlation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Represent the Correlation on heatmap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 900x900 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Heatmap_Correlation= Read_data[High_Correlation].corr().round(2)\n",
"\n",
"plt.figure(figsize=(12.5,12.5))\n",
"sns.heatmap(Heatmap_Correlation, annot=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Split the high correlation data"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"X = Read_data[High_Correlation].iloc[:,:-1]\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"Y = Read_data[High_Correlation].iloc[:,-1:]\n",
"Y = np.ravel(Y)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size = 0.3,random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Apply the SVM Classification Model"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"SVC_Model = svm.SVC()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC()"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVC_Model.fit(X_train, Y_train)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"Y_train_pred = SVC_Model.predict(X_train)\n",
"Y_test_pred = SVC_Model.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model accuracy result"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8002369252369252"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVC_Model.score(X_train,Y_train)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7998771624526564"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVC_Model.score(X_test, Y_test)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"confusion_matrix [[7385 22]\n",
" [1933 429]]\n"
]
}
],
"source": [
"print(\"confusion_matrix\",confusion_matrix(Y_test, Y_test_pred))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import plot_confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_confusion_matrix(SVC_Model, X_test, Y_test) \n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}