Gender Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "881395f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries \n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31acb2a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Data\n",
    "data=pd.read_csv(\"C:\\\\Users\\\\busra\\\\OneDrive\\\\Masaüstü\\\\gender.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de43690f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Income (USD)</th>\n",
       "      <th>Favorite Color</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>70</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>Married</td>\n",
       "      <td>75000</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>25</td>\n",
       "      <td>182</td>\n",
       "      <td>85</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>45000</td>\n",
       "      <td>Green</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>41</td>\n",
       "      <td>160</td>\n",
       "      <td>62</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>Doctorate Degree</td>\n",
       "      <td>Married</td>\n",
       "      <td>120000</td>\n",
       "      <td>Purple</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>38</td>\n",
       "      <td>178</td>\n",
       "      <td>79</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>90000</td>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>165</td>\n",
       "      <td>58</td>\n",
       "      <td>Graphic Designer</td>\n",
       "      <td>Associate's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>35000</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender   Age   Height (cm)   Weight (kg)             Occupation  \\\n",
       "0     male    32           175            70      Software Engineer   \n",
       "1     male    25           182            85   Sales Representative   \n",
       "2   female    41           160            62                 Doctor   \n",
       "3     male    38           178            79                 Lawyer   \n",
       "4   female    29           165            58       Graphic Designer   \n",
       "\n",
       "       Education Level  Marital Status   Income (USD)  Favorite Color  \\\n",
       "0      Master's Degree         Married          75000            Blue   \n",
       "1    Bachelor's Degree          Single          45000           Green   \n",
       "2     Doctorate Degree         Married         120000          Purple   \n",
       "3    Bachelor's Degree          Single          90000             Red   \n",
       "4   Associate's Degree          Single          35000          Yellow   \n",
       "\n",
       "   Unnamed: 9  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f352616",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Income (USD)</th>\n",
       "      <th>Favorite Color</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>female</td>\n",
       "      <td>32</td>\n",
       "      <td>170</td>\n",
       "      <td>64</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>Associate's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>60000</td>\n",
       "      <td>Orange</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>male</td>\n",
       "      <td>38</td>\n",
       "      <td>176</td>\n",
       "      <td>79</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Married</td>\n",
       "      <td>90000</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "      <td>162</td>\n",
       "      <td>55</td>\n",
       "      <td>Graphic Designer</td>\n",
       "      <td>Associate's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>55000</td>\n",
       "      <td>Green</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>male</td>\n",
       "      <td>33</td>\n",
       "      <td>175</td>\n",
       "      <td>77</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Married</td>\n",
       "      <td>80000</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>164</td>\n",
       "      <td>57</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>65000</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender   Age   Height (cm)   Weight (kg)            Occupation  \\\n",
       "126  female    32           170            64                 Nurse   \n",
       "127    male    38           176            79       Project Manager   \n",
       "128  female    27           162            55      Graphic Designer   \n",
       "129    male    33           175            77  Sales Representative   \n",
       "130  female    29           164            57    Software Developer   \n",
       "\n",
       "        Education Level  Marital Status   Income (USD)  Favorite Color  \\\n",
       "126  Associate's Degree          Single          60000          Orange   \n",
       "127   Bachelor's Degree         Married          90000           Black   \n",
       "128  Associate's Degree          Single          55000           Green   \n",
       "129   Bachelor's Degree         Married          80000          Yellow   \n",
       "130   Bachelor's Degree          Single          65000            Blue   \n",
       "\n",
       "     Unnamed: 9  \n",
       "126         NaN  \n",
       "127         NaN  \n",
       "128         NaN  \n",
       "129         NaN  \n",
       "130         NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79f3786a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([' Gender', ' Age', ' Height (cm)', ' Weight (kg)', ' Occupation',\n",
       "       ' Education Level', ' Marital Status', ' Income (USD)',\n",
       "       ' Favorite Color', 'Unnamed: 9'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns    #There is 1 tab space in front of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93c21e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Income (USD)</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>131.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.564885</td>\n",
       "      <td>173.198473</td>\n",
       "      <td>71.458015</td>\n",
       "      <td>93206.106870</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.984723</td>\n",
       "      <td>8.045467</td>\n",
       "      <td>12.648052</td>\n",
       "      <td>74045.382919</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>55000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>180.500000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   Height (cm)   Weight (kg)   Income (USD)  Unnamed: 9\n",
       "count  131.000000    131.000000    131.000000     131.000000         0.0\n",
       "mean    34.564885    173.198473     71.458015   93206.106870         NaN\n",
       "std      5.984723      8.045467     12.648052   74045.382919         NaN\n",
       "min     24.000000    160.000000     50.000000   30000.000000         NaN\n",
       "25%     29.000000    166.000000     60.000000   55000.000000         NaN\n",
       "50%     34.000000    175.000000     75.000000   75000.000000         NaN\n",
       "75%     39.000000    180.500000     83.000000  100000.000000         NaN\n",
       "max     52.000000    190.000000     94.000000  500000.000000         NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c1810f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 131 entries, 0 to 130\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0    Gender           131 non-null    object \n",
      " 1    Age              131 non-null    int64  \n",
      " 2    Height (cm)      131 non-null    int64  \n",
      " 3    Weight (kg)      131 non-null    int64  \n",
      " 4    Occupation       131 non-null    object \n",
      " 5    Education Level  131 non-null    object \n",
      " 6    Marital Status   131 non-null    object \n",
      " 7    Income (USD)     131 non-null    int64  \n",
      " 8    Favorite Color   131 non-null    object \n",
      " 9   Unnamed: 9        0 non-null      float64\n",
      "dtypes: float64(1), int64(4), object(5)\n",
      "memory usage: 10.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "391b5584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(131, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23babfac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Gender               0\n",
       " Age                  0\n",
       " Height (cm)          0\n",
       " Weight (kg)          0\n",
       " Occupation           0\n",
       " Education Level      0\n",
       " Marital Status       0\n",
       " Income (USD)         0\n",
       " Favorite Color       0\n",
       "Unnamed: 9          131\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3cf9bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Teacher                  7\n",
       "Nurse                    6\n",
       "Writer                   6\n",
       "Project Manager          6\n",
       "Marketing Specialist     6\n",
       "Lawyer                   6\n",
       "Graphic Designer         6\n",
       "Doctor                   6\n",
       "Engineer                 6\n",
       " Teacher                 5\n",
       "Architect                4\n",
       "Sales Representative     4\n",
       "IT Manager               4\n",
       " Writer                  3\n",
       " Sales Representative    3\n",
       "Business Analyst         3\n",
       "Accountant               3\n",
       "CEO                      3\n",
       " IT Manager              3\n",
       " Analyst                 3\n",
       " Nurse                   3\n",
       " Architect               3\n",
       " Accountant              3\n",
       " Engineer                3\n",
       " Project Manager         3\n",
       " CEO                     3\n",
       " Marketing Specialist    3\n",
       "Analyst                  3\n",
       " Graphic Designer        3\n",
       " Lawyer                  3\n",
       " Doctor                  3\n",
       " Business Analyst        2\n",
       " Software Engineer       1\n",
       " Business Consultant     1\n",
       "Software Developer       1\n",
       "Name:  Occupation, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\" Occupation\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9133a332",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Unnamed: 9',' Favorite Color', \" Occupation\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d1d1b638",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Income (USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>70</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>Married</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>25</td>\n",
       "      <td>182</td>\n",
       "      <td>85</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>41</td>\n",
       "      <td>160</td>\n",
       "      <td>62</td>\n",
       "      <td>Doctorate Degree</td>\n",
       "      <td>Married</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>38</td>\n",
       "      <td>178</td>\n",
       "      <td>79</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>165</td>\n",
       "      <td>58</td>\n",
       "      <td>Associate's Degree</td>\n",
       "      <td>Single</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender   Age   Height (cm)   Weight (kg)      Education Level  \\\n",
       "0     male    32           175            70      Master's Degree   \n",
       "1     male    25           182            85    Bachelor's Degree   \n",
       "2   female    41           160            62     Doctorate Degree   \n",
       "3     male    38           178            79    Bachelor's Degree   \n",
       "4   female    29           165            58   Associate's Degree   \n",
       "\n",
       "   Marital Status   Income (USD)  \n",
       "0         Married          75000  \n",
       "1          Single          45000  \n",
       "2         Married         120000  \n",
       "3          Single          90000  \n",
       "4          Single          35000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce978f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelor's Degree      49\n",
       " Bachelor's Degree     29\n",
       "Associate's Degree     12\n",
       " Master's Degree       10\n",
       "Master's Degree        10\n",
       "Doctorate Degree        9\n",
       " Doctorate Degree       6\n",
       " Associate's Degree     6\n",
       "Name:  Education Level, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\" Education Level\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b31388ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({\" Education Level\":{\"Bachelor's Degree\":1,\" Bachelor's Degree\":1,\"Associate's Degree\":2,\" Associate's Degree\":2,\n",
    "                                 \" Master's Degree\":3,\"Master's Degree\":3,\"Doctorate Degree\":4,\" Doctorate Degree\":4}},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c1b6b9",
   "metadata": {},
   "source": [
    "Education Level\n",
    "- 1-->Bachelor's Degree\n",
    "- 2-->Associate's Degree\n",
    "- 3-->Master's Degree\n",
    "- 4-->Doctorate Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0b354d8f",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Income (USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>25</td>\n",
       "      <td>182</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>41</td>\n",
       "      <td>160</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>38</td>\n",
       "      <td>178</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>165</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender   Age   Height (cm)   Weight (kg)   Education Level  \\\n",
       "0     male    32           175            70                 3   \n",
       "1     male    25           182            85                 1   \n",
       "2   female    41           160            62                 4   \n",
       "3     male    38           178            79                 1   \n",
       "4   female    29           165            58                 2   \n",
       "\n",
       "   Marital Status   Income (USD)  \n",
       "0         Married          75000  \n",
       "1          Single          45000  \n",
       "2         Married         120000  \n",
       "3          Single          90000  \n",
       "4          Single          35000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d2d1c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married      41\n",
       "Single       38\n",
       " Married     24\n",
       " Single      21\n",
       " Divorced     4\n",
       " Widowed      2\n",
       "Divorced      1\n",
       "Name:  Marital Status, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\" Marital Status\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c7356dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({\" Marital Status\":{\"Married\":1,\" Married\":1,\"Single\":2,\" Single\":2,\"Divorced\":3,\" Divorced\":3,\" Widowed\":4}},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477b41a9",
   "metadata": {},
   "source": [
    "Marital Status\n",
    "- 1-->Married\n",
    "- 2-->Single\n",
    "- 3-->Divorced\n",
    "- 4-->Widowed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f85211ad",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Income (USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>25</td>\n",
       "      <td>182</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>41</td>\n",
       "      <td>160</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>38</td>\n",
       "      <td>178</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>165</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender   Age   Height (cm)   Weight (kg)   Education Level  \\\n",
       "0     male    32           175            70                 3   \n",
       "1     male    25           182            85                 1   \n",
       "2   female    41           160            62                 4   \n",
       "3     male    38           178            79                 1   \n",
       "4   female    29           165            58                 2   \n",
       "\n",
       "    Marital Status   Income (USD)  \n",
       "0                1          75000  \n",
       "1                2          45000  \n",
       "2                1         120000  \n",
       "3                2          90000  \n",
       "4                2          35000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec06b1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male       41\n",
       "female     39\n",
       " male      27\n",
       " female    24\n",
       "Name:  Gender, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\" Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "25cd0a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({\" Gender\":{\"male\":0,\" male\":0,\"female\":1,\" female\":1}},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63230438",
   "metadata": {},
   "source": [
    "Gender \n",
    "- 0-->MALE\n",
    "- 1-->FEMALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "84fadff8",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Income (USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>182</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>160</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>178</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>165</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender   Age   Height (cm)   Weight (kg)   Education Level  \\\n",
       "0        0    32           175            70                 3   \n",
       "1        0    25           182            85                 1   \n",
       "2        1    41           160            62                 4   \n",
       "3        0    38           178            79                 1   \n",
       "4        1    29           165            58                 2   \n",
       "\n",
       "    Marital Status   Income (USD)  \n",
       "0                1          75000  \n",
       "1                2          45000  \n",
       "2                1         120000  \n",
       "3                2          90000  \n",
       "4                2          35000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b324f5de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split Data into Targets and Features\n",
    "X=data.drop(columns=\" Gender\",axis=1)\n",
    "Y=data[\" Gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2a631b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Age   Height (cm)   Weight (kg)   Education Level   Marital Status  \\\n",
      "0      32           175            70                 3                1   \n",
      "1      25           182            85                 1                2   \n",
      "2      41           160            62                 4                1   \n",
      "3      38           178            79                 1                2   \n",
      "4      29           165            58                 2                2   \n",
      "..    ...           ...           ...               ...              ...   \n",
      "126    32           170            64                 2                2   \n",
      "127    38           176            79                 1                1   \n",
      "128    27           162            55                 2                2   \n",
      "129    33           175            77                 1                1   \n",
      "130    29           164            57                 1                2   \n",
      "\n",
      "      Income (USD)  \n",
      "0            75000  \n",
      "1            45000  \n",
      "2           120000  \n",
      "3            90000  \n",
      "4            35000  \n",
      "..             ...  \n",
      "126          60000  \n",
      "127          90000  \n",
      "128          55000  \n",
      "129          80000  \n",
      "130          65000  \n",
      "\n",
      "[131 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1d391f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0\n",
      "1      0\n",
      "2      1\n",
      "3      0\n",
      "4      1\n",
      "      ..\n",
      "126    1\n",
      "127    0\n",
      "128    1\n",
      "129    0\n",
      "130    1\n",
      "Name:  Gender, Length: 131, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "372a9748",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split Data Train and Test\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c18f68b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(131, 6) (104, 6) (27, 6)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape ,X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "126f05aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(131,) (104,) (27,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape ,Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "94efe0b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logistic Regression \n",
    "model=LogisticRegression()\n",
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "67795ec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data 1.0\n"
     ]
    }
   ],
   "source": [
    "#Accuracy on Train Data\n",
    "X_train_predict=model.predict(X_train)\n",
    "X_train_score=accuracy_score(X_train_predict,Y_train)\n",
    "print(\"Accuracy on train data {}\".format(X_train_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "598754db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data 1.0\n"
     ]
    }
   ],
   "source": [
    "#Accuracy on Test Data\n",
    "X_test_predict=model.predict(X_test)\n",
    "X_test_score=accuracy_score(X_test_predict,Y_test)\n",
    "print(\"Accuracy on test data {}\".format(X_test_score))"
   ]
  }
 ],
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