Gender Classification Using Sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d2cdeaba",
   "metadata": {},
   "source": [
    "# Gender Classification Using Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39d61e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing liraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e64fde50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c4c1797",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.ensemble import VotingClassifier,RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2867a0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2307246d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"L:\\Gender classification\\gender_classification_v7.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "939a2999",
   "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>long_hair</th>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>15.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>11.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>14.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>14.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>15.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>11.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>14.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
       "0           1               11.8                 6.1          1          0   \n",
       "1           0               14.0                 5.4          0          0   \n",
       "2           0               11.8                 6.3          1          1   \n",
       "3           0               14.4                 6.1          0          1   \n",
       "4           1               13.5                 5.9          0          0   \n",
       "5           1               13.0                 6.8          1          1   \n",
       "6           1               15.3                 6.2          1          1   \n",
       "7           0               13.0                 5.2          0          0   \n",
       "8           1               11.9                 5.4          1          0   \n",
       "9           1               12.1                 5.4          0          0   \n",
       "10          0               12.5                 5.4          1          1   \n",
       "11          1               15.5                 5.8          1          1   \n",
       "12          0               14.7                 5.2          1          1   \n",
       "13          1               14.5                 6.7          0          1   \n",
       "14          1               14.2                 6.5          0          0   \n",
       "15          0               12.5                 5.2          1          1   \n",
       "16          1               15.2                 6.0          1          1   \n",
       "17          1               14.0                 5.9          0          0   \n",
       "18          1               11.9                 5.2          0          0   \n",
       "19          1               14.4                 5.6          1          0   \n",
       "\n",
       "    lips_thin  distance_nose_to_lip_long  gender  \n",
       "0           1                          1    Male  \n",
       "1           1                          0  Female  \n",
       "2           1                          1    Male  \n",
       "3           1                          1    Male  \n",
       "4           0                          0  Female  \n",
       "5           1                          1    Male  \n",
       "6           1                          0    Male  \n",
       "7           0                          0  Female  \n",
       "8           1                          1  Female  \n",
       "9           0                          0  Female  \n",
       "10          1                          1    Male  \n",
       "11          1                          1    Male  \n",
       "12          1                          1    Male  \n",
       "13          1                          1    Male  \n",
       "14          0                          0  Female  \n",
       "15          1                          1    Male  \n",
       "16          1                          1    Male  \n",
       "17          0                          0  Female  \n",
       "18          0                          0  Female  \n",
       "19          1                          1    Male  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42e47db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "long_hair                    0\n",
       "forehead_width_cm            0\n",
       "forehead_height_cm           0\n",
       "nose_wide                    0\n",
       "nose_long                    0\n",
       "lips_thin                    0\n",
       "distance_nose_to_lip_long    0\n",
       "gender                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3987c882",
   "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>long_hair</th>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.869626</td>\n",
       "      <td>13.181484</td>\n",
       "      <td>5.946311</td>\n",
       "      <td>0.493901</td>\n",
       "      <td>0.507898</td>\n",
       "      <td>0.493101</td>\n",
       "      <td>0.498900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.336748</td>\n",
       "      <td>1.107128</td>\n",
       "      <td>0.541268</td>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.499988</td>\n",
       "      <td>0.500002</td>\n",
       "      <td>0.500049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         long_hair  forehead_width_cm  forehead_height_cm    nose_wide  \\\n",
       "count  5001.000000        5001.000000         5001.000000  5001.000000   \n",
       "mean      0.869626          13.181484            5.946311     0.493901   \n",
       "std       0.336748           1.107128            0.541268     0.500013   \n",
       "min       0.000000          11.400000            5.100000     0.000000   \n",
       "25%       1.000000          12.200000            5.500000     0.000000   \n",
       "50%       1.000000          13.100000            5.900000     0.000000   \n",
       "75%       1.000000          14.000000            6.400000     1.000000   \n",
       "max       1.000000          15.500000            7.100000     1.000000   \n",
       "\n",
       "         nose_long    lips_thin  distance_nose_to_lip_long  \n",
       "count  5001.000000  5001.000000                5001.000000  \n",
       "mean      0.507898     0.493101                   0.498900  \n",
       "std       0.499988     0.500002                   0.500049  \n",
       "min       0.000000     0.000000                   0.000000  \n",
       "25%       0.000000     0.000000                   0.000000  \n",
       "50%       1.000000     0.000000                   0.000000  \n",
       "75%       1.000000     1.000000                   1.000000  \n",
       "max       1.000000     1.000000                   1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "048d1ac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='forehead_width_cm', ylabel='forehead_height_cm'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising forehead length data\n",
    "sns.lineplot(data['forehead_width_cm'],data['forehead_height_cm'], hue=data[\"gender\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f8048ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='nose_long', ylabel='nose_wide'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising nose length data\n",
    "sns.lineplot(data['nose_long'],data['nose_wide'], hue=data[\"gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3b02b369",
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding data\n",
    "twogender = {'Female':0, 'Male':1}\n",
    "data['gender'] = data['gender'].map(twogender)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "84f685ba",
   "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>long_hair</th>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.4</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>11.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>5.9</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",
       "</div>"
      ],
      "text/plain": [
       "   long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
       "0          1               11.8                 6.1          1          0   \n",
       "1          0               14.0                 5.4          0          0   \n",
       "2          0               11.8                 6.3          1          1   \n",
       "3          0               14.4                 6.1          0          1   \n",
       "4          1               13.5                 5.9          0          0   \n",
       "\n",
       "   lips_thin  distance_nose_to_lip_long  gender  \n",
       "0          1                          1       1  \n",
       "1          1                          0       0  \n",
       "2          1                          1       1  \n",
       "3          1                          1       1  \n",
       "4          0                          0       0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "34d2a9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('gender', axis=1)\n",
    "y = data['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0c8956d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting data for testing and traing process\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8f4b2af7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3500, 7)\n",
      "(1501, 7)\n",
      "(3500,)\n",
      "(1501,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_val.shape)\n",
    "print(y_train.shape)\n",
    "print(y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e150f63",
   "metadata": {},
   "source": [
    "# trying diffrent methods to find best accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4c310606",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, plot_confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1bd60513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree is: 96.87%\n"
     ]
    }
   ],
   "source": [
    "#Decision tree-classifier\n",
    "dt = DecisionTreeClassifier(random_state=0)\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "dt_pred = dt.predict(X_val)\n",
    "dt_acc = accuracy_score(y_val, dt_pred)\n",
    "print('Accuracy of Decision Tree is: {:.2f}%'.format(dt_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ebb8d03b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random Forest is: 97.53%\n"
     ]
    }
   ],
   "source": [
    "#randomforestClassifier \n",
    "rf = RandomForestClassifier(random_state=0)\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "rf_pred = rf.predict(X_val)\n",
    "rf_acc = accuracy_score(y_val, rf_pred)\n",
    "print('Accuracy of Random Forest is: {:.2f}%'.format(rf_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "10669950",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression is: 97.27%\n"
     ]
    }
   ],
   "source": [
    "#logistic regression\n",
    "lr = LogisticRegression(random_state=0)\n",
    "\n",
    "lr.fit(X_train, y_train)\n",
    "lr_pred = lr.predict(X_val)\n",
    "lr_acc = accuracy_score(y_val, lr_pred)\n",
    "print('Accuracy of Logistic Regression is: {:.2f}%'.format(lr_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "49710d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 7}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k nearest neighbour\n",
    "knn = KNeighborsClassifier()\n",
    "params = {'n_neighbors':[2,3,4,5,6,7,8,9]}\n",
    "\n",
    "model = GridSearchCV(knn, params, cv=5)\n",
    "model.fit(X_train, y_train)\n",
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "40b105c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNeighbors is: 97.20%\n"
     ]
    }
   ],
   "source": [
    "kn = KNeighborsClassifier(n_neighbors=8)\n",
    "\n",
    "kn.fit(X_train, y_train)\n",
    "kn_pred = kn.predict(X_val)\n",
    "kn_acc = accuracy_score(y_val, kn_pred)\n",
    "print('Accuracy of KNeighbors is: {:.2f}%'.format(kn_acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0283abd7",
   "metadata": {},
   "source": [
    "# Accuracy of Decision Tree is: 96.87%\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64583e7",
   "metadata": {},
   "source": [
    "# Accuracy of Random Forest is: 97.53%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9236d1fa",
   "metadata": {},
   "source": [
    "# Accuracy of Logistic Regression is: 97.27%\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02cf391f",
   "metadata": {},
   "source": [
    "\n",
    "# Accuracy of KNeighbors is: 97.20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7ad212e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#so random forest gave best results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3402bf56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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