KNN iphone purchase records prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c5d5bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d856ff9c",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Salary  Purchase Iphone\n",
       "0      Male   19   19000                0\n",
       "1      Male   35   20000                0\n",
       "2    Female   26   43000                0\n",
       "3    Female   27   57000                0\n",
       "4      Male   19   76000                0\n",
       "..      ...  ...     ...              ...\n",
       "395  Female   46   41000                1\n",
       "396    Male   51   23000                1\n",
       "397  Female   50   20000                1\n",
       "398    Male   36   33000                0\n",
       "399  Female   49   36000                1\n",
       "\n",
       "[400 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:/Users/Sathya/Desktop/Data science assignments/27 November/KNN Assignment/iphone_purchase_records.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1c0d7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9614f8aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender             0\n",
       "Age                0\n",
       "Salary             0\n",
       "Purchase Iphone    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ad24487",
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.655000</td>\n",
       "      <td>69742.500000</td>\n",
       "      <td>0.357500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.482877</td>\n",
       "      <td>34096.960282</td>\n",
       "      <td>0.479864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.750000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>88000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              Age         Salary  Purchase Iphone\n",
       "count  400.000000     400.000000       400.000000\n",
       "mean    37.655000   69742.500000         0.357500\n",
       "std     10.482877   34096.960282         0.479864\n",
       "min     18.000000   15000.000000         0.000000\n",
       "25%     29.750000   43000.000000         0.000000\n",
       "50%     37.000000   70000.000000         0.000000\n",
       "75%     46.000000   88000.000000         1.000000\n",
       "max     60.000000  150000.000000         1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "684186d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Gender           400 non-null    object\n",
      " 1   Age              400 non-null    int64 \n",
      " 2   Salary           400 non-null    int64 \n",
      " 3   Purchase Iphone  400 non-null    int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 12.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96c879cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.155238</td>\n",
       "      <td>0.622454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.155238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchase Iphone</th>\n",
       "      <td>0.622454</td>\n",
       "      <td>0.362083</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                      Age    Salary  Purchase Iphone\n",
       "Age              1.000000  0.155238         0.622454\n",
       "Salary           0.155238  1.000000         0.362083\n",
       "Purchase Iphone  0.622454  0.362083         1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
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   "execution_count": 10,
   "id": "0dcb1626",
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   "outputs": [
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       "<AxesSubplot:>"
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     "execution_count": 10,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74f65257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age', 'Salary', 'Purchase Iphone'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "89c7018d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Age'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "sns.boxplot(y='Age',color='red',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05f79206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Salary'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.boxplot(y='Salary',color='yellow',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1602ed07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x209c2e84f10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='Age',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9111868b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x209c1bc4ee0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='Salary',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "98e4aa46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x209c18f9cd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='Salary',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee300c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    204\n",
       "Male      196\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7df95644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35    32\n",
       "37    20\n",
       "26    16\n",
       "41    16\n",
       "42    16\n",
       "39    15\n",
       "40    15\n",
       "48    14\n",
       "47    14\n",
       "27    13\n",
       "38    13\n",
       "28    12\n",
       "36    12\n",
       "46    12\n",
       "31    11\n",
       "30    11\n",
       "49    10\n",
       "29    10\n",
       "33     9\n",
       "32     9\n",
       "24     9\n",
       "59     7\n",
       "19     7\n",
       "45     7\n",
       "20     7\n",
       "60     7\n",
       "34     6\n",
       "23     6\n",
       "52     6\n",
       "25     6\n",
       "58     6\n",
       "53     5\n",
       "57     5\n",
       "22     5\n",
       "18     5\n",
       "21     4\n",
       "50     4\n",
       "54     4\n",
       "55     3\n",
       "56     3\n",
       "51     3\n",
       "43     3\n",
       "44     2\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30e7875b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72000     12\n",
       "80000     11\n",
       "79000     10\n",
       "75000      9\n",
       "71000      9\n",
       "          ..\n",
       "123000     1\n",
       "37000      1\n",
       "115000     1\n",
       "148000     1\n",
       "139000     1\n",
       "Name: Salary, Length: 117, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Salary.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9457d312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    257\n",
       "1    143\n",
       "Name: Purchase Iphone, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Purchase Iphone'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4fbceaa9",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38.411765</td>\n",
       "      <td>71759.803922</td>\n",
       "      <td>0.377451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>36.867347</td>\n",
       "      <td>67642.857143</td>\n",
       "      <td>0.336735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Salary  Purchase Iphone\n",
       "Gender                                          \n",
       "Female  38.411765  71759.803922         0.377451\n",
       "Male    36.867347  67642.857143         0.336735"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f47482f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='Age'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "sns.boxplot(x='Gender',y='Age',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "621e8d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='Salary'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LB4yZmWXhgDEzsywcMGZmlkUpASPpfZLWStoh6UVJl0iaKGm9pJ3p+aya9ZdJqkh6SdIVNfWZkramZXdIUqqPlfRQqm+S1F7CbpqZndLKGsHcDvxjRFwA/CrwInA9sCEipgEb0nskXQh0AdOBucCdklrSdu4CFgHT0mNuqi8EDkREB8VPO9/SjJ0yM7Ojmh4wksYDlwL3AETEOxHxI2AesDqtthq4Kr2eBzwYEYciYhdQAWZJmgyMj4in0s8I3FfXprqttcCc6ujGzMyao4wRzAeAXuCvJH1H0t2S3gtMioi9AOn53LR+G/BqTfueVGtLr+vrfdpExGGKH0c7u74jkhZJ6pbU3dvbO1L7Z2ZmlBMwY4APAndFxMXAT0mHw/rRaOQRA9QHatO3ELEyIjojorO1tXXgXpuZ2TEpI2B6gJ6I2JTer6UInNfTYS/S876a9afWtJ8CvJbqUxrU+7SRNAaYAOwf8T0xM7N+NT1gIuL7wKuSfjmV5gDbgXXAglRbADycXq8DutKZYedTTOZvTofRDkqaneZXrq1rU93WfGBjzc89m5lZE5R1u/4lwFcl/QLwPeD3KMJujaSFwG7gaoCI2CZpDUUIHQYWR8SRtJ3rgHuBccCj6QHFCQT3S6pQjFy6mrFTZmZ2VCkBExHPAZ0NFs3pZ/3lwPIG9W5gRoP626SAMjOzcvhKfjMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRZl/aKlmTXZ7p+0cPOW8WV3o3Svv1n8XT3pjJ+X3JPy7f5JC9Mybt8BY3YK6OjoKLsLo8Y7lQoAY9/v/ybTyPvdcMCMoJY39zNuxyNld6N0p739YwB+frr/Wm55cz8wqexusGTJkrK7MGosXboUgNtvv73knpz8HDAjxH8hHlWpHASg4wPl/4+1fJP83bBTVmkBI6kF6Ab2RMQnJU0EHgLagZeBfx8RB9K6y4CFwBHgcxHxWKrPBO4FxgGPAEsjIiSNBe4DZgI/BD4VES/n3B//hXiU/0I0Myj3LLKlwIs1768HNkTENGBDeo+kC4EuYDowF7gzhRPAXcAiikOJ09JyKMLoQER0ALcBt+TdFTMzq1dKwEiaAnwCuLumPA9YnV6vBq6qqT8YEYciYhdQAWZJmgyMj4inIiIoRixXNdjWWmCOJGXaHTMza6CsEcz/AD4P1J4nOCki9gKk53NTvQ14tWa9nlRrS6/r633aRMRh4A3g7PpOSFokqVtSd29v73HukpmZ1Wp6wEj6JLAvIp4dapMGtRigPlCbvoWIlRHRGRGdra2tQ+yOmZkNRRmT/B8FrpT0ceB0YLykvwZelzQ5Ivamw1/70vo9wNSa9lOA11J9SoN6bZseSWOACcD+XDtkZmbv1vQRTEQsi4gpEdFOMXm/MSI+DawDFqTVFgAPp9frgC5JYyWdTzGZvzkdRjsoaXaaX7m2rk11W/PTZ7xrBGNmZvmMputgvgSskbQQ2A1cDRAR2yStAbYDh4HFEXEktbmOo6cpP5oeAPcA90uqUIxcupq1E2ZmVig1YCLiCeCJ9PqHwJx+1lsOLG9Q7wZmNKi/TQooMzMrh++mbGZmWThgzMwsCweMmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7Msmh4wkqZK+qakFyVtk7Q01SdKWi9pZ3o+q6bNMkkVSS9JuqKmPlPS1rTsDklK9bGSHkr1TZLam72fZmanujJGMIeB/xwRvwLMBhZLuhC4HtgQEdOADek9aVkXMB2YC9wpqSVt6y5gETAtPeam+kLgQER0ALcBtzRjx8zM7KimB0xE7I2ILen1QeBFoA2YB6xOq60Grkqv5wEPRsShiNgFVIBZkiYD4yPiqYgI4L66NtVtrQXmVEc3ZmbWHKXOwaRDVxcDm4BJEbEXihACzk2rtQGv1jTrSbW29Lq+3qdNRBwG3gDOzrITZmbWUGkBI+lM4GvAf4yIHw+0aoNaDFAfqE19HxZJ6pbU3dvbO1iXzczsGJQSMJLeQxEuX42Ir6fy6+mwF+l5X6r3AFNrmk8BXkv1KQ3qfdpIGgNMAPbX9yMiVkZEZ0R0tra2jsSumZlZUsZZZALuAV6MiL+oWbQOWJBeLwAerql3pTPDzqeYzN+cDqMdlDQ7bfPaujbVbc0HNqZ5GjMza5IxJXzmR4H/AGyV9FyqfQH4ErBG0kJgN3A1QERsk7QG2E5xBtriiDiS2l0H3AuMAx5NDygC7H5JFYqRS1fmfTIzszpND5iI+Gcaz5EAzOmnzXJgeYN6NzCjQf1tUkCZmVk5fCW/mZllIU9NFDo7O6O7u7vsbhy3FStWUKlUSu1D9fM7OjpK7Ue1D0uWLCm7G8bo+G7C6Pl+nizfTUnPRkRno2VlzMHYSW7cuHFld8GsX/5+No9HMMnJMoIxM2umgUYwnoMxM7MsHDBmZpaFA8bMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4QstE0m9wCtl9+Mkcg7wg7I7YdYPfz9HzvsjouEPajlgLAtJ3f1d3WtWNn8/m8OHyMzMLAsHjJmZZeGAsVxWlt0BswH4+9kEnoMxM7MsPIIxM7MsHDBmZpaFA8aGTFJIur/m/RhJvZL+YZB2lw22jtlQSDoi6bmaR3vGz3pZ0jm5tn8q8E8m27H4KTBD0riIeAv4TWBPyX2yU8tbEfFrZXfChsYjGDtWjwKfSK+vAR6oLpA0S9K3JX0nPf9yfWNJ75W0StIzab15Teq3naQkzZT0T5KelfSYpMmp/oSk2yQ9KelFSR+S9HVJOyX9t5r2f5fabpO0qJ/P+LSkzWnU9BVJLc3avxOZA8aO1YNAl6TTgYuATTXLdgCXRsTFwB8DNzdo/0VgY0R8CPgYcKuk92bus508xtUcHvuGpPcAK4D5ETETWAUsr1n/nYi4FPgy8DCwGJgBfEbS2Wmdz6a2ncDnauoASPoV4FPAR9Po6Qjwu/l28eThQ2R2TCLi+XTc+xrgkbrFE4DVkqYBAbynwSYuB66U9Ifp/enAecCLeXpsJ5k+h8gkzaAIjPWSAFqAvTXrr0vPW4FtEbE3tfseMBX4IUWo/FZabyowLdWr5gAzgWfSZ4wD9o3oXp2kHDA2HOuAPwMuA2r/2rsJ+GZE/FYKoScatBXwOxHxUuY+2qlBFMFxST/LD6Xnn9e8rr4fI+ky4DeASyLiTUlPUPzRU/8ZqyNi2Uh1+lThQ2Q2HKuAGyNia119Akcn/T/TT9vHgCVKfwpKujhLD+1U8RLQKukSAEnvkTT9GNpPAA6kcLkAmN1gnQ3AfEnnps+YKOn9x9vxU4EDxo5ZRPRExO0NFv0p8N8lfYviUEUjN1EcOnte0gvpvdmwRMQ7wHzgFknfBZ4DPnIMm/hHipHM8xTfxacbfMZ24L8Aj6f11gOTj7PrpwTfKsbMzLLwCMbMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMWWaSJkn6G0nfS/e8eqrmyvHj2a7vUm2jmgPGLKN0QenfAU9GxAfSPa+6gCkl9MV37rCmcsCY5fXrFDdc/HK1EBGvRMQKSS2Sbk13ln5e0u/D/x+ZPCFpraQdkr5ac+eDuan2z8BvV7fZ312qJX1G0t9K+nvg8abuuZ3y/BeNWV7TgS39LFsIvBERH5I0FviWpGoIXJzavgZ8C/iopG7gLylCqwI8VLOt6l2qPyvpfcBmSf8nLbsEuCgi9o/gfpkNygFj1kSS/hfwb4B3gFeAiyTNT4snUNzJ9x1gc0T0pDbPAe3AT4BdEbEz1f8aqP5+SX93qQZY73CxMjhgzPLaBvxO9U1ELE4/w9sN7AaWRMRjtQ3SHX5r7/x7hKP/Vvu7t1PDu1RL+jDFL5GaNZ3nYMzy2gicLum6mtoZ6fkx4Lr0o1lI+teD/PjaDuB8Sb+U3l9Ts8x3qbZRxwFjllEUd5O9Cvi3knZJ2gysBv4IuBvYDmxJd5b+CgMcVYiItykOif3vNMn/Ss1i36XaRh3fTdnMzLLwCMbMzLJwwJiZWRYOGDMzy8IBY2ZmWThgzMwsCweMmZll4YAxM7Ms/h9JGg8rixuwXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Gender',y='Salary',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1ce7a0a5",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>84000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>65000</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>392</th>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>45000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>59000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Salary  Purchase Iphone\n",
       "2    Female   26   43000                0\n",
       "3    Female   27   57000                0\n",
       "6    Female   27   84000                0\n",
       "7    Female   32  150000                1\n",
       "9    Female   35   65000                0\n",
       "..      ...  ...     ...              ...\n",
       "392  Female   45   45000                1\n",
       "394  Female   39   59000                0\n",
       "395  Female   46   41000                1\n",
       "397  Female   50   20000                1\n",
       "399  Female   49   36000                1\n",
       "\n",
       "[204 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female = df[df['Gender']=='Female']\n",
    "female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e31a10fa",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>58000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>33000</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>390</th>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>33000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>42000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Salary  Purchase Iphone\n",
       "0     Male   19   19000                0\n",
       "1     Male   35   20000                0\n",
       "4     Male   19   76000                0\n",
       "5     Male   27   58000                0\n",
       "8     Male   25   33000                0\n",
       "..     ...  ...     ...              ...\n",
       "390   Male   48   33000                1\n",
       "391   Male   47   23000                1\n",
       "393   Male   60   42000                1\n",
       "396   Male   51   23000                1\n",
       "398   Male   36   33000                0\n",
       "\n",
       "[196 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = df[df['Gender']=='Male']\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "86c1d6b0",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>204.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>204.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.411765</td>\n",
       "      <td>71759.803922</td>\n",
       "      <td>0.377451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.859019</td>\n",
       "      <td>35595.236189</td>\n",
       "      <td>0.485942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>44000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.500000</td>\n",
       "      <td>70500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>90750.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age         Salary  Purchase Iphone\n",
       "count  204.000000     204.000000       204.000000\n",
       "mean    38.411765   71759.803922         0.377451\n",
       "std     10.859019   35595.236189         0.485942\n",
       "min     18.000000   15000.000000         0.000000\n",
       "25%     30.000000   44000.000000         0.000000\n",
       "50%     37.500000   70500.000000         0.000000\n",
       "75%     47.000000   90750.000000         1.000000\n",
       "max     60.000000  150000.000000         1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1c10012e",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>196.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>196.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.867347</td>\n",
       "      <td>67642.857143</td>\n",
       "      <td>0.336735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.043636</td>\n",
       "      <td>32421.819181</td>\n",
       "      <td>0.473804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.500000</td>\n",
       "      <td>68000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>86000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age         Salary  Purchase Iphone\n",
       "count  196.000000     196.000000       196.000000\n",
       "mean    36.867347   67642.857143         0.336735\n",
       "std     10.043636   32421.819181         0.473804\n",
       "min     18.000000   15000.000000         0.000000\n",
       "25%     29.000000   43000.000000         0.000000\n",
       "50%     36.500000   68000.000000         0.000000\n",
       "75%     42.000000   86000.000000         1.000000\n",
       "max     60.000000  150000.000000         1.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "633725c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    127\n",
       "1     77\n",
       "Name: Purchase Iphone, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female['Purchase Iphone'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c1032d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.745098039215684"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(77*100)/204"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f23c92c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    130\n",
       "1     66\n",
       "Name: Purchase Iphone, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male['Purchase Iphone'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a88cdd9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.673469387755105"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(66*100)/196"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a6c654d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26, 27, 32, 35, 48, 47, 45, 21, 28, 33, 31, 24, 18, 29, 23, 59, 22,\n",
       "       20, 30, 19, 38, 39, 37, 36, 40, 41, 34, 58, 55, 52, 46, 60, 42, 53,\n",
       "       50, 56, 51, 57, 44, 43, 49, 54], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "da2e22be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 35, 27, 25, 20, 32, 18, 29, 47, 45, 46, 48, 49, 31, 30, 23, 28,\n",
       "       22, 24, 34, 39, 26, 21, 38, 37, 42, 40, 36, 41, 33, 52, 59, 60, 56,\n",
       "       57, 55, 58, 53, 54, 43, 51], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male['Age'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bee4cb8",
   "metadata": {},
   "source": [
    "# Model_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "28d6a67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age', 'Salary', 'Purchase Iphone'], dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d63a1102",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Age', 'Salary']]\n",
    "y = df[['Purchase Iphone']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "05a4227b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.neighbors as sn\n",
    "import sklearn.metrics as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8a1821d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k= 1 [0.995]\n",
      "k= 2 [0.995, 0.9]\n",
      "k= 3 [0.995, 0.9, 0.8975]\n",
      "k= 4 [0.995, 0.9, 0.8975, 0.8625]\n",
      "k= 5 [0.995, 0.9, 0.8975, 0.8625, 0.8725]\n",
      "k= 6 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625]\n",
      "k= 7 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87]\n",
      "k= 8 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475]\n",
      "k= 9 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725]\n",
      "k= 10 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475]\n",
      "k= 11 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325]\n",
      "k= 12 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825]\n",
      "k= 13 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845]\n",
      "k= 14 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845, 0.8425]\n",
      "k= 15 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845, 0.8425, 0.8325]\n",
      "k= 16 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845, 0.8425, 0.8325, 0.8325]\n",
      "k= 17 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845, 0.8425, 0.8325, 0.8325, 0.83]\n",
      "k= 18 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845, 0.8425, 0.8325, 0.8325, 0.83, 0.815]\n",
      "k= 19 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845, 0.8425, 0.8325, 0.8325, 0.83, 0.815, 0.805]\n",
      "k= 20 [0.995, 0.9, 0.8975, 0.8625, 0.8725, 0.8625, 0.87, 0.8475, 0.8725, 0.8475, 0.8325, 0.825, 0.845, 0.8425, 0.8325, 0.8325, 0.83, 0.815, 0.805, 0.8175]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "acc=[]\n",
    "for i in range(1,21):\n",
    "    knn=sn.KNeighborsClassifier(n_neighbors=i)\n",
    "    model= knn.fit(x,y)\n",
    "    pred_y = model.predict(x)\n",
    "    acc.append(sm.accuracy_score(pred_y,y))\n",
    "    print('k=',i,acc)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e1cd1e33",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "max(acc)\n",
    "0.995\n",
    "k=acc.index(max(acc))+1\n",
    "k\n",
    "1\n",
    "knn=sn.KNeighborsClassifier(n_neighbors=1)\n",
    "model= knn.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b706dcd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x209c4ee5e80>]"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.plot(range(1,21),acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61bb8d8",
   "metadata": {},
   "source": [
    "# Model_2(With Gender maping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "91d5a043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    0.377451\n",
       "Male      0.336735\n",
       "Name: Purchase Iphone, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=df.groupby('Gender')['Purchase Iphone'].mean()\n",
    "m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3d317fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7141856742697079"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q=m.sum()\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0e5d5211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    52.850539\n",
       "Male      47.149461\n",
       "Name: Purchase Iphone, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(m/q)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "364adb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Female':52.850539,'Male':47.149461}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "16cc08a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gm']=df['Gender'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "32384b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Age', 'Salary','gm']]\n",
    "y = df[['Purchase Iphone']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7f6dfe83",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k= 1 [0.9975]\n",
      "k= 2 [0.9975, 0.8975]\n",
      "k= 3 [0.9975, 0.8975, 0.8975]\n",
      "k= 4 [0.9975, 0.8975, 0.8975, 0.8625]\n",
      "k= 5 [0.9975, 0.8975, 0.8975, 0.8625, 0.875]\n",
      "k= 6 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625]\n",
      "k= 7 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87]\n",
      "k= 8 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855]\n",
      "k= 9 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865]\n",
      "k= 10 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475]\n",
      "k= 11 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835]\n",
      "k= 12 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275]\n",
      "k= 13 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275, 0.8425]\n",
      "k= 14 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275, 0.8425, 0.8475]\n",
      "k= 15 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275, 0.8425, 0.8475, 0.835]\n",
      "k= 16 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275, 0.8425, 0.8475, 0.835, 0.8325]\n",
      "k= 17 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275, 0.8425, 0.8475, 0.835, 0.8325, 0.83]\n",
      "k= 18 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275, 0.8425, 0.8475, 0.835, 0.8325, 0.83, 0.815]\n",
      "k= 19 [0.9975, 0.8975, 0.8975, 0.8625, 0.875, 0.8625, 0.87, 0.855, 0.865, 0.8475, 0.835, 0.8275, 0.8425, 0.8475, 0.835, 0.8325, 0.83, 0.815, 0.805]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "acc=[]\n",
    "for i in range(1,20):\n",
    "    knn=sn.KNeighborsClassifier(n_neighbors=i)\n",
    "    model= knn.fit(x,y)\n",
    "    pred_y = model.predict(x)\n",
    "    acc.append(sm.accuracy_score(pred_y,y))\n",
    "    print('k=',i,acc)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "56944fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "max(acc)\n",
    "0.9975\n",
    "k=acc.index(max(acc))+1\n",
    "k\n",
    "1\n",
    "knn=sn.KNeighborsClassifier(n_neighbors=1)\n",
    "model= knn.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "27e785c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x209c4f49070>]"
      ]
     },
     "execution_count": 50,
     "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": [
    "plt.plot(range(1,20),acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9078317",
   "metadata": {},
   "source": [
    "Here i have mapped the male and female with percentage of total average of purchase iphone. but it is not nacessary to do this way in kkn method. you can map male and female any 2 different number. basically mapping does that add one more dimension and categorise male and female into number so we can calculate distance from those point to new data point. if you choose mapping with same number, you will get same result as model_1. try this"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6606ddac",
   "metadata": {},
   "source": [
    "# Model_2more accurate because accuracy score of model_2 is closer to 1 for k=1\n",
    "\n",
    "Gender Wise model\n",
    "\n",
    "Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bff6e2f2",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>84000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>65000</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>392</th>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>45000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>59000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Salary  Purchase Iphone\n",
       "2    Female   26   43000                0\n",
       "3    Female   27   57000                0\n",
       "6    Female   27   84000                0\n",
       "7    Female   32  150000                1\n",
       "9    Female   35   65000                0\n",
       "..      ...  ...     ...              ...\n",
       "392  Female   45   45000                1\n",
       "394  Female   39   59000                0\n",
       "395  Female   46   41000                1\n",
       "397  Female   50   20000                1\n",
       "399  Female   49   36000                1\n",
       "\n",
       "[204 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "32945461",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = female[['Age', 'Salary']]\n",
    "y = female[['Purchase Iphone']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0f783a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k= 1 [0.9950980392156863]\n",
      "k= 2 [0.9950980392156863, 0.8774509803921569]\n",
      "k= 3 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294]\n",
      "k= 4 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882]\n",
      "k= 5 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471]\n",
      "k= 6 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058]\n",
      "k= 7 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882]\n",
      "k= 8 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334]\n",
      "k= 9 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921]\n",
      "k= 10 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373]\n",
      "k= 11 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058]\n",
      "k= 12 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058]\n",
      "k= 13 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373]\n",
      "k= 14 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373, 0.7990196078431373]\n",
      "k= 15 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373, 0.7990196078431373, 0.7990196078431373]\n",
      "k= 16 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373, 0.7990196078431373, 0.7990196078431373, 0.8137254901960784]\n",
      "k= 17 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373, 0.7990196078431373, 0.7990196078431373, 0.8137254901960784, 0.7990196078431373]\n",
      "k= 18 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373, 0.7990196078431373, 0.7990196078431373, 0.8137254901960784, 0.7990196078431373, 0.7990196078431373]\n",
      "k= 19 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373, 0.7990196078431373, 0.7990196078431373, 0.8137254901960784, 0.7990196078431373, 0.7990196078431373, 0.7892156862745098]\n",
      "k= 20 [0.9950980392156863, 0.8774509803921569, 0.8676470588235294, 0.8529411764705882, 0.8382352941176471, 0.8235294117647058, 0.8529411764705882, 0.8333333333333334, 0.8186274509803921, 0.7990196078431373, 0.8235294117647058, 0.8235294117647058, 0.7990196078431373, 0.7990196078431373, 0.7990196078431373, 0.8137254901960784, 0.7990196078431373, 0.7990196078431373, 0.7892156862745098, 0.7941176470588235]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "acc = []\n",
    "for i in range(1,21):\n",
    "    knn=sn.KNeighborsClassifier(n_neighbors=i)\n",
    "    model=knn.fit(x,y)\n",
    "    predy=model.predict(x)\n",
    "    acc.append(sm.accuracy_score(predy,y))\n",
    "    print('k=',i,acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2bf63c68",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "max(acc)\n",
    "0.9950980392156863\n",
    "k = acc.index(max(acc))+1\n",
    "k\n",
    "1\n",
    "knn=sn.KNeighborsClassifier(n_neighbors=1)\n",
    "model=knn.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7f29ad90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x209c4f8e550>]"
      ]
     },
     "execution_count": 55,
     "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": [
    "plt.plot(range(1,21),acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02b43ef",
   "metadata": {},
   "source": [
    "# Male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5ef81f81",
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>58000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>33000</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>390</th>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>33000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>42000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Salary  Purchase Iphone\n",
       "0     Male   19   19000                0\n",
       "1     Male   35   20000                0\n",
       "4     Male   19   76000                0\n",
       "5     Male   27   58000                0\n",
       "8     Male   25   33000                0\n",
       "..     ...  ...     ...              ...\n",
       "390   Male   48   33000                1\n",
       "391   Male   47   23000                1\n",
       "393   Male   60   42000                1\n",
       "396   Male   51   23000                1\n",
       "398   Male   36   33000                0\n",
       "\n",
       "[196 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f8a8bcb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = male[['Age', 'Salary']]\n",
    "y = male[['Purchase Iphone']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "28eabcfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k= 1 [1.0]\n",
      "k= 2 [1.0, 0.8979591836734694]\n",
      "k= 3 [1.0, 0.8979591836734694, 0.8673469387755102]\n",
      "k= 4 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898]\n",
      "k= 5 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898]\n",
      "k= 6 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571]\n",
      "k= 7 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837]\n",
      "k= 8 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041]\n",
      "k= 9 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898]\n",
      "k= 10 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898]\n",
      "k= 11 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449]\n",
      "k= 12 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918]\n",
      "k= 13 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714]\n",
      "k= 14 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714, 0.8061224489795918]\n",
      "k= 15 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714, 0.8061224489795918, 0.7908163265306123]\n",
      "k= 16 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714, 0.8061224489795918, 0.7908163265306123, 0.8163265306122449]\n",
      "k= 17 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714, 0.8061224489795918, 0.7908163265306123, 0.8163265306122449, 0.8112244897959183]\n",
      "k= 18 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714, 0.8061224489795918, 0.7908163265306123, 0.8163265306122449, 0.8112244897959183, 0.8214285714285714]\n",
      "k= 19 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714, 0.8061224489795918, 0.7908163265306123, 0.8163265306122449, 0.8112244897959183, 0.8214285714285714, 0.8112244897959183]\n",
      "k= 20 [1.0, 0.8979591836734694, 0.8673469387755102, 0.826530612244898, 0.8826530612244898, 0.8571428571428571, 0.8622448979591837, 0.8469387755102041, 0.826530612244898, 0.826530612244898, 0.8163265306122449, 0.8061224489795918, 0.8214285714285714, 0.8061224489795918, 0.7908163265306123, 0.8163265306122449, 0.8112244897959183, 0.8214285714285714, 0.8112244897959183, 0.826530612244898]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "acc = []\n",
    "for i in range(1,21):\n",
    "    knn=sn.KNeighborsClassifier(n_neighbors=i)\n",
    "    model=knn.fit(x,y)\n",
    "    predy=model.predict(x)\n",
    "    acc.append(sm.accuracy_score(predy,y))\n",
    "    print('k=',i,acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9a03a12f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sathya\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "max(acc)\n",
    "1.0\n",
    "k = acc.index(max(acc))+1\n",
    "k\n",
    "1\n",
    "knn=sn.KNeighborsClassifier(n_neighbors=1)\n",
    "model=knn.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2143f820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x209c4fd0ac0>]"
      ]
     },
     "execution_count": 60,
     "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": [
    "plt.plot(range(1,21),acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aed71c9c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}