I_Phone_Purchase_Project_using_KNN_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "725a3b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "## import python liabraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "269f43b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extract the data from database \n",
    "data=pd.read_csv(r\"C:\\Users\\pv11379\\OneDrive - Deere & Co\\Desktop\\Personal folder\\Data science\\Database/iphone_purchase_records.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "31ec490d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## copy data into new variable\n",
    "df=data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "02817622",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in data -  400\n",
      "Columns in data -  4\n"
     ]
    }
   ],
   "source": [
    "## shape of dataset\n",
    "print(\"Rows in data - \",df.shape[0])\n",
    "print(\"Columns in data - \",df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f2fb0481",
   "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>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## top 3 rows of dataset\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7c4be997",
   "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",
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       "    }\n",
       "\n",
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       "    }\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>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",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Salary  Purchase Iphone\n",
       "397  Female   50   20000                1\n",
       "398    Male   36   33000                0\n",
       "399  Female   49   36000                1"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## bottom 3 rows of dataset\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "31fef714",
   "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": [
    "## detail information of dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "19e22048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=400, step=1)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## index of dataset\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5f908c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age', 'Salary', 'Purchase Iphone'], dtype='object')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## columns in dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d836b527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>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",
       "</div>"
      ],
      "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": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## statistical analysis of dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ab0b5f",
   "metadata": {},
   "source": [
    "## average age - 37 years and average salary - 69742 Rs.\n",
    "## Min age - 18 yr, max age - 60Yr\n",
    "## Min salary - 15000Rs, max salary - 150000Rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d201258c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender               2\n",
      "Age                 43\n",
      "Salary             117\n",
      "Purchase Iphone      2\n",
      "dtype: int64\n",
      "Gender having unique values -  ['Male' 'Female']\n",
      "Purchase IPhone having unique values -  [0 1]\n"
     ]
    }
   ],
   "source": [
    "## number of unique values in dataset\n",
    "print(df.nunique())\n",
    "print(\"Gender having unique values - \",df['Gender'].unique())\n",
    "print(\"Purchase IPhone having unique values - \",df['Purchase Iphone'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa839d1e",
   "metadata": {},
   "source": [
    "## Gender having 2 unique values - Male and Female\n",
    "## Purchase Iphone having 2 unique values - 0(No), 1(yes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ce190347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender             object\n",
       "Age                 int64\n",
       "Salary              int64\n",
       "Purchase Iphone     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## data types in dataset\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3976563",
   "metadata": {},
   "source": [
    "## Gender column having object datatype and remaining others having integer datatype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1242f79f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender             0\n",
       "Age                0\n",
       "Salary             0\n",
       "Purchase Iphone    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## any null values in dataset\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79628982",
   "metadata": {},
   "source": [
    "## No null values in data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81446f06",
   "metadata": {},
   "source": [
    "# verify for outliers in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f51b1311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Salary', 'Purchase Iphone'], dtype='object')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## any outliers in dataset\n",
    "# numeric columns in data\n",
    "ncol= df.columns[df.dtypes!='O']\n",
    "ncol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f188513a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in ncol:\n",
    "    plt.figure()\n",
    "    sns.boxplot(x=df[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b3cd63",
   "metadata": {},
   "source": [
    "## In given dataset, outliers are not available. So it is free from outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "34de98a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    51.0\n",
       "Male      49.0\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "1ad40552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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al8tlIiMjzSOPPGKWLVt23TmzsrLM0KFDTVhYmPHz8zPh4eHmscceM0eOHHGvc+DAAdOvXz9To0YN4+fnZxo3bmxef/1199FI13uedMVRKvn5+WbYsGGmVq1axnEcI8l96LIkM2rUqGJnvfJ2Cr8vUlNTzcCBA81dd91lAgICzAMPPGB2797tcd2CggKTnJxs6tevb/z9/U3r1q1Nenp6kaNdjDHmgw8+ME2aNDF+fn4e91ncobaXLl0yr732mmnUqJHx8/MzNWvWNAMGDHAfnluoY8eOpnnz5kUe06BBg0xkZGSxjxcoaY4xxvgiegAAwJ2JfT4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWHXbvc9HQUGBDh8+rODgYK/fXRAAAPiGMUZ5eXkKDw9XhQrX3rZx28XH4cOHFRER4esxAADATcjKyrruGyPedvFR+DbWWVlZRd4JEgAA3J5yc3MVERFxQx9HcdvFR+FLLVWrViU+AAAoY25klwl2OAUAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVlXw9AP7pnhe+8PUIsGj/b3v5egQA8Am2fAAAAKuIDwAAYJVX8ZGYmCjHcTxOYWFh7suNMUpMTFR4eLgCAgLUqVMnbd++vcSHBgAAZZfXWz6aN2+u7Oxs92nbtm3uy5KTk5WSkqIZM2YoIyNDYWFh6t69u/Ly8kp0aAAAUHZ5HR+VKlVSWFiY+1SrVi1Jl7d6TJ8+XZMmTVLfvn0VExOjOXPm6OzZs5o3b16JDw4AAMomr+Nj9+7dCg8PV1RUlJ544gllZmZKkvbt26ecnBz16NHDva7L5VLHjh21du3aq95efn6+cnNzPU4AAKD88io+2rZtq7lz52rp0qX605/+pJycHHXo0EHHjx9XTk6OJCk0NNTjOqGhoe7LijNt2jSFhIS4TxERETfxMAAAQFnhVXzEx8fr4YcfVmxsrLp166Yvvrj8vhRz5sxxr+M4jsd1jDFFlv3cxIkTderUKfcpKyvLm5EAAEAZc0uH2gYGBio2Nla7d+92H/Vy5VaOo0ePFtka8nMul0tVq1b1OAEAgPLrluIjPz9fO3fuVJ06dRQVFaWwsDClpaW5Lz9//rxWrlypDh063PKgAACgfPDq7dWff/559e7dW3fffbeOHj2qV199Vbm5uRo0aJAcx9GYMWOUlJSk6OhoRUdHKykpSVWqVFG/fv1Ka34AAFDGeBUf33//vX7961/r2LFjqlWrltq1a6f169crMjJSkjRhwgSdO3dOCQkJOnnypNq2bavU1FQFBweXyvAAAKDscYwxxtdD/Fxubq5CQkJ06tSpO27/Dz5Y7s7CB8sBKE+8+fvNp9oCgAX8c3Fn4Z+La+OD5QAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFW3FB/Tpk2T4zgaM2aMe5kxRomJiQoPD1dAQIA6deqk7du33+qcAACgnLjp+MjIyNDs2bMVFxfnsTw5OVkpKSmaMWOGMjIyFBYWpu7duysvL++WhwUAAGXfTcXH6dOn1b9/f/3pT39StWrV3MuNMZo+fbomTZqkvn37KiYmRnPmzNHZs2c1b968EhsaAACUXTcVH6NGjVKvXr3UrVs3j+X79u1TTk6OevTo4V7mcrnUsWNHrV27ttjbys/PV25urscJAACUX5W8vcL8+fO1efNmZWRkFLksJydHkhQaGuqxPDQ0VAcOHCj29qZNm6bJkyd7OwYAACijvNrykZWVpeeee05/+ctf5O/vf9X1HMfxOG+MKbKs0MSJE3Xq1Cn3KSsry5uRAABAGePVlo9Nmzbp6NGjatWqlXvZpUuXtGrVKs2YMUO7du2SdHkLSJ06ddzrHD16tMjWkEIul0sul+tmZgcAAGWQV1s+unbtqm3btmnLli3uU+vWrdW/f39t2bJF9evXV1hYmNLS0tzXOX/+vFauXKkOHTqU+PAAAKDs8WrLR3BwsGJiYjyWBQYGqkaNGu7lY8aMUVJSkqKjoxUdHa2kpCRVqVJF/fr1K7mpAQBAmeX1DqfXM2HCBJ07d04JCQk6efKk2rZtq9TUVAUHB5f0XQEAgDLoluNjxYoVHucdx1FiYqISExNv9aYBAEA5xGe7AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABglVfx8dZbbykuLk5Vq1ZV1apV1b59ey1evNh9uTFGiYmJCg8PV0BAgDp16qTt27eX+NAAAKDs8io+6tWrp9/+9rfauHGjNm7cqC5duuihhx5yB0ZycrJSUlI0Y8YMZWRkKCwsTN27d1deXl6pDA8AAMoer+Kjd+/eeuCBB9SoUSM1atRIU6dOVVBQkNavXy9jjKZPn65Jkyapb9++iomJ0Zw5c3T27FnNmzevtOYHAABlzE3v83Hp0iXNnz9fZ86cUfv27bVv3z7l5OSoR48e7nVcLpc6duyotWvXXvV28vPzlZub63ECAADll9fxsW3bNgUFBcnlcmnEiBH661//qmbNmiknJ0eSFBoa6rF+aGio+7LiTJs2TSEhIe5TRESEtyMBAIAyxOv4aNy4sbZs2aL169dr5MiRGjRokHbs2OG+3HEcj/WNMUWW/dzEiRN16tQp9ykrK8vbkQAAQBlSydsrVK5cWQ0bNpQktW7dWhkZGfr973+v//zP/5Qk5eTkqE6dOu71jx49WmRryM+5XC65XC5vxwAAAGXULb/PhzFG+fn5ioqKUlhYmNLS0tyXnT9/XitXrlSHDh1u9W4AAEA54dWWjxdffFHx8fGKiIhQXl6e5s+frxUrVmjJkiVyHEdjxoxRUlKSoqOjFR0draSkJFWpUkX9+vUrrfkBAEAZ41V8HDlyRAMHDlR2drZCQkIUFxenJUuWqHv37pKkCRMm6Ny5c0pISNDJkyfVtm1bpaamKjg4uFSGBwAAZY9X8fHOO+9c83LHcZSYmKjExMRbmQkAAJRjfLYLAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWeRUf06ZNU5s2bRQcHKzatWvr3/7t37Rr1y6PdYwxSkxMVHh4uAICAtSpUydt3769RIcGAABll1fxsXLlSo0aNUrr169XWlqaLl68qB49eujMmTPudZKTk5WSkqIZM2YoIyNDYWFh6t69u/Ly8kp8eAAAUPZU8mblJUuWeJx/9913Vbt2bW3atEn33XefjDGaPn26Jk2apL59+0qS5syZo9DQUM2bN0/Dhw8vuckBAECZdEv7fJw6dUqSVL16dUnSvn37lJOTox49erjXcblc6tixo9auXVvsbeTn5ys3N9fjBAAAyq+bjg9jjMaNG6df/vKXiomJkSTl5ORIkkJDQz3WDQ0NdV92pWnTpikkJMR9ioiIuNmRAABAGXDT8TF69Ght3bpVH3zwQZHLHMfxOG+MKbKs0MSJE3Xq1Cn3KSsr62ZHAgAAZYBX+3wUeuaZZ/TZZ59p1apVqlevnnt5WFiYpMtbQOrUqeNefvTo0SJbQwq5XC65XK6bGQMAAJRBXm35MMZo9OjRWrhwodLT0xUVFeVxeVRUlMLCwpSWluZedv78ea1cuVIdOnQomYkBAECZ5tWWj1GjRmnevHn6v//7PwUHB7v34wgJCVFAQIAcx9GYMWOUlJSk6OhoRUdHKykpSVWqVFG/fv1K5QEAAICyxav4eOuttyRJnTp18lj+7rvvavDgwZKkCRMm6Ny5c0pISNDJkyfVtm1bpaamKjg4uEQGBgAAZZtX8WGMue46juMoMTFRiYmJNzsTAAAox/hsFwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArPI6PlatWqXevXsrPDxcjuPo008/9bjcGKPExESFh4crICBAnTp10vbt20tqXgAAUMZ5HR9nzpzRv/zLv2jGjBnFXp6cnKyUlBTNmDFDGRkZCgsLU/fu3ZWXl3fLwwIAgLKvkrdXiI+PV3x8fLGXGWM0ffp0TZo0SX379pUkzZkzR6GhoZo3b56GDx9+a9MCAIAyr0T3+di3b59ycnLUo0cP9zKXy6WOHTtq7dq1xV4nPz9fubm5HicAAFB+lWh85OTkSJJCQ0M9loeGhrovu9K0adMUEhLiPkVERJTkSAAA4DZTKke7OI7jcd4YU2RZoYkTJ+rUqVPuU1ZWVmmMBAAAbhNe7/NxLWFhYZIubwGpU6eOe/nRo0eLbA0p5HK55HK5SnIMAABwGyvRLR9RUVEKCwtTWlqae9n58+e1cuVKdejQoSTvCgAAlFFeb/k4ffq09uzZ4z6/b98+bdmyRdWrV9fdd9+tMWPGKCkpSdHR0YqOjlZSUpKqVKmifv36lejgAACgbPI6PjZu3KjOnTu7z48bN06SNGjQIL333nuaMGGCzp07p4SEBJ08eVJt27ZVamqqgoODS25qAABQZnkdH506dZIx5qqXO46jxMREJSYm3spcAACgnOKzXQAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsKrU4uOPf/yjoqKi5O/vr1atWunvf/97ad0VAAAoQ0olPhYsWKAxY8Zo0qRJ+vrrr3XvvfcqPj5eBw8eLI27AwAAZUipxEdKSoqeeuopDRs2TE2bNtX06dMVERGht956qzTuDgAAlCGVSvoGz58/r02bNumFF17wWN6jRw+tXbu2yPr5+fnKz893nz916pQkKTc3t6RHu+0V5J/19Qiw6E78Hr+T8fN9Z7kTf74LH7Mx5rrrlnh8HDt2TJcuXVJoaKjH8tDQUOXk5BRZf9q0aZo8eXKR5RERESU9GnBbCZnu6wkAlJY7+ec7Ly9PISEh11ynxOOjkOM4HueNMUWWSdLEiRM1btw49/mCggKdOHFCNWrUKHZ9lC+5ubmKiIhQVlaWqlat6utxAJQgfr7vLMYY5eXlKTw8/Lrrlnh81KxZUxUrViyylePo0aNFtoZIksvlksvl8lh21113lfRYuM1VrVqVX05AOcXP953jels8CpX4DqeVK1dWq1atlJaW5rE8LS1NHTp0KOm7AwAAZUypvOwybtw4DRw4UK1bt1b79u01e/ZsHTx4UCNGjCiNuwMAAGVIqcTH448/ruPHj2vKlCnKzs5WTEyMFi1apMjIyNK4O5RhLpdLr7zySpGX3gCUffx842occyPHxAAAAJQQPtsFAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEf8Jkff/xRb7/9tiZOnKgTJ05IkjZv3qxDhw75eDIAJWHPnj1aunSpzp07J+nGPmoddwbeZAw+sXXrVnXr1k0hISHav3+/du3apfr16+vll1/WgQMHNHfuXF+PCOAmHT9+XI8//rjS09PlOI52796t+vXr66mnntJdd92lN99809cjwsfY8gGfGDdunAYPHqzdu3fL39/fvTw+Pl6rVq3y4WQAbtXYsWNVqVIlHTx4UFWqVHEvf/zxx7VkyRIfTobbRal8tgtwPRkZGZo1a1aR5XXr1lVOTo4PJgJQUlJTU7V06VLVq1fPY3l0dLQOHDjgo6lwO2HLB3zC399fubm5RZbv2rVLtWrV8sFEAErKmTNnPLZ4FDp27BgfMgdJxAd85KGHHtKUKVN04cIFSZLjODp48KBeeOEFPfzwwz6eDsCtuO+++zz223IcRwUFBXr99dfVuXNnH06G2wU7nMIncnNz9cADD2j79u3Ky8tTeHi4cnJy1L59ey1atEiBgYG+HhHATdqxY4c6deqkVq1aKT09XX369NH27dt14sQJrVmzRg0aNPD1iPAx4gM+lZ6ers2bN6ugoEAtW7ZUt27dfD0SgBKQk5Ojt956S5s2bXL/fI8aNUp16tTx9Wi4DRAfAADAKo52gTX//d//fcPrPvvss6U4CYCStnXr1hteNy4urhQnQVnAlg9YExUVdUPrOY6jzMzMUp4GQEmqUKGCHMe57ruYOo6jS5cuWZoKtyviAwBwy7x5/47IyMhSnARlAfEBAACsYp8P+Mz333+vzz77TAcPHtT58+c9LktJSfHRVABKyo4dO4r9+e7Tp4+PJsLtgviAT3z55Zfq06ePoqKitGvXLsXExGj//v0yxqhly5a+Hg/ALcjMzNSvfvUrbdu2zWM/EMdxJIl9PsA7nMI3Jk6cqPHjx+vbb7+Vv7+/PvnkE2VlZaljx4569NFHfT0egFvw3HPPKSoqSkeOHFGVKlW0fft2rVq1Sq1bt9aKFSt8PR5uA+zzAZ8IDg7Wli1b1KBBA1WrVk2rV69W8+bN9c033+ihhx7S/v37fT0igJtUs2ZNpaenKy4uTiEhIdqwYYMaN26s9PR0jR8/Xl9//bWvR4SPseUDPhEYGKj8/HxJUnh4uPbu3eu+7NixY74aC0AJuHTpkoKCgiRdDpHDhw9LunyUy65du3w5Gm4T7PMBn2jXrp3WrFmjZs2aqVevXho/fry2bdumhQsXql27dr4eD8AtiImJ0datW1W/fn21bdtWycnJqly5smbPnq369ev7ejzcBnjZBT6RmZmp06dPKy4uTmfPntXzzz+v1atXq2HDhvrd737H+wAAZdjSpUt15swZ9e3bV5mZmXrwwQf13XffqUaNGpo/f766du3q6xHhY8QHAKDUnThxQtWqVXMf8YI7Gy+7wOdOnz6tgoICj2VVq1b10TQAbtbQoUNvaL0///nPpTwJbnds+YBP7Nu3T6NHj9aKFSv0008/uZcbY/jsB6CMqlChgiIjI9WiRYtrfsbLX//6V4tT4XbElg/4RP/+/SVd/g8oNDSUTbFAOTBixAjNnz9fmZmZGjp0qAYMGKDq1av7eizchtjyAZ8ICgrSpk2b1LhxY1+PAqAE5efna+HChfrzn/+stWvXqlevXnrqqafUo0cP/smAG+/zAZ9o06aNsrKyfD0GgBLmcrn061//WmlpadqxY4eaN2+uhIQERUZG6vTp074eD7cJXnaBT7z99tsaMWKEDh06pJiYGPn5+XlcHhcX56PJAJQUx3Hcn+1y5U7luLMRH/CJH374QXv37tWQIUPcywp/SbHDKVB2/fxll9WrV+vBBx/UjBkzdP/996tCBTa24zLiAz4xdOhQtWjRQh988AE7nALlREJCgubPn6+7775bQ4YM0fz581WjRg1fj4XbEDucwicCAwP1zTffqGHDhr4eBUAJqVChgu6++261aNHimv9QLFy40OJUuB2x5QM+0aVLF+IDKGeefPJJtmLihrDlAz4xe/Zsvfrqqxo6dKhiY2OL7HDap08fH00GAChtxAd84lo7nrHDKQCUb8QHAACwiuOe4HM//2wXAED5R3zAJy5duqTf/OY3qlu3roKCgpSZmSlJevnll/XOO+/4eDoAQGkiPuATU6dO1Xvvvafk5GRVrlzZvTw2NlZvv/22DycDAJQ24gM+MXfuXM2ePVv9+/dXxYoV3cvj4uL03Xff+XAyAEBpIz7gE4cOHSr2PT4KCgp04cIFH0wEALCF+IBPNG/eXH//+9+LLP/oo4/UokULH0wEALCFdziFT7zyyisaOHCgDh06pIKCAi1cuFC7du3S3Llz9fnnn/t6PABAKeJ9PmBVZmamoqKi5DiOli5dqqSkJG3atEkFBQVq2bKl/uu//ks9evTw9ZgAgFJEfMCqihUrKjs7W7Vr15YkPf744/r973+vsLAwH08GALCFfT5g1ZWtu3jxYp09e9ZH0wAAfIH4gE+x4Q0A7jzEB6xyHKfIR27zEdwAcGfhaBdYZYzR4MGD5XK5JF3+XJcRI0YoMDDQY72FCxf6YjwAgAXEB6waNGiQx/kBAwb4aBIAgK9wtAsAALCKfT4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABg1f8D0NHKbCZxZpgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df['Gender'].value_counts(normalize=True)*100).plot(kind='bar')\n",
    "plt.title('Gender wise contribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75d7a34",
   "metadata": {},
   "source": [
    "## In dataset, females are 51% and males are 49%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "57aed74d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female average age is -  38.0\n",
      "Male avergae age is -  37.0\n"
     ]
    }
   ],
   "source": [
    "genderwise_avg_age = round(df.groupby('Gender')['Age'].mean(),0)\n",
    "print(\"Female average age is - \",genderwise_avg_age[0])\n",
    "print(\"Male avergae age is - \",genderwise_avg_age[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "cf3d109e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genderwise_avg_age.plot(kind='pie')\n",
    "plt.title('gender wise avg age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ad5579",
   "metadata": {},
   "source": [
    "## female average is more than male average age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "f5b6d1aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female max age is -  60\n",
      "Male max age is -  60\n"
     ]
    }
   ],
   "source": [
    "genderwise_max_age = round(df.groupby('Gender')['Age'].max(),0)\n",
    "print(\"Female max age is - \",genderwise_max_age[0])\n",
    "print(\"Male max age is - \",genderwise_max_age[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "06cc6ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female min age is -  18\n",
      "Male min age is -  18\n"
     ]
    }
   ],
   "source": [
    "genderwise_min_age = round(df.groupby('Gender')['Age'].min(),0)\n",
    "print(\"Female min age is - \",genderwise_min_age[0])\n",
    "print(\"Male min age is - \",genderwise_min_age[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "055b8387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female average salary is -  71760.0\n",
      "Male avergae salary is -  67643.0\n"
     ]
    }
   ],
   "source": [
    "genderwise_avg_salary = round(df.groupby('Gender')['Salary'].mean(),0)\n",
    "print(\"Female average salary is - \",genderwise_avg_salary[0])\n",
    "print(\"Male avergae salary is - \",genderwise_avg_salary[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "e21da7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genderwise_avg_salary.plot(kind='barh')\n",
    "plt.title('Gender wise avg salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad493f4",
   "metadata": {},
   "source": [
    "## female average salary is more than male average salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d68abfb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female max salary is -  150000\n",
      "Male max salary is -  150000\n"
     ]
    }
   ],
   "source": [
    "genderwise_max_salary = round(df.groupby('Gender')['Salary'].max(),0)\n",
    "print(\"Female max salary is - \",genderwise_max_salary[0])\n",
    "print(\"Male max salary is - \",genderwise_max_salary[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "3cd8443b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female min salary is -  15000\n",
      "Male min salary is -  15000\n"
     ]
    }
   ],
   "source": [
    "genderwise_min_salary = round(df.groupby('Gender')['Salary'].min(),0)\n",
    "print(\"Female min salary is - \",genderwise_min_salary[0])\n",
    "print(\"Male min salary is - \",genderwise_min_salary[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "80f1debc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Iphone purchased are -  143\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Iphone purchased are - \",df['Purchase Iphone'].value_counts()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "231a227d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    77\n",
       "Male      66\n",
       "Name: Purchase Iphone, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Gender')['Purchase Iphone'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "0e544e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Gender')['Purchase Iphone'].sum().plot()\n",
    "plt.title(\"Gender wise Iphone purchased\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50eac890",
   "metadata": {},
   "source": [
    "## Iphone purchased - Female 77 no's (out of 204) and male 66 no's(out of 196)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "be6eab9d",
   "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>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>25000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>26000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Male</td>\n",
       "      <td>46</td>\n",
       "      <td>28000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>29000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </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>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>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>143 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Salary  Purchase Iphone\n",
       "7    Female   32  150000                1\n",
       "16     Male   47   25000                1\n",
       "17     Male   45   26000                1\n",
       "18     Male   46   28000                1\n",
       "19   Female   48   29000                1\n",
       "..      ...  ...     ...              ...\n",
       "393    Male   60   42000                1\n",
       "395  Female   46   41000                1\n",
       "396    Male   51   23000                1\n",
       "397  Female   50   20000                1\n",
       "399  Female   49   36000                1\n",
       "\n",
       "[143 rows x 4 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Iphone purchased data filtered from dataset as df1\n",
    "\n",
    "df1= df[df['Purchase Iphone']==1]\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "17860a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df1,x='Age',y='Purchase Iphone',estimator='sum',hue='Gender')\n",
    "plt.title('Age wise Iphone Purchased')\n",
    "plt.show()\n",
    "             "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0597fe",
   "metadata": {},
   "source": [
    "## maximum Iphone purchased by age - 46 to 50 years\n",
    "## Female - between 47 to 48 years\n",
    "## Male - between 46 to 50 and at 60 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "aa6f23e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "agewise_salary=pd.DataFrame(df1.groupby(['Age','Gender'])['Salary'].sum(),columns=['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "aea179ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Salary'>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=agewise_salary,x='Age',y='Salary',hue='Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb65013",
   "metadata": {},
   "source": [
    "## There is no any correlation in salary and age in case of both male and female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "e669e238",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pv11379\\AppData\\Local\\Temp\\ipykernel_37540\\158208961.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  correlation = df.corr()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = df.corr()\n",
    "sns.heatmap(correlation,annot=True,cmap='rainbow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d1624c",
   "metadata": {},
   "source": [
    "## As per correlation table, only Age is having good correlation with Purchase Iphone."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ac48b7",
   "metadata": {},
   "source": [
    "# KNN machine learning model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "562075f1",
   "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>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3) ## top 3 rows of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "b4dd00e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## convert categorical column gender into numerical using get dummies data.\n",
    "df_dummy=pd.get_dummies(df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "65e019e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## concatenate df and df_dummy\n",
    "df_final=pd.concat([df,df_dummy],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "98cdb244",
   "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",
       "      <th>Female</th>\n",
       "      <th>Male</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",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Salary  Purchase Iphone  Female  Male\n",
       "0      Male   19   19000                0       0     1\n",
       "1      Male   35   20000                0       0     1\n",
       "2    Female   26   43000                0       1     0\n",
       "3    Female   27   57000                0       1     0\n",
       "4      Male   19   76000                0       0     1\n",
       "..      ...  ...     ...              ...     ...   ...\n",
       "395  Female   46   41000                1       1     0\n",
       "396    Male   51   23000                1       0     1\n",
       "397  Female   50   20000                1       1     0\n",
       "398    Male   36   33000                0       0     1\n",
       "399  Female   49   36000                1       1     0\n",
       "\n",
       "[400 rows x 6 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747d86f7",
   "metadata": {},
   "source": [
    "## in above dataset, Gender/Age/Salary is independent variable and Purchase Iphone is dependent variable. It is label dataset.\n",
    "## It is fall under supervised machine learning.\n",
    "## In dataset, there are Two class in dependent variable - one is 0 i.e not purchase and one is 1 i.e purchase.\n",
    "## It is fall under classification supervised machine learning. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b33f3c",
   "metadata": {},
   "source": [
    "# Import Scikit liabrary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b211e70e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02eec05",
   "metadata": {},
   "source": [
    "## Scalling of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "b569d9dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7mJkfCwwMxNChQ3Hq1CnMmjULAFBeXo7o6Itv2JWVlR16a35MpVJBpep9f6WSd2q0tuL1L49j477zAABdsAorfj4Utw269O9Ab3Nj33Ccr2lEXZMNu09VY+pg73ntRHR1ZB8z82NWqxX5+fmIjo5GUlIS9Ho90tPTncdbWlqQkZGBiRMnylglUc/4vsKK6Wt2OYPMnLFx2P7sJK8KMgCg9FHg9h9e87EyE0rqLDJXRETuRtYw8z//8z/IyMhAQUEBvvvuO/ziF7+AyWTCggULIEkSUlNTsWLFCmzbtg25ublYuHAhAgICMG/ePDnLJnIpmwMIu+Mp/DajFsV1FsSEqPGP/xqH1+4dBq2651a3dieGEDVSDMEAgK+PV6DV4ZC5IiJyJ7JeZiouLsbcuXNRXV2NyMhI3HDDDdi/fz8SEhIAAEuXLoXFYsHixYtRV1eH8ePHY/v27dBoNHKWTeQyBdWNSC/zhWb4dADAggkJWHrHQASq3OqKsCxu7BeBM1Vtl5uyz9djXFKY3CURkZuQ9R1y8+bNlz0uSRLS0tKQlpbWMwURycTaasfuU9U/zHgrwVZbilX3DsX86Slyl+Y2/H19cEv/CHyVV4GD52oxODpY7pKIyE241ZgZIm9UVNuE978rdE7dn6yxo2zDUxgc6SdzZe5ngE6DaK0/Wh0Ce89Uy10OEbkJhhkimdjsDmScqMLWwyUwN7ci2F+JX4yKxbBQO0Qrp/DvjCRJmNQ/EgBwvNyMWmvvmCSQiK4PL8QTyaDMaMH2YxWob7IBAFJignFzv0j4KRUoZofDZemC/TEoWoP8MjO+r/ORuxwicgMMM0Q9qNXhwIGCWhw6VwcBIFDlg9sH6ZAYHih3aR7lxr4ROF3ZgNoWBdTJE+Quh4hkxjBD1EOqzFZsP1aO6oYWAMAAvQaT+0fC35e9C9cqUKXEyLhQHDhXi9BJD8PuEHKXREQyYpghcjEhBA4X1WPv6Wo4BKD29cGUgZFIjuIUA9djVEIIjhTWAOFx+PacBWPHyF0REcmFA4CJXKjR2opPjpRi96m2INMnIhAPjo9nkOkGKqUPBgS3rdW0Oc/MdZuIvBjDDJGLnK1qwPvfFaKwtglKhYQpAyJx97BoToDXjfpqHGg1VaLG4sAHBwrlLoeIZMIwQ9TNbHYHvjleiX8fLYPFZkdEkB/mjovHsNiQy674TtfORwKMmR8BANZ9e4a9M0ReimGGqBtVma3YfLAIOSVGAMDI+BA8MDYOYYGcAM9VGnJ2ICLAB5VmKzZ9x94ZIm/EMEPUTY6VmvDhoSLUNrYgwM8Hs0YYcEtyJJQK/pq5lKMVvxgUBABYl8HeGSJvxHdZouvUanfg6/wKpOdXwO4QSAgPwIPj45HAuWN6zJRENWJC1KgyWzl2hsgLMcwQXQejxYaPs4qR+8O6Sjf0CcM9ww0I8OMg357k6yPh8cl9AQB/3V0Am90hc0VE1JMYZoi6qKC6ER8cKESl2Qp/XwVmjTBgfFI4B/nK5L7RsYgI8kNJvQWf55TJXQ4R9SCGGaJr5BACmWeq8en3pbC2OqALVmHuOF5Wkpu/rw8WTkwEAPwl4yyE4KzARN6CYYboGjS1tOKTwyU4eK4OADAsVotfjI5FsL+vzJURAMy/IQEBfj7ILzNh1ymu2EnkLRhmiK5SmdGCDw4UoajOAqVCwvQhOkwZEMW7ldxISEDbnD4A8H8ZZ2Suhoh6Ct+Fia5ACIEjRfX4Z1YxGqytCA3wxZyxcRioD5a7NOrEIzclQamQkHmmBkeL6+Uuh4h6AG+5ILqMlta2265PVjYAAJKjgnD7IB38lK7/OyA/P9/lz3G13KmWK4kJUeNnww3YergE/5dxFn9+cJTcJRGRizHMEF1CTYMV/8kpQ12TDQoJuKlfBEbEuX5JAlNtFQBg/vz5Ln2ermhoaJC7hKvy35P6YOvhEnyRW4Zz1Y1IjODgbKLejGGGqBMnys34+ngFbHaBQJUP7kqJhiFE3SPPbWlom7NmxqLlGDBsdI8855XkH8jAFxv/iObmZrlLuSoD9cGYMiASO09U4a97zuJ3s4bKXRIRuRDDDNGP2B0Cu09V4fvitrWVYkPVuDNFL8skeOGGBMQmD+nx5+1MRaHnDaZdNKkvdp6owseHivGrqQMQyvWxiHotDgAm+oG52YZ/ZhU7g8yYhFD8fEQMZ/P1UOOTwpASEwxrqwObDxbJXQ4RuRDDDBGAwtomfHCgCOWmZvgpFZg5LBo39ouAQsHZfD2VJElYMCERAPDe/vNo5RIHRL0Wwwx5OQnHjQpsO1wCi82OSI0K88bFo09kkNyFUTeYOdyAsMC2JQ525FfKXQ4RuQjDDHkts9WByHtfQp6x7TLSEEMw7h8dC62as/n2Fv6+PpgzNg4AsDHznLzFEJHLMMyQV8opNuL5HdUI6DcOCkng9kFRuH2QDkof/kr0NvNvSICPQsK+szU4UW6WuxwicgG+c5NXEULgvf3nce+6TFQ22mGrK8MUXSuGGLRyl0YuYghRY9pgHQBg475z8hZDRC7BMENeo8Haiqc3H8GvP8lFi92BMQYVyjamIsSPqyv3dgt+WE17W3YJjE02eYshom7HMENe4VipCTPf3IN/f18KH4WE/71rIF68MRTC2ih3adQDxieFYaBeA4vNjo+zeJs2UW/DMEO9mhACHxwoxM/f2ouC6kZEa/3x0aIb8N+39IXCxcsSkPuQJMnZO/PuvvOwO9gbR9SbMMxQr9VobcWzHx7Bsq05sLY6MGVAJD5/+maMTgiTuzSSwawRMdCqfVFY24RvT/A2baLehGGGeqXcEiNmrt2DT460XVZ68c6B+NuCsZzS3oup/XzwwA+3af9j/3mZqyGi7sR52q9TYWEhqqur5S6jnYiICMTHx8tdhizsDoH1u85idfoJ2OwC+mB/vDlvJMYmsjeGgHnj4rF+11lknKxCUW0T4sIC5C6JiLoBw8x1KCwsxMBBg2BpapK7lHbUAQE4np/vdYGmuK4Jz330PQ4U1AIA7hiix4rZQxHG3hj6QWJEIG7sF469p2vw4cEi/M/0AXKXRETdgGHmOlRXV8PS1IQHX/g9dPF95S4HQNvqxu+veh7V1dVeE2aEENiaXYK0T/NgtrYi0M8HL/9sCO4bHQuJg3zpJ+aNS2gLM4eK8MztyfDlRIlEHo9hphvo4vsiNnmI3GV4pZJ6C/53aw4yTlYBAEbFh+CNB0YgITxQ5srIXU0drENEkB+qzFZ8nV+BO1Ki5S6JiK4T/yQhj+RwCPxj/3lMW52BjJNV8FMq8Pz0Afho0QQGGbosP6UC941pGwi86QDnnCHqDdgzQx7nVIUZyz/JdY6NGZ0QilX3DkO/KK50TVdn7th4rPv2DHaf4kBgot6APTPkMczNNvzus2O484+7caCgFgF+PkibORgfLZrAIEPXJD48ADcnR0AI4IMDhXKXQ0TXiWGG3F7bAN9i3PqHDPx1TwFaHQLTBuvwVeotWHhjEnwUHORL1+7B8W0D5D86VAyb3SFzNUR0PdwmzKxcuRKSJCE1NdW5TwiBtLQ0GAwGqNVqTJ48GXl5efIVST1u35ka/PytTDz30feoMluRFBGId345FusfHsNLA3RdbhukQ6RGheoGK9KPVchdDhFdB7cIMwcPHsT69esxbNiwdvtff/11rF69GmvXrsXBgweh1+sxdepUmM1mmSqlnpJbYsTDfz+AuW/vx5Gieqh9fbD0jgH4MvVmTB4QJXd51Av4+ijwwIWBwN/xUhORJ5M9zDQ0NODBBx/E22+/jdDQUOd+IQTWrFmD5cuXY/bs2UhJScHGjRvR1NSETZs2yVgxudLJCjOWbMrG3W/uwa6TVVAqJDw8IQEZSydj8eR+UCl95C6RepE54+IgScCe09U4X8MV1Ik8lexh5sknn8SMGTNw++23t9tfUFCA8vJyTJs2zblPpVJh0qRJyMzMvOTjWa1WmEymdhu5v8OFdXjs3UOY9sYufHa0DABwzwgDvv7VJLxyTwqiNP4yV0i9UWxoAG5JjgQAfHiQt2kTeSpZb83evHkzsrOzcfDgwQ7HysvLAQA6na7dfp1Oh/PnL71I3MqVK/Hb3/62ewsll3A4BHadqsL6XWeReaYGACBJbcsQLLm1H4YYtDJXSN5gztg4ZJyswsdZxXhuan8oOSMwkceRLcwUFRXhmWeewfbt2+Hvf+m/un86Hb0Q4rJT1C9btgzPPfec82uTyYS4uLjrL5i6TaW5GR8fKsYHBwpRXGcBACgVEmaNjMHjk/ryNmvqUbcNujgj8M4TVZg6WHflbyIityJbmMnKykJlZSVGjx7t3Ge327Fr1y6sXbsWJ06cANDWQxMdfXG68crKyg69NT+mUqmgUqlcVzh1icMhsOd0NTZ9V4gd+RVodQgAQLC/EveOjsWjN/dBTIha5irJG/kpFbh3VCz+b9dZbD5QyDBD5IFkCzO33XYbcnJy2u375S9/iYEDB+KFF15Anz59oNfrkZ6ejpEjRwIAWlpakJGRgVWrVslRMnVBldmKj7OKsPlAEQprL64uPio+BPPGJ2DG0Gio/Tiol+R1/9g4/N+us9h5ohLlxmbotRyjReRJuhRm+vTpg4MHDyI8PLzd/vr6eowaNQpnz5694mNoNBqkpKS02xcYGIjw8HDn/tTUVKxYsQLJyclITk7GihUrEBAQgHnz5nWlbOohDofAvrM12PRdIbYfK4fN3tYLo/FXYvbIGMwdH4+B+mCZq6TeJD8//7ofY3CkH45VteBP//4Ovxisue7Hi4iI8JqV64nk1qUwc+7cOdjt9g77rVYrSkpKrruoC5YuXQqLxYLFixejrq4O48ePx/bt26HRXP8bDXW/6gYr/plVjM0HCnGu5mIvzIi4EMwbH4+ZwwzshaFuZaptWy19/vz51/1YgUOmIOLuX2HjntNY+dBjAMR1PZ46IADH8/MZaIh6wDWFmU8//dT5/1999RW02ot3m9jtdnz99ddITEzscjHffvttu68lSUJaWhrS0tK6/JjkWkJc7IX5Ku9iL0yQSomfj4zB3HHxGGxgLwy5hqWhbeqFGYuWY8Cw0Vc4+/JaHcDnJQII0ePBP2yFzr/rYaai8AzeX/U8qqurGWaIesA1hZlZs2YBaAsZCxYsaHfM19cXiYmJ+MMf/tBtxZH7arC2YktWMd7ddw5nqi5ONjY8VtvWCzPcgAA/LspOPSPckIDY5CHX/TiDUImjxUZUIBSjk6Ov/A1E5Bau6dPG4WhbjC0pKQkHDx5ERESES4oi93WmqgHvZp7DluwSNFhbAQCBfj64Z2QM5o2LR0oM54Yhz5Vi0OJosRFnqhpgabHzsiiRh+jSn84FBQXdXQd1s+4YEPljZ+ts+OexBuwvaXbui9H44M5+gZicqEaArw0tFWeQfZn1+jggktxdpEaFKI0KlWYr8stNGBUfeuVvIiLZdfk6wNdff42vv/4alZWVzh6bC/7+979fd2HUNd05IBIA/KL7QztxDgL6jXPuazr1HczZ/8b5c0dw6YUlOuKASPIEKQYtvjlRibwSE0bGhVx2kk4icg9dCjO//e1v8corr2DMmDGIjo7mL7sb6a4BkWYbcLROifLmC1O7C8QFODAw2IHg+JHAbSOv6fE4IJI8RX99EHadqkJtUwvKjM0wcDJHIrfXpTDzl7/8Be+88w4eeuih7q6HuklXB0S2tDpw4FwtDpfXwSHa1koaqNdgbGIYQgP8XFApkXtRKX3QX6fBsTIT8kpNDDNEHqBLYaalpQUTJ07s7lpIRkIIHC83Y8/pajS1tM0hlBDetqJwWCBDDHmXIYZgHCsz4WSFGbf0j4BKyYHARO6sS8vDPvroo9i0aVN310IyMTfb8MmRUmw/VoGmFju0al/MHB6Ne4YbGGTIK0Vr/REW4IdWh8DJ8ga5yyGiK+hSz0xzczPWr1+PHTt2YNiwYfD19W13fPXq1d1SHLne8XITdh6vQovdAR+FhBuSwjAiPgRKRZdyLlGvIEkShsQEY/epauSWGjE0llMOELmzLoWZo0ePYsSIEQCA3Nzcdsc4GNgz2OwOZJysQl5p24BhfbA/pg3WIZQ9MUQAgEH6YOw9XY1KsxWV5mZEabj4JJG76lKY2blzZ3fXQT3IZLHh30dLUd3QAgAYlxSG8YlhUCgYRIkuUPv5oG9kEE5VNiCv1ISoAQwzRO6K1xK8TGm9BZsPFqG6oQVqXx/8fGQMJvQJZ5Ah6sSFGa2Pl5thszuucDYRyaVLPTNTpky57OWkb775pssFkeucqjDjq7wK2IVAZJAKdw+PRrC/75W/kchLxYWqEeyvhKm5FacrGzAomoumErmjLoWZC+NlLrDZbDhy5Ahyc3M7LEBJ7uFocT12nmibHbhvZCCmD9HD14cdc0SXI0kShhi02He2BnmlJoYZIjfVpTDzxhtvdLo/LS0NDQ28jdHdHDpfi72nawAAQ2O0mDwgEgoO1Ca6KoOjg7H/bA1K6i2oa2zhIHkiN9Stf5rPnz+f6zK5mR8HmXGJYZjCIEN0TYL8lUiMCAQA591/ROReujXM7Nu3D/7+HPHvLrIL65xB5oakMEzoG85b54m6YIih7fLSsTIT7A4hczVE9FNdusw0e/bsdl8LIVBWVoZDhw7hpZde6pbC6PqUt6px4lQ1gLYgM75PuMwVEXmupPBABPr5oLHFjrPVDUiO0shdEhH9SJfCjFbbfjZMhUKBAQMG4JVXXsG0adO6pTDqOnWfMTjREgIAGBUf4lZBJj8/X+4SnNypFnJvCoWEQdHBOHS+DnmlJoYZIjfTpTCzYcOG7q6DukkDVIi450UAEgbqNbipX4TcJQEATLVtd1LNnz9f5ko64qB1uhpDDG1h5nxNE0wWG4LVnNaAyF10KcxckJWVhfz8fEiShMGDB2PkyJHdVRd1QaO1FccQB4WfL0IVzbh9UD+3GSNjaWgbODlj0XIMGDZa5mra5B/IwBcb/4jm5ma5SyEPEBLgh9hQNYrrLDhWZsINbtTjSeTtuhRmKisrMWfOHHz77bcICQmBEAJGoxFTpkzB5s2bERkZ2d110hW02h347GgZWuALW00RBscq4eOGs/qGGxIQmzxE7jIAABWFZ+QugTxMikGL4joL8kpNGJcUxjsDidxEl+5meuqpp2AymZCXl4fa2lrU1dUhNzcXJpMJTz/9dHfXSFch42QVyk3NUKIVlf98BUqJd1wQdbe+kYHwVyrQYG1FYU2T3OUQ0Q+6FGa+/PJLrFu3DoMGDXLuGzx4MP785z/jiy++6Lbi6OocKzMh94f5LwagFK31ZTJXRNQ7KX0UGPjDLMC5pUaZqyGiC7oUZhwOB3x9Ow5+8/X1hcPBxdh6UpXZim+OVwJouwU7FI0yV0TUu12Yc6aguhGN1laZqyEioIth5tZbb8UzzzyD0tJS576SkhI8++yzuO2227qtOLq8VrsDX+aVw+4QSAgPwLikMLlLIur1IoJU0Af7wyGA/HLOCEzkDroUZtauXQuz2YzExET07dsX/fr1Q1JSEsxmM958883urpEuYffpatQ2tiDAzwfTBuvc5s4lot5uSExb70xeiQlCcHwakdy6dDdTXFwcsrOzkZ6ejuPHj0MIgcGDB+P222/v7vroEgqqG3G0uO2a/bTBOgT4Xddd9kR0DfpHabDrZBXqLTaU1FsQGxogd0lEXu2aema++eYbDB48GCZTW9fq1KlT8dRTT+Hpp5/G2LFjMWTIEOzevdslhdJFzTY7duRXAABGxIUgITxQ5oqIvIufUoEBurZZgLn4JJH8rinMrFmzBo899hiCg4M7HNNqtVi0aBFWr17dbcVR5zJOVqGpxY6wAD/c2JcTdxHJYYihbVmXU5UNaLbZZa6GyLtdU5j5/vvvcccdd1zy+LRp05CVlXXdRdGlna1qwPFyMyQAUwfroPTp1oXPiegq6YJVCA/yg90hcKLcLHc5RF7tmj4JKyoqOr0l+wKlUomqqqrrLoo6Z221O2/DHhUfCr3WX+aKiLyXJElI+aF3JrfUyIHARDK6pjATExODnJycSx4/evQooqOjr7so6lzmmRo0ttgRovbFDX14GzaR3AbqNfBRSKhuaEGF2Sp3OURe65rCzF133YXf/OY3nS7MZ7FY8PLLL+Puu+/utuLoonJjs/PupVsHRvHyEpEb8Pf1Qb/IIABAXglnBCaSyzXdz/vrX/8aW7duRf/+/bFkyRIMGDAAkiQhPz8ff/7zn2G327F8+XJX1eq1HA7hvLw0UK9BXBhvAyVyFykxwThRYcaJCjNuTo6En5J/aBD1tGsKMzqdDpmZmXjiiSewbNky5zViSZIwffp0vPXWW9DpdC4p1JvllBhR1WCFSqnAzckRcpdDRD8SE6KGVu0Lo8WGU5Vm511ORNRzrnmmtYSEBHz++eeoq6vD6dOnIYRAcnIyQkNDXVGf17PY7Nh3tgYAMKFvOCfHI3IzbQOBg7H3TA3ySk0MM0Qy6PInY2hoKMaOHdudtVAn9p2pgbXVgYggPwyN4ZskkTsaFB2MzLM1KDM2o7qBA4GJehov7rqxKrMVuT8MKpzUPxIKrr1E5JYCVUr0jWgbCPx9cb28xRB5IYYZN7bndDUEgOSoIK79QuTmhse19ZweLzOjxSFzMURehmHGTRXWNqGwtgkKCbixHwf9Erm7mBA1wgP90OoQONfAt1ainiTrb9y6deswbNgwBAcHIzg4GBMmTMAXX3zhPC6EQFpaGgwGA9RqNSZPnoy8vDwZK+4ZQgjsPV0NABgWGwKt+tKzLhORe5AkCcPjQgAAZxt8APCyMFFPkTXMxMbG4rXXXsOhQ4dw6NAh3HrrrbjnnnucgeX111/H6tWrsXbtWhw8eBB6vR5Tp06F2dy710E5VdmASrMVfj4KjE3kXWJEnmKgXgOVUoHGVgnqPqPlLofIa8gaZmbOnIm77roL/fv3R//+/fHqq68iKCgI+/fvhxACa9aswfLlyzF79mykpKRg48aNaGpqwqZNmy75mFarFSaTqd3mSewOgcwzbbdij0oI4a3YRB7E10eBwYZgAIBmNGdDJ+opbnNh1263Y/PmzWhsbMSECRNQUFCA8vJyTJs2zXmOSqXCpEmTkJmZecnHWblyJbRarXOLi4vrifK7TW6pEUaLDQF+PhgZx14ZIk8zPDYEgIC6zxiUmlvlLofIK8geZnJychAUFASVSoXHH38c27Ztw+DBg1FeXg4AHWYU1ul0zmOdWbZsGYxGo3MrKipyaf3dqaXVge/O1gIAxiWFcVp0Ig+kVftC7982O/oXpxtlrobIO8h+DWPAgAE4cuQI6uvrsWXLFixYsAAZGRnO49JP5lYRQnTY92MqlQoqlcpl9brS4aI6WGx2aNW+SOEsokQeq6/GjvJmBXaes6DB2ooglexvtUS9mux/+vv5+aFfv34YM2YMVq5cieHDh+OPf/wj9Ho9AHTohamsrOyV6z9ZbHZkn68HAEzsGw4fBe+EIPJUOn8BW00xmmwC27KL5S6HqNeTPcz8lBACVqsVSUlJ0Ov1SE9Pdx5raWlBRkYGJk6cKGOFrnGksB4t9rZlC5KjguQuh4iugyQB5uzPAAAb9p6DwyFkroiod5M1zPzv//4vdu/ejXPnziEnJwfLly/Ht99+iwcffBCSJCE1NRUrVqzAtm3bkJubi4ULFyIgIADz5s2Ts+xuZ7XZcaSoHgAwPin8spfRiMgzNOR+jQBfCWerG7Ejv0Lucoh6NVkv5FZUVOChhx5CWVkZtFothg0bhi+//BJTp04FACxduhQWiwWLFy9GXV0dxo8fj+3bt0Oj0chZdrc7UtTWKxMe6Ie+kYFyl0NE3UC0WHBH3wBsPd6I9bvOYtoQvdwlEfVasoaZv/3tb5c9LkkS0tLSkJaW1jMFycDaasfhH3plxiWFsVeGqBe5KzkQn52y4ND5OmSdr8PoBE63QOQKbjdmxtscLTbC2upAaIAv+nGsDFGvEqb2wayRBgDA+l1nZK6GqPdimJFRS6sD2YV1AIBxiWFQsFeGqNd57OY+AIDtxypQUM15Z4hcgWFGRjklRjTbHNCqfdFf17vGARFRm2SdBrcOjIIQwF93n5W7HKJeiWFGJja7A1nn23plxiaGQsF5ZYh6rf++pa135p9ZxahusMpcDVHvwzAjk9wSIyw2O4L9lRioD5a7HCJyofFJYRgeq4W11YF3952XuxyiXodhRgZ2h3DewTQmIYyz/RL1cpIk4b9v6QsA+Me+c7C02GWuiKh3YZiRwalKM8zNrVD7+mBQNMfKEHmDO1L0iA8LQF2TDZsOFMpdDlGvwjDTw4QQzjWYRsSFQOnDHwGRN/BRSFg8ua135i8ZZ9BsY+8MUXfhJ2kPK6qzoKrBCqVCwtBYroxN5E1mj4pFbKgaVWYr3v+OvTNE3YVhpodduINpiCEYal8fmashop7kp1RgyZR+ANg7Q9SdGGZ6UJXZisLaJkgARsZzWnMib8TeGaLuxzDTgy7M9tsvKghata/M1RCRHNg7Q9T9GGZ6iLnZhpMVZgDgYnNEXo69M0Tdi2GmhxwpqodDADEhauiC/eUuh4hkxN4Zou7FMNMDrK125JaYALBXhoja/Lh35r39nBWY6HowzPSAY6UmtNgdCAvwQ2J4gNzlEJEb+HHvzLpvz6DB2ipzRUSei2HGxRxC4PtiIwBgeJwWksSlC4iozb2jY9EnIhA1jS1Yv4srahN1FcOMi52rboTRYoNKqcCgaC4oSUQX+foo8Pz0AQCAv+4+i0pzs8wVEXkmhhkXO/LDgpIpBi18uXQBEf3EHSl6jIgLQVOLHX/6+pTc5RB5JH66ulB1gxVFdRZIAIZx6QIi6oQkSVh250AAwAcHinDqhykciOjqMcy40Pc/9Mr0jQxCMCfJI6JLGN8nHFMH62B3CPx//8mHEELukog8CsOMi1hsdhwvb/sLa0RciLzFEJHbW37XIPj6SNh1sgo7T1TKXQ6RR2GYcZG8EiNaHQKRGhUMIZwkj4guLzEiEI/cmAQA+P8+y0dLq0Pmiog8B8OMCzgcF2/HHhEbwtuxieiqLLm1HyKC/FBQ3Yi/7SmQuxwij8Ew4wJnqhrQYG2F2tcH/XVBcpdDRB5C4++LF+8cBAD409enUFzXJHNFRJ6BYcYFjhTXAwCGxmih5O3YRHQN7h0Vg3FJYbDY7Ej79Jjc5RB5BH7SdrPqBitK65shScBQ3o5NRNdIkiS8OisFSoWEHfkV2J5XLndJRG6PYaabHf1hrEzfyCAEqZQyV0NEnihZp8F/39IHAPDSv3JhtNhkrojIvTHMdCNrqx3Hy9tWxx7OXhkiug5P35aMPhGBqDBZ8ep/eLmJ6HIYZrpRfpkZNrtAeKAfYkLUcpdDRB7M39cHr/9iGCQJ+OhQMXadrJK7JCK3xTDTTYQQOHph4G8sV8cmous3JjEMCyYkAgBe2HIUxiZebiLqDMNMNymqs6CuyQZfHwmD9Fwdm4i6x9I7BiApIhBlxmb87yc5XOqAqBMMM93kQq/MIH0w/JRsViLqHgF+Sqx5YASUCgn/OVqGrdklcpdE5Hb4qdsNmlqBs9WNALg6NhF1v+FxIUi9PRkA8Jt/5eJsVYPMFRG5F4aZblDQ4AMhgNgQNcKDVHKXQ0S90BOT+2FcUhgaW+xY/H42LC12uUsichsMM9dLoURBQ1szsleGiFzFRyFh7dyRiAhS4Xi5Gb/5V67cJRG5DYaZ6xQwYCKsDgmBKh/0ieQ6TETkOlHB/vjT3BFQSMDHWcV4b/95uUsicgsMM9dJM3IGACDFoIWPgrdjE5FrTewbgf+ZPgAA8PKnecg8XS1zRUTyY5i5DufqbfCPGwIJAkNjeImJiHrGE5P6YtYIA+wOgSfez0bBDzcgEHkrhpnr8MXpJgBATIADgVyHiYh6iCRJeO3eYRgRFwKjxYaFGw6gymyVuywi2TDMdJHRYsOu8xYAQJ8gh8zVEJG38ff1wfqHRyMuTI3zNU1YuOEAzM2cIZi8k6xhZuXKlRg7diw0Gg2ioqIwa9YsnDhxot05QgikpaXBYDBArVZj8uTJyMvLk6nii7ZkFcNqF2ipOo8IFWfkJKKeF6Xxxz8eGY/wQD/klZrw2LuHeMs2eSVZw0xGRgaefPJJ7N+/H+np6WhtbcW0adPQ2Hjx+u/rr7+O1atXY+3atTh48CD0ej2mTp0Ks9ksY+VAgJ8PwtUKmLM/A5dhIiK5JEYE4p1fjkOgnw/2n63Fo+8eRLONgYa8i6xh5ssvv8TChQsxZMgQDB8+HBs2bEBhYSGysrIAtPXKrFmzBsuXL8fs2bORkpKCjRs3oqmpCZs2ber0Ma1WK0wmU7vNFeaMi8dfZkSh4Wi6Sx6fiOhqDY3VYuMj4xDg54O9p2vw6Eb20JB3casxM0ajEQAQFhYGACgoKEB5eTmmTZvmPEelUmHSpEnIzMzs9DFWrlwJrVbr3OLi4lxWr49CAhytLnt8IqKrNSYxDO/8si3Q7Dldjfl/+w71TS1yl0XUI9wmzAgh8Nxzz+Gmm25CSkoKAKC8vBwAoNPp2p2r0+mcx35q2bJlMBqNzq2oqMi1hRMRuYlxSWH4x3+NQ7C/Elnn6/DA/+1HmdEid1lELuc2YWbJkiU4evQoPvjggw7HpJ8MShFCdNh3gUqlQnBwcLuNiMhbjE4Iw0ePT0CURoUTFWbcs3Yvvi+ql7ssIpdyizDz1FNP4dNPP8XOnTsRGxvr3K/X6wGgQy9MZWVlh94aIiJqM1AfjC1PTMQAnQaVZivu/799+NeRErnLInIZWcOMEAJLlizB1q1b8c033yApKand8aSkJOj1eqSnXxxk29LSgoyMDEycOLGnyyUi8hhxYQH45xMTcOvAKFhbHXhm8xEs35bDO52oV5I1zDz55JN47733sGnTJmg0GpSXl6O8vBwWS9s1XkmSkJqaihUrVmDbtm3Izc3FwoULERAQgHnz5slZOhGR29P4++Lth8fgqVv7QZKA978rxOy3MnGiXN6pLYi6m6xz8K9btw4AMHny5Hb7N2zYgIULFwIAli5dCovFgsWLF6Ourg7jx4/H9u3bodFoerhaIiLP46OQ8KtpAzA6IRTPffQ9jpWZMPPNPfjVtP74r5uSoPRxi9EGRNdF9stMnW0XggzQ1juTlpaGsrIyNDc3IyMjw3m3ExERXZ3JA6LwZerNuG1gFFrsDqz84jhmvbUXR4vr5S6N6LoxkhMReYkojT/+umAMVt07FMH+SuSWmDDrz3ux9J/f8xZu8mgMM0REXkSSJDwwNh5f/2oyZo0wwCGAjw4VY/Lvv8VrXxyHsYmLVZLnkXXMDBER9ZzCwkJUV1c7v364PzA+LBz/OGrGseoW/CXjDN7bdxazBgRhet8ABPr1zN+7ERERiI+P75Hn8nQ//Rm6A3f4+THMEBF5gcLCQgwcNAiWpqZOj6v7jkXIpAVoiEzEezlmvHuoAg056TAf+hStxgqX1qYOCMDx/HzZPxDd3ZV+hnJxh58fwwwRkReorq6GpakJD77we+ji+3Z6jhBAYVMrTpoUMCEAwWPuQfCYnyFGLdBHY0ekSuASk693WUXhGby/6nlUV1czzFzB1fwMe5q7/PwYZoiIvIguvi9ik4dc8ngcgIlCoLC2CYcL63G+tgklFgklFgU0/koMig7G4OhgaNW+PVc0tXOln6E3YpghIqJ2JElCQnggEsIDUd1gxdFiI05UmGFubsWBglocKKhFTIgayVFB6BsVhCAVP0pIXvwXSERElxQRpMKtA6NwS3IEzlQ14liZCYW1TSipt6Ck3oJvT1bBoPVHv6gg9IkM6jU9Nu440DY/P1/uEtwWwwwREV2R0keBAXoNBug1MDXbcLqyAacrG1BmbEbpD9uuU9UIDfBFYnggEiMCYQjxh1LheTOAuOtA2wsaGhrkLsHtMMwQEdE1Cfb3xaj4UIyKD4W52YYzVY04XdmAUqMFdU021DXV43BRPXx9JMSFBiAxPBBxYWpo1b6QunsEsQu440BbAMg/kIEvNv4Rzc3NcpfidhhmiIioyzT+vhgRF4IRcSGw2uworG3CuZomnKtpRFOLHWerG3G2uhEAEKRSIjZUjdhQNeJCAxDs5pek3G2gbUXhGblLcFsMM0RE1C1Uvj5I1mmQrNNACIGqBivO1TShsKYJ5cZmNFhbcbzcjOM/rNod7K9EqI8PgoZNRbHJhhEOAYXC/XtuyP0wzBARUbeTJAlRGn9EafwxLjEMNrsDZcZmFNc1objOggpTM0zNrTDBB+F3PoOnv6zGb3alY1R8KEYntF3CGh6nRYAfP6boyvivhIiIXM7XR4H4sADEhwUAAFpaHSg1WnD8bBG+zz2G4KShqG+y4ZvjlfjmeCUAQCEBfSODkBKjRUqMFkNjtBhsCOat4NQB/0UQEVGP81MqkBgeCGWtHds/WIZPDx6CStcXWefrnFu5qRmnKhtwqrIB2w6XAAAkCUiKCMRAvQZ9I4PQLyoIfSOD0CcykL04Xow/eSIikp1SIWF4XAiGx4XgkZuSAACVpmbklhqRU2xCbqkRuSVGlBmbcbaqEWerGjs8RkyIGn0iAxEbqka0Vo1orT8MIWrotf4waNVQ+/n09MuiHsIwQ0REbikq2B+3Bvvj1oE6577qBivySk04VWHGmaoGnKlsxOmqBtQ2tjgn8ruUYH8lQgL8oFX7IlithFbt2/b//r4IUinhp1TA10eBitJGBA69HYWNClgqzPBRSJAkCQoJUEgSFJIEyfn/uHhMITn3/fgcXx8FfDiw2aUYZoiIyGNEBKkwqX8kJvWPbLe/trEFZ6oacLaqAaX1zSgzWlBmbG7b6i1obLG3DThubr2657krFQdrANSUd0vdfj4KqHwVUPv6wN/XB8H+SmjUvtD6twWr0AA/+Puy56irGGaIiMjjhQX6ISwwDGMTwzocE0LAbG1FpakZRosNRosNJkvrj/7fhsaWVlhbHbDZBSqra7Bz1x4kDhkNpX8AHA7AIQSEaPtv29b2uI7L7BPiYg0tdgda7A6YLxOmgv2ViNL4IzJYBZ1GhWitGn5Kz5tBWQ4MM0RE1KtJkoRg/7bLSVcjOzsbHz39Wzw0ZStik+O6/LwXgk2L3YFmmx1WmwMWmx2WFjtMzba27YdQ1WBt/aHnqAGnq9qWK1BIgCFEjYTwACSEBUJc4fm8GcMMERGRC0iSBB8JUCt8oL7CJaRmmx1VZiuqzFZUmq0oM1pgam5FcZ0FxXUW7EUNVOgL7c3zYXHwctRPMcwQERHJzN/XB3FhAYj7YR4eIQTqLTacr2nC+ZpGFNdZYHX4IWTiHBxoBooOFWFwdDAG6DXw9eGlKIYZIiIXyc/Pl7sEJ3eqpTPuVJ871CJJEkID/BAa4IcRcSFotTuw49tdOHK2DOo+o52Dm/eeqcaIuBAMjw3x6gHEDDNERN3MVFsFAJg/f77MlXTU0NAgdwntsK2ujtJHgUiYUPlxGh54+a/wiR2C3BITjBYb9p+tRdb5OgyN0WJkfKhXzpDsfa+YiMjFLA0mAMCMRcsxYNhomatpk38gA19s/COam5vlLqUdttW1UykcGJEQhlFxoThV2YBD52tR3dCC7MJ6HC02YlRCKMYkhHrV5SeGGSIiFwk3JCA2eYjcZQAAKgrPyF3CZbGtrp1CIWGAXoP+uiCcr2nCgXO1KDM240BBLY6VmnBjv3AM0GkgSb1/wj6GGSIiIg8mSRISIwKREB6A05UN2HO6GqbmVnyVV4Hvi4y4dWAUIjUquct0Ke/pgyIiIurFJElCsk6Dh25IwIS+4fD1kVBuasbmg4XYf7YGdkfvnamGYYaIiKgXUfooMC4xDAsmJKJvZCAcAviuoBYfHipCdYNV7vJcgmGGiIioFwpUKTFjaDTuGKKHv1KBKrMVHxwoxMFztRCid/XSMMwQERH1UpLUNkh4/g0J6BPR1kuTeaYG/zpSCkuLXe7yug3DDBERUS8XqFLi7mHRuH1QFJQKCedrm7DpQCHKje55+/m1YpghIiLyApIkYYhBiwfGxiFE7YsGays+zirCkaJ6j7/sxDBDRETkRSKCVJgzLg79ooLgEEDGySpsP1aBVodD7tK6jGGGiIjIy6iUPrgrRY9bkiMgScDxcjO2ZZd47DgahhkiIiIvJEkSRsaH4p7hBvgpFSg1NuPDQ0WobWyRu7RrxjBDRETkxRLCA3H/6FgE+ythtNjw4aEiFNY2yV3WNWGYISIi8nLhQSo8MDYO0Vp/tLQ68K8jJThebpK7rKvGMENEREQI8FNi9sgY9Ne1DQz+Kq8CR4rq5S7rqjDMEBEREYC2pRDuGKLHiNgQAG13Ou07U+P2t27LGmZ27dqFmTNnwmAwQJIkfPLJJ+2OCyGQlpYGg8EAtVqNyZMnIy8vT55iiYiIvIAkSbilfwQm9AkHABw4V4udJ6rgcONAI2uYaWxsxPDhw7F27dpOj7/++utYvXo11q5di4MHD0Kv12Pq1Kkwm809XCkREZH3kCQJ45LCMGVAJAAgp8SIr3LL3XblbaWcT37nnXfizjvv7PSYEAJr1qzB8uXLMXv2bADAxo0bodPpsGnTJixatKgnSyUiIvI6w2JDoPb1wZd55ThZ2QCHKMcdKXr4KCS5S2vHbcfMFBQUoLy8HNOmTXPuU6lUmDRpEjIzMy/5fVarFSaTqd1GREREXZOs02DGsGj4SBJOVzXg85wyt5st2G3DTHl5OQBAp9O126/T6ZzHOrNy5UpotVrnFhcX59I6iYiIers+EUGYOTwaPgoJZ6sb8Z+jZWi1u0+gcdswc4Ekte/KEkJ02Pdjy5Ytg9FodG5FRUWuLpGIiKjXSwgPxM+GG6BUSDhX04R/Hy1Dq5vkGbcNM3q9HgA69MJUVlZ26K35MZVKheDg4HYbERERXb/4sADcM8IAXx8JhbVNyKxSQvJVyV2W+4aZpKQk6PV6pKenO/e1tLQgIyMDEydOlLEyIiIi7xUbGoBZI2Lg56NAlVWBqPt+C4tN3i4aWcNMQ0MDjhw5giNHjgBoG/R75MgRFBYWQpIkpKamYsWKFdi2bRtyc3OxcOFCBAQEYN68eXKWTURE5NUMIWrMGmmAryTgH5eCNd/Vy1qPrLdmHzp0CFOmTHF+/dxzzwEAFixYgHfeeQdLly6FxWLB4sWLUVdXh/Hjx2P79u3QaDRylUxEREQAorVq3BzViq/yKzF32nBZa5E1zEyePPmyUyRLkoS0tDSkpaX1XFFERER0VUJVAqV/fQKJjx+UtQ63HTNDREREHkDIf0sTwwwRERF5NIYZIiIi8mgMM0REROTRGGaIiIjIozHMEBERkUdjmCEiIiKPxjBDREREHo1hhoiIiDwawwwRERF5NIYZIiIi8mgMM0REROTRGGaIiIjIozHMEBERkUdjmCEiIiKPxjBDREREHo1hhoiIiDwawwwRERF5NIYZIiIi8mgMM0REROTRGGaIiIjIozHMEBERkUdjmCEiIiKPxjBDREREHo1hhoiIiDwawwwRERF5NIYZIiIi8mgMM0REROTRGGaIiIjIozHMEBERkUdjmCEiIiKPxjBDREREHo1hhoiIiDwawwwRERF5NIYZIiIi8mgMM0REROTRGGaIiIjIozHMEBERkUdjmCEiIiKPxjBDREREHo1hhoiIiDyaR4SZt956C0lJSfD398fo0aOxe/duuUsiIiIiN+H2YebDDz9Eamoqli9fjsOHD+Pmm2/GnXfeicLCQrlLIyIiIjfg9mFm9erV+K//+i88+uijGDRoENasWYO4uDisW7dO7tKIiIjIDSjlLuByWlpakJWVhRdffLHd/mnTpiEzM7PT77FarbBarc6vjUYjAMBkMnV7fQ0NDQCA4lN5sFqauv3xu6Ki8AwAoPzcSZwJDJC5movcsS53rAlwz7rcsSaAdV0Ld6wJcM+63LEmwD3rqiouAND2edjdn7MXHk8IceWThRsrKSkRAMTevXvb7X/11VdF//79O/2el19+WQDgxo0bN27cuPWCraio6Ip5wa17Zi6QJKnd10KIDvsuWLZsGZ577jnn1w6HA7W1tQgPD7/k98jFZDIhLi4ORUVFCA4OlrscWbEtLmJbXMS2aMN2uIhtcVFvbwshBMxmMwwGwxXPdeswExERAR8fH5SXl7fbX1lZCZ1O1+n3qFQqqFSqdvtCQkJcVWK3CA4O7pX/ELuCbXER2+IitkUbtsNFbIuLenNbaLXaqzrPrQcA+/n5YfTo0UhPT2+3Pz09HRMnTpSpKiIiInInbt0zAwDPPfccHnroIYwZMwYTJkzA+vXrUVhYiMcff1zu0oiIiMgNuH2YeeCBB1BTU4NXXnkFZWVlSElJweeff46EhAS5S7tuKpUKL7/8cofLYt6IbXER2+IitkUbtsNFbIuL2BYXSUJczT1PRERERO7JrcfMEBEREV0JwwwRERF5NIYZIiIi8mgMM0REROTRGGauwcqVKzF27FhoNBpERUVh1qxZOHHiRLtzhBBIS0uDwWCAWq3G5MmTkZeX1+4cq9WKp556ChEREQgMDMTPfvYzFBcXtzunrq4ODz30ELRaLbRaLR566CHU19e3O6ewsBAzZ85EYGAgIiIi8PTTT6OlpcUlr/1KVq5cCUmSkJqa6tznTW1RUlKC+fPnIzw8HAEBARgxYgSysrKcx72hLVpbW/HrX/8aSUlJUKvV6NOnD1555RU4HA7nOb21HXbt2oWZM2fCYDBAkiR88skn7Y672+vOycnBpEmToFarERMTg1deeeXq1r+5zraw2Wx44YUXMHToUAQGBsJgMODhhx9GaWmp17XFTy1atAiSJGHNmjXt9veWtnC5Li+c5IWmT58uNmzYIHJzc8WRI0fEjBkzRHx8vGhoaHCe89prrwmNRiO2bNkicnJyxAMPPCCio6OFyWRynvP444+LmJgYkZ6eLrKzs8WUKVPE8OHDRWtrq/OcO+64Q6SkpIjMzEyRmZkpUlJSxN133+083traKlJSUsSUKVNEdna2SE9PFwaDQSxZsqRnGuNHDhw4IBITE8WwYcPEM88849zvLW1RW1srEhISxMKFC8V3330nCgoKxI4dO8Tp06e9qi1+97vfifDwcPHZZ5+JgoIC8fHHH4ugoCCxZs2aXt8On3/+uVi+fLnYsmWLACC2bdvW7rg7vW6j0Sh0Op2YM2eOyMnJEVu2bBEajUb8v//3/1zeFvX19eL2228XH374oTh+/LjYt2+fGD9+vBg9enS7x/CGtvixbdu2ieHDhwuDwSDeeOONXtkWrsYwcx0qKysFAJGRkSGEEMLhcAi9Xi9ee+015znNzc1Cq9WKv/zlL0KItl9mX19fsXnzZuc5JSUlQqFQiC+//FIIIcSxY8cEALF//37nOfv27RMAxPHjx4UQbb8kCoVClJSUOM/54IMPhEqlEkaj0XUv+ifMZrNITk4W6enpYtKkSc4w401t8cILL4ibbrrpkse9pS1mzJghHnnkkXb7Zs+eLebPny+E8J52+OmHlru97rfeektotVrR3NzsPGflypXCYDAIh8PRjS3RsS06c+DAAQFAnD9/XgjhfW1RXFwsYmJiRG5urkhISGgXZnprW7gCLzNdB6PRCAAICwsDABQUFKC8vBzTpk1znqNSqTBp0iRkZmYCALKysmCz2dqdYzAYkJKS4jxn37590Gq1GD9+vPOcG264AVqttt05KSkp7Rbgmj59OqxWa7vLG6725JNPYsaMGbj99tvb7femtvj0008xZswY3HfffYiKisLIkSPx9ttvO497S1vcdNNN+Prrr3Hy5EkAwPfff489e/bgrrvuAuA97fBT7va69+3bh0mTJrWbaG369OkoLS3FuXPnur8BrsBoNEKSJOcaet7UFg6HAw899BCef/55DBkypMNxb2qL68Uw00VCCDz33HO46aabkJKSAgDOBTF/ugimTqdzHisvL4efnx9CQ0Mve05UVFSH54yKimp3zk+fJzQ0FH5+fh0W5nSVzZs3Izs7GytXruxwzJva4uzZs1i3bh2Sk5Px1Vdf4fHHH8fTTz+Nd99911kf0Pvb4oUXXsDcuXMxcOBA+Pr6YuTIkUhNTcXcuXOdtQG9vx1+yt1ed2fnXPi6p9umubkZL774IubNm+dcKNGb2mLVqlVQKpV4+umnOz3uTW1xvdx+OQN3tWTJEhw9ehR79uzpcEySpHZfCyE67Pupn57T2fldOcdVioqK8Mwzz2D79u3w9/e/5Hne0BYOhwNjxozBihUrAAAjR45EXl4e1q1bh4cffviSNfa2tvjwww/x3nvvYdOmTRgyZAiOHDmC1NRUGAwGLFiw4JL19bZ2uBR3et2d1XKp73UVm82GOXPmwOFw4K233rri+b2tLbKysvDHP/4R2dnZ1/xcva0tugN7ZrrgqaeewqeffoqdO3ciNjbWuV+v1wPomGIrKyudCVev16OlpQV1dXWXPaeioqLD81ZVVbU756fPU1dXB5vN1iFdu0JWVhYqKysxevRoKJVKKJVKZGRk4E9/+hOUSuUlE31vbIvo6GgMHjy43b5BgwahsLDQWR/Q+9vi+eefx4svvog5c+Zg6NCheOihh/Dss886e+68pR1+yt1ed2fnVFZWAujYe+QqNpsN999/PwoKCpCenu7slblQnze0xe7du1FZWYn4+Hjne+j58+fxq1/9ComJic76vKEtukVPDMzpLRwOh3jyySeFwWAQJ0+e7PS4Xq8Xq1atcu6zWq2dDvT78MMPneeUlpZ2OqDru+++c56zf//+Tgd0lZaWOs/ZvHlzjw16NZlMIicnp902ZswYMX/+fJGTk+NVbTF37twOA4BTU1PFhAkThBDe8+8iLCxMvPXWW+32rVixQiQnJwshvKcdcIkBwO7yut966y0REhIirFar85zXXnutxwa9trS0iFmzZokhQ4aIysrKDt/jLW1RXV3d4T3UYDCIF154wfkaemtbuALDzDV44oknhFarFd9++60oKytzbk1NTc5zXnvtNaHVasXWrVtFTk6OmDt3bqe3YMbGxoodO3aI7Oxsceutt3Z6q92wYcPEvn37xL59+8TQoUM7vdXutttuE9nZ2WLHjh0iNjZWlluzL/jx3UxCeE9bHDhwQCiVSvHqq6+KU6dOiffff18EBASI9957z6vaYsGCBSImJsZ5a/bWrVtFRESEWLp0aa9vB7PZLA4fPiwOHz4sAIjVq1eLw4cPO+/QcafXXV9fL3Q6nZg7d67IyckRW7duFcHBwd12C+7l2sJms4mf/exnIjY2Vhw5cqTd++iPP0S9oS0689O7mXpTW7gaw8w1ANDptmHDBuc5DodDvPzyy0Kv1wuVSiVuueUWkZOT0+5xLBaLWLJkiQgLCxNqtVrcfffdorCwsN05NTU14sEHHxQajUZoNBrx4IMPirq6unbnnD9/XsyYMUOo1WoRFhYmlixZ0u62up720zDjTW3x73//W6SkpAiVSiUGDhwo1q9f3+64N7SFyWQSzzzzjIiPjxf+/v6iT58+Yvny5e0+pHprO+zcubPT94YFCxa45es+evSouPnmm4VKpRJ6vV6kpaV121/fl2uLgoKCS76P7ty506vaojOdhZne0hauJgnhKdP7EREREXXEAcBERETk0RhmiIiIyKMxzBAREZFHY5ghIiIij8YwQ0RERB6NYYaIiIg8GsMMEREReTSGGSIiIvJoDDNE5FHeeecdhISEyF0GEbkRhhki6lGVlZVYtGgR4uPjoVKpoNfrMX36dOzbt0/u0ojIQynlLoCIvMu9994Lm82GjRs3ok+fPqioqMDXX3+N2traHquhpaUFfn5+PfZ8RORa7Jkhoh5TX1+PPXv2YNWqVZgyZQoSEhIwbtw4LFu2DDNmzAAArF69GkOHDkVgYCDi4uKwePFiNDQ0XPIxz5w5g3vuuQc6nQ5BQUEYO3YsduzY0e6cxMRE/O53v8PChQuh1Wrx2GOP4dZbb8WSJUvanVdTUwOVSoVvvvmm+188EbkMwwwR9ZigoCAEBQXhk08+gdVq7fQchUKBP/3pT8jNzcXGjRvxzTffYOnSpZd8zIaGBtx1113YsWMHDh8+jOnTp2PmzJkoLCxsd97vf/97pKSkICsrCy+99BIeffRRbNq0qV0d77//PgwGA6ZMmdI9L5iIegRXzSaiHrVlyxY89thjsFgsGDVqFCZNmoQ5c+Zg2LBhnZ7/8ccf44knnkB1dTWAtgHAqampqK+vv+RzDBkyBE888YSz5yUxMREjR47Etm3bnOdYrVYYDAasW7cO999/PwBg5MiRmDVrFl5++eVuerVE1BPYM0NEPeree+9FaWkpPv30U0yfPh3ffvstRo0ahXfeeQcAsHPnTkydOhUxMTHQaDR4+OGHUVNTg8bGxk4fr7GxEUuXLsXgwYMREhKCoKAgHD9+vEPPzJgxY9p9rVKpMH/+fPz9738HABw5cgTff/89Fi5c2O2vmYhci2GGiHqcv78/pk6dit/85jfIzMzEwoUL8fLLL+P8+fO46667kJKSgi1btiArKwt//vOfAQA2m63Tx3r++eexZcsWvPrqq9i9ezeOHDmCoUOHoqWlpd15gYGBHb730UcfRXp6OoqLi/H3v/8dt912GxISErr/BRORS/FuJiKS3eDBg/HJJ5/g0KFDaG1txR/+8AcoFG1/a3300UeX/d7du3dj4cKF+PnPfw6gbQzNuXPnrup5hw4dijFjxuDtt9/Gpk2b8Oabb17X6yAieTDMEFGPqampwX333YdHHnkEw4YNg0ajwaFDh/D666/jnnvuQd++fdHa2oo333wTM2fOxN69e/GXv/zlso/Zr18/bN26FTNnzoQkSXjppZfgcDiuuqZHH30US5YsQUBAgDMQEZFn4WUmIuoxQUFBGD9+PN544w3ccsstSElJwUsvvYTHHnsMa9euxYgRI7B69WqsWrUKKSkpeP/997Fy5crLPuYbb7yB0NBQTJw4ETNnzsT06dMxatSoq65p7ty5UCqVmDdvHvz9/a/3JRKRDHg3ExF5taKiIiQmJuLgwYPXFIKIyH0wzBCRV7LZbCgrK8OLL76I8+fPY+/evXKXRERdxMtMROSV9u7di4SEBGRlZV1xXA4RuTf2zBAREZFHY88MEREReTSGGSIiIvJoDDNERETk0RhmiIiIyKMxzBAREZFHY5ghIiIij8YwQ0RERB6NYYaIiIg82v8PnjqFMBfk7wAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## verify distribution of dataset\n",
    "for i in ncol:\n",
    "    plt.figure()\n",
    "    sns.histplot(df[i],kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b07ef10",
   "metadata": {},
   "source": [
    "## Dataset is normally distributed. So Standardscaller will use for scalling of dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "feacf4dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from math import sqrt\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "e2659530",
   "metadata": {},
   "outputs": [],
   "source": [
    "## identify independent and dependent variable\n",
    "x=df_final[['Female','Male','Age','Salary']]\n",
    "y=df_final['Purchase Iphone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "fb94d89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.02020406,  1.02020406, -1.78179743, -1.49004624],\n",
       "       [-1.02020406,  1.02020406, -0.25358736, -1.46068138],\n",
       "       [ 0.98019606, -0.98019606, -1.11320552, -0.78528968],\n",
       "       ...,\n",
       "       [ 0.98019606, -0.98019606,  1.17910958, -1.46068138],\n",
       "       [-1.02020406,  1.02020406, -0.15807423, -1.07893824],\n",
       "       [ 0.98019606, -0.98019606,  1.08359645, -0.99084367]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## scalling of x variable using standard scaller\n",
    "sc_x=StandardScaler()\n",
    "sc_x.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "f81531ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "## split the dataset into train,test\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=.80,random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "b82f3a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((320, 4), (320,))"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## shape of split data\n",
    "x_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "36cad418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80, 4), (80,))"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c453456d",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "cf01f6d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## find K values by standard method \n",
    "k1=sqrt(df_final.shape[0])\n",
    "k1\n",
    "## it is even value, so we can consider 19 or 21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "b36495ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "## find k values by error method/elbow method\n",
    "error=[]\n",
    "for i in range(1,31):\n",
    "    clf=KNeighborsClassifier(n_neighbors=i)\n",
    "    clf.fit(x_train,y_train)\n",
    "    predict_i=clf.predict(x_test)\n",
    "    error.append(1-accuracy_score(y_test,predict_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "23e8c4c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,31),error)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b6b82c",
   "metadata": {},
   "source": [
    "## lowest error values is at 15, so as per error method we can consider best K values is 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "445e1e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=15)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=15)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=15)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=KNeighborsClassifier(n_neighbors=15)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "413b0324",
   "metadata": {},
   "outputs": [],
   "source": [
    "## predict dependent variable \n",
    "y_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "e284036e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.875"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check accuracy score of predict vs actual\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "2e867a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  3],\n",
       "       [ 7, 20]], dtype=int64)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check confusion matrix of predict vs actual\n",
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "57f546d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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quLg4fI3X61VBQYGam5uj+v1I/gAAGCwrdkcgEFB6enrEEQgEjvrsLVu26Fvf+pa8Xq8WLFig5557Tuedd56CwaAkyefzRVzv8/nC544Xr/oBAGCI5Xv+lZWVKi8vjxjzer1HvX7SpEnavHmz9u7dq2eeeUbz5s1TU1NT+LzHExmbZVn9xgZC8gcAII68Xu8xk71pxIgRmjhxoiRp2rRpamlp0YMPPhie5w8Gg8rMzAxf397e3q8bMBDa/gAAGGI553/SsViWQqGQsrOz5ff71djYGD7X29urpqYm5efnR3VPKn8AAAx2vQf34x//WCUlJRo/fry6u7u1evVqvfLKK1q7dq08Ho/KyspUU1OjnJwc5eTkqKamRsnJyZozZ05UzyH5AwCQID7++GNdf/312r17t9LT0zVlyhStXbtWRUVFkqSKigodPHhQCxcuVGdnp/Ly8tTQ0KDU1NSonsN7/kAC4z1/4Mji/Z7/tsnFA190nM7c0hCze8UKlT8AAIZot+X9pmHBHwAALkPlDwCAwelf6UvyBwDA0EfbHwAAOAmVPwAABqcv+CP5AwBgiOXe/omI5A8AgCExdsCJH+b8AQBwGSp/AAAMtP0BAHAZXvUDAACOQuUPAICBV/0AAHAZVvsDAABHofIHAMDg9AV/JH8AAAxOn/On7Q8AgMtQ+QMAYHD6gj+SPwAABub8B8nZk662OwQg4fzMP9PuEABXYs4fAAA4SsJU/gAAJAra/gAAuIzD1/vR9gcAwG2o/AEAMND2BwDAZVjtDwAAHIXKHwAAQ5/dAcQZyR8AAIMl2v4AAMBBqPwBADD0OfxFf5I/AACGPoe3/Un+AAAYmPMHAACOQuUPAICBV/0AAHAZ2v4AAMBRqPwBADDQ9gcAwGWcnvxp+wMA4DJU/gAAGJy+4I/kDwCAoc/ZuZ+2PwAAbkPlDwCAgb39AQBwGYd/qR/JHwAAE6/6AQAAR6HyBwDA0Odhzh8AAFdx+pw/bX8AAFyGyh8AAIPTF/yR/AEAMLDDHwAAcBQqfwAADOzwBwCAy7DaHwAAOAqVPwAABqcv+CP5AwBg4FU/AABchjl/AADgKFT+AAAYmPMHAMBlnD7nT9sfAACXofIHAMDg9Mqf5A8AgMFy+Jw/bX8AAFyGyh8AAIPT2/5U/gAAGPpieEQjEAjooosuUmpqqsaOHavS0lK99957EddYlqXq6mplZWUpKSlJhYWFam1tjeo5JH8AABJEU1OTFi1apNdff12NjY36/PPPVVxcrJ6envA1tbW1WrJkierq6tTS0iK/36+ioiJ1d3cf93No+wMAYLBre9+1a9dGfH7sscc0duxYbdy4UZdddpksy9KyZctUVVWl2bNnS5Lq6+vl8/m0atUqzZ8//7ieQ+UPAIChzxO7IxQKqaurK+IIhULHFce+ffskSRkZGZKktrY2BYNBFRcXh6/xer0qKChQc3Pzcf9+JH8AAAyxnPMPBAJKT0+POAKBwIAxWJal8vJyXXrppcrNzZUkBYNBSZLP54u41ufzhc8dD9r+AADEUWVlpcrLyyPGvF7vgD93yy236K233tL69ev7nfN4IjcisCyr39ixkPwBADDE8lU/r9d7XMn+62699Va98MILWrduncaNGxce9/v9kr7oAGRmZobH29vb+3UDjoW2PwAABiuGR1TPtSzdcsstevbZZ/XSSy8pOzs74nx2drb8fr8aGxvDY729vWpqalJ+fv5xP4fKHwCABLFo0SKtWrVKzz//vFJTU8Pz+Onp6UpKSpLH41FZWZlqamqUk5OjnJwc1dTUKDk5WXPmzDnu55D8AQAw9Nm0t//y5cslSYWFhRHjjz32mG688UZJUkVFhQ4ePKiFCxeqs7NTeXl5amhoUGpq6nE/h+QPAIDBru19LWvgiQKPx6Pq6mpVV1ef8HOY8wcAwGWo/AEAMNi1w99gIfkDAGDoc3j6p+0PAIDLUPkDAGCwa8HfYCH5AwBgcHbTn+QPAEA/Tq/8mfMHAMBlqPwBADDYtcPfYCH5AwBg4FU/AADgKFT+AAAYnF33k/wBAOiH1f4AAMBRqPwBADA4fcEfyR8AAIOzUz9tfwAAXIfKHwAAg9MX/JH8AQAwMOcPAIDLODv1M+cPAIDrUPkDAGBgzh8AAJexHN74p+0PAIDLUPkDAGCg7Q8AgMs4/VU/2v4AALgMlT8AAAZn1/0kfwAA+nF625/kD0nSq2/+H42bcFq/8d/8erXuqQjYEBEwuKYtmqWJ37lIp56Vqc8/69XujVu1PvCU9m7bHXFd3o9mK3fOTI1MT1HwzQ/18j89rj3v77IpauDEkPwhSbrqirkaMvSrJSCTzp2oJ55doT8832hjVMDgOS3vXP1HfaM+fmubhgwdqvyKa3X1E3fpN9++S58fDEmSpv799/SXN5eo8faHtXdbUBf9w1W6+sl/1MrCO3Wo5zObfwPEktNX+7PgD5KkPZ92qqP90/BxefFl+tO2HXrj3zfYHRowKJ6/oVbvPv2q9ry/Sx3v7lDj7SuUNm60xk4+I3zNX/6v76il7nl9uHaDPn1/pxrLH9bwkSM0qTTfvsARF1YM/0lEJH/0M3z4MJVee6V+t2qN3aEAthmRmixJCu3tkSSlTRijlLGnaMe6LeFrDvd+rp1v/Kcyp+bYEiPipy+GRyKKefL/6KOPdNNNNx3zmlAopK6urojDshL1P5H7FH/3cqWlp+rp375gdyiAbS67Z652/f/39On7OyVJKWNOkSQd6NgXcd2Bjn1KGZM+2OEBJyXmyX/Pnj2qr68/5jWBQEDp6ekRx96D7bEOBSfouh9eraY//rvag5/YHQpgi8J/mafR54zX2lt+2e+cZXRxPR5PvzF88zm97R/1gr8XXjh2Nbht27YB71FZWany8vKIsSlnXBJtKIiD08Zl6pKCPP39vPKBLwYcqOC+G3Rm0YV6+trF2h/cEx7v+WSvJCllTLoOtO8NjyeNSuvXDcA3n9N70VEn/9LS0v/+m+7R/zbj8XiOeQ+v1yuv12v8DMsPEsE1c67Sp5/s0UsNr9odCjDoCv/5Bp31nWl65rqfqOujyM5X145P1NO+VxNm5OqT1u2SpCHDh2pc3jlaf/9TdoQLnLCoM25mZqaeeeYZ9fX1HfHYtGlTPOLEIPB4PLp2zlV65ql/0+HDh+0OBxhUMxffqHOuvkRrb31IvT2fKXlMupLHpGuod3j4mjd/vVYXLfobnfXX0zTq7HEq/tl8HfqsV++tabYxcsRDn2XF7EhEUVf+U6dO1aZNm1RaWnrE8wN1BZC4Li24WKeNz9LvnlxjdyjAoJtywxWSpGt+d3fEeEP5w3r36S86YRuX/17DRo7QzJ/cKG9asoKbP9SauQ/wjr8DOT2LRZ3877zzTvX09Bz1/MSJE/Xyyy+fVFCwx6uvvKbsURfYHQZgiwcn/PC4rntj6bN6Y+mzcY4GiK+ok/+MGTOOeT4lJUUFBQUnHBAAAHZjb38AAFwmUV/RixWW2AMA4DJU/gAAGHjPHwAAl2HOHwAAl2HOHwAAOAqVPwAABub8AQBwGafvVEvbHwAAl6HyBwDAwGp/AABcxulz/rT9AQBwGSp/AAAMTn/Pn+QPAIDB6XP+tP0BAHAZKn8AAAxOf8+f5A8AgMHpq/1J/gAAGJy+4I85fwAAXIbKHwAAg9NX+5P8AQAwOH3BH21/AABchsofAAADbX8AAFyG1f4AAMBRqPwBADD0OXzBH8kfAACDs1M/bX8AABLGunXrNGvWLGVlZcnj8WjNmjUR5y3LUnV1tbKyspSUlKTCwkK1trZG/RySPwAAhj5ZMTui0dPTowsuuEB1dXVHPF9bW6slS5aorq5OLS0t8vv9KioqUnd3d1TPoe0PAIAhlq/6hUIhhUKhiDGv1yuv19vv2pKSEpWUlBzxPpZladmyZaqqqtLs2bMlSfX19fL5fFq1apXmz59/3DFR+QMAYLAsK2ZHIBBQenp6xBEIBKKOqa2tTcFgUMXFxeExr9ergoICNTc3R3UvKn8AAOKosrJS5eXlEWNHqvoHEgwGJUk+ny9i3Ofzafv27VHdi+QPAIAhlm3/o7X4T5TH44n4bFlWv7GB0PYHAMBgxfCfWPH7/ZK+6gB8qb29vV83YCAkfwAAvgGys7Pl9/vV2NgYHuvt7VVTU5Py8/OjuhdtfwAADHZ9pe/+/fv1wQcfhD+3tbVp8+bNysjI0IQJE1RWVqaamhrl5OQoJydHNTU1Sk5O1pw5c6J6DskfAACDXd/qt2HDBs2cOTP8+cuFgvPmzdPjjz+uiooKHTx4UAsXLlRnZ6fy8vLU0NCg1NTUqJ7jsez6640he9QFdocAJJyylMl2hwAkpNt2PBHX+1+YeWnM7rVp9/qY3StWqPwBADAkSF0cNyR/AAAMdrX9Bwur/QEAcBkqfwAADLF8Pz8RkfwBADD0MecPAIC7OL3yZ84fAACXofIHAMBA2x8AAJeh7Q8AAByFyh8AAANtfwAAXIa2PwAAcBQqfwAADLT9AQBwGdr+AADAUaj8AQAwWFaf3SHEFckfAABDn8Pb/iR/AAAMlsMX/DHnDwCAy1D5AwBgoO0PAIDL0PYHAACOQuUPAICBHf4AAHAZdvgDAACOQuUPAIDB6Qv+SP4AABic/qofbX8AAFyGyh8AAANtfwAAXIZX/QAAcBmnV/7M+QMA4DJU/gAAGJy+2p/kDwCAgbY/AABwFCp/AAAMrPYHAMBl+GIfAADgKFT+AAAYaPsDAOAyrPYHAACOQuUPAIDB6Qv+SP4AABic3vYn+QMAYHB68mfOHwAAl6HyBwDA4Oy6X/JYTu9tICqhUEiBQECVlZXyer12hwMkBP5cwGlI/ojQ1dWl9PR07du3T2lpaXaHAyQE/lzAaZjzBwDAZUj+AAC4DMkfAACXIfkjgtfr1b333suiJuBr+HMBp2HBHwAALkPlDwCAy5D8AQBwGZI/AAAuQ/IHAMBlSP4AALgMyR9hDz30kLKzszVy5EhNnTpVr776qt0hAbZat26dZs2apaysLHk8Hq1Zs8bukICYIPlDkvTUU0+prKxMVVVVevPNNzVjxgyVlJRox44ddocG2Kanp0cXXHCB6urq7A4FiCne84ckKS8vTxdeeKGWL18eHjv33HNVWlqqQCBgY2RAYvB4PHruuedUWlpqdyjASaPyh3p7e7Vx40YVFxdHjBcXF6u5udmmqAAA8ULyhzo6OnT48GH5fL6IcZ/Pp2AwaFNUAIB4IfkjzOPxRHy2LKvfGADgm4/kD40ePVpDhw7tV+W3t7f36wYAAL75SP7QiBEjNHXqVDU2NkaMNzY2Kj8/36aoAADxMszuAJAYysvLdf3112vatGmaPn26VqxYoR07dmjBggV2hwbYZv/+/frggw/Cn9va2rR582ZlZGRowoQJNkYGnBxe9UPYQw89pNraWu3evVu5ublaunSpLrvsMrvDAmzzyiuvaObMmf3G582bp8cff3zwAwJihOQPAIDLMOcPAIDLkPwBAHAZkj8AAC5D8gcAwGVI/gAAuAzJHwAAlyH5AwDgMiR/AABchuQPAIDLkPwBAHAZkj8AAC7zX2626cNTGNtgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,y_pred),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "44c992c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=19)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=19)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=19)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check model with K value 19.\n",
    "model1=KNeighborsClassifier(n_neighbors=19)\n",
    "model1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "c7a724d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## predict dependent variable \n",
    "y_pred1=model1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "01268eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8375"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check accuracy score of predict vs actual\n",
    "accuracy_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fc3b23",
   "metadata": {},
   "source": [
    "## accuracy score is less than K value 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "fd38d66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=21)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=21)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=21)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check model with K value 21.\n",
    "model2=KNeighborsClassifier(n_neighbors=21)\n",
    "model2.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "025f37db",
   "metadata": {},
   "outputs": [],
   "source": [
    "## predict dependent variable \n",
    "y_pred2=model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "25b5010e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check accuracy score of predict vs actual\n",
    "accuracy_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96e6c3d",
   "metadata": {},
   "source": [
    "## accuracy score is less than k value 15."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2d44d3",
   "metadata": {},
   "source": [
    "# So accuracy score of K=15 is more, so i am considering this model is better to predict dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "791d57a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=15)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=15)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=15)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "953aa9cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## to save model, importing joblib\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "eb54cca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename='iphone_KNN_Project.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "07046fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iphone_KNN_Project.sav']"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,filename)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
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 },
 "nbformat": 4,
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