Standardization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d711df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6124ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"social_network.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4187904b",
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\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>15810944</td>\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>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0  15624510    Male   19            19000          0\n",
       "1  15810944    Male   35            20000          0\n",
       "2  15668575  Female   26            43000          0\n",
       "3  15603246  Female   27            57000          0\n",
       "4  15804002    Male   19            76000          0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the top 5 values\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fdb3479b",
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>15709476</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>49000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>15776844</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>75000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>15766289</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>88000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>15793813</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>89000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>15741049</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "82   15709476    Male   20            49000          0\n",
       "341  15776844    Male   35            75000          0\n",
       "100  15766289    Male   27            88000          0\n",
       "49   15793813  Female   31            89000          0\n",
       "121  15741049    Male   37            72000          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample of the datasets\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bfb9805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71e63058",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   User ID          400 non-null    int64 \n",
      " 1   Gender           400 non-null    object\n",
      " 2   Age              400 non-null    int64 \n",
      " 3   EstimatedSalary  400 non-null    int64 \n",
      " 4   Purchased        400 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ee64275",
   "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>User ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000e+02</td>\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>1.569154e+07</td>\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>7.165832e+04</td>\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>1.556669e+07</td>\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>1.562676e+07</td>\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>1.569434e+07</td>\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>1.575036e+07</td>\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>1.581524e+07</td>\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": [
       "            User ID         Age  EstimatedSalary   Purchased\n",
       "count  4.000000e+02  400.000000       400.000000  400.000000\n",
       "mean   1.569154e+07   37.655000     69742.500000    0.357500\n",
       "std    7.165832e+04   10.482877     34096.960282    0.479864\n",
       "min    1.556669e+07   18.000000     15000.000000    0.000000\n",
       "25%    1.562676e+07   29.750000     43000.000000    0.000000\n",
       "50%    1.569434e+07   37.000000     70000.000000    0.000000\n",
       "75%    1.575036e+07   46.000000     88000.000000    1.000000\n",
       "max    1.581524e+07   60.000000    150000.000000    1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ccbf17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can use the index location \n",
    "df1=df.iloc[:,2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb421569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  EstimatedSalary  Purchased\n",
       "0   19            19000          0\n",
       "1   35            20000          0\n",
       "2   26            43000          0\n",
       "3   27            57000          0\n",
       "4   19            76000          0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "693bc287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                143\n",
       "EstimatedSalary    143\n",
       "Purchased          143\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many have purchas the car\n",
    "df1[df1['Purchased']==1].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b22ecf00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</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  EstimatedSalary   Purchased\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47819966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EstimatedSalary', ylabel='Density'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df1['EstimatedSalary'],hue=df1['Purchased'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0fb12f9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prana\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df1['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b1431179",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f25ef3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(df1.drop('Purchased',axis=1),df1['Purchased'],test_size=.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c6beb311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(280, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c05c4be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86d232c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87538b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ef29149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "009b4379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a33cbaf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scale=scale.transform(x_train)\n",
    "x_test_scale=scale.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f504396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.1631724 , -1.5849703 ],\n",
       "       [ 2.17018137,  0.93098672],\n",
       "       [ 0.0133054 ,  1.22017719],\n",
       "       [ 0.20938504,  1.07558195],\n",
       "       [ 0.40546467, -0.48604654],\n",
       "       [-0.28081405, -0.31253226],\n",
       "       [ 0.99370357, -0.8330751 ],\n",
       "       [ 0.99370357,  1.8563962 ],\n",
       "       [ 0.0133054 ,  1.24909623],\n",
       "       [-0.86905295,  2.26126285],\n",
       "       [-1.1631724 , -1.5849703 ],\n",
       "       [ 2.17018137, -0.80415605],\n",
       "       [-1.35925203, -1.46929411],\n",
       "       [ 0.40546467,  2.2901819 ],\n",
       "       [ 0.79762394,  0.75747245],\n",
       "       [-0.96709276, -0.31253226],\n",
       "       [ 0.11134522,  0.75747245],\n",
       "       [-0.96709276,  0.55503912],\n",
       "       [ 0.30742485,  0.06341534],\n",
       "       [ 0.69958412, -1.26686079],\n",
       "       [-0.47689368, -0.0233418 ],\n",
       "       [-1.7514113 ,  0.3526058 ],\n",
       "       [-0.67297331,  0.12125343],\n",
       "       [ 0.40546467,  0.29476771],\n",
       "       [-0.28081405,  0.06341534],\n",
       "       [-0.47689368,  2.2901819 ],\n",
       "       [ 0.20938504,  0.03449629],\n",
       "       [ 1.28782302,  2.20342476],\n",
       "       [ 0.79762394,  0.26584866],\n",
       "       [-0.28081405,  0.15017248],\n",
       "       [ 0.0133054 , -0.54388463],\n",
       "       [-0.18277423,  0.15017248],\n",
       "       [-0.08473441,  0.23692961],\n",
       "       [ 0.0133054 , -0.25469417],\n",
       "       [ 2.17018137,  1.104501  ],\n",
       "       [-1.7514113 ,  0.3526058 ],\n",
       "       [ 1.87606192,  0.12125343],\n",
       "       [ 0.40546467, -0.13901799],\n",
       "       [-1.1631724 ,  0.29476771],\n",
       "       [ 0.79762394,  1.36477242],\n",
       "       [-0.28081405, -0.25469417],\n",
       "       [-1.65337148, -0.05226085],\n",
       "       [-0.96709276, -0.74631796],\n",
       "       [ 0.30742485,  0.49720103],\n",
       "       [-0.08473441, -1.06442747],\n",
       "       [-1.06513258,  0.58395817],\n",
       "       [ 0.11134522, -0.80415605],\n",
       "       [-0.96709276,  1.53828669],\n",
       "       [-0.67297331,  1.39369146],\n",
       "       [-1.26121221,  0.49720103],\n",
       "       [-0.28081405,  0.03449629],\n",
       "       [-0.08473441,  0.00557724],\n",
       "       [-0.28081405, -0.89091319],\n",
       "       [ 0.89566375, -1.35361793],\n",
       "       [-0.28081405,  2.2323438 ],\n",
       "       [ 0.99370357,  1.97207239],\n",
       "       [-1.1631724 ,  0.46828198],\n",
       "       [-1.26121221,  0.26584866],\n",
       "       [ 1.38586284,  1.97207239],\n",
       "       [ 1.28782302, -1.35361793],\n",
       "       [-0.28081405, -0.28361322],\n",
       "       [-0.47689368,  1.24909623],\n",
       "       [-0.77101313,  1.07558195],\n",
       "       [ 0.99370357, -1.06442747],\n",
       "       [ 0.30742485,  0.29476771],\n",
       "       [ 0.99370357,  0.75747245],\n",
       "       [-0.67297331, -1.49821316],\n",
       "       [-0.67297331,  0.03449629],\n",
       "       [ 0.50350449,  1.71180097],\n",
       "       [ 2.07214155,  0.17909152],\n",
       "       [-1.94749093, -0.74631796],\n",
       "       [-0.18277423,  1.39369146],\n",
       "       [ 0.40546467,  0.58395817],\n",
       "       [ 0.89566375, -1.1511846 ],\n",
       "       [-1.1631724 , -0.775237  ],\n",
       "       [ 0.20938504,  0.23692961],\n",
       "       [ 0.79762394, -0.31253226],\n",
       "       [ 2.07214155, -0.80415605],\n",
       "       [ 0.79762394,  0.12125343],\n",
       "       [-0.28081405,  0.61287722],\n",
       "       [-0.96709276, -0.31253226],\n",
       "       [ 0.20938504, -0.37037036],\n",
       "       [ 2.07214155,  2.11666762],\n",
       "       [ 1.87606192, -1.26686079],\n",
       "       [ 1.38586284, -0.91983223],\n",
       "       [ 0.89566375,  1.24909623],\n",
       "       [ 1.48390265,  2.11666762],\n",
       "       [-0.28081405, -1.23794174],\n",
       "       [ 1.97410174,  0.90206768],\n",
       "       [ 0.69958412, -0.71739891],\n",
       "       [-1.45729185,  0.3526058 ],\n",
       "       [ 0.79762394, -1.35361793],\n",
       "       [ 0.40546467, -0.13901799],\n",
       "       [-0.96709276,  0.41044389],\n",
       "       [ 0.0133054 , -0.31253226],\n",
       "       [-1.1631724 ,  0.41044389],\n",
       "       [-0.86905295, -1.2090227 ],\n",
       "       [-0.08473441,  0.03449629],\n",
       "       [-1.55533166, -0.42820845],\n",
       "       [ 0.99370357, -1.00658937],\n",
       "       [ 1.09174339, -1.2090227 ],\n",
       "       [ 0.0133054 , -0.13901799],\n",
       "       [-1.06513258, -1.52713221],\n",
       "       [ 0.79762394, -1.2090227 ],\n",
       "       [ 0.99370357,  2.05882953],\n",
       "       [-1.1631724 , -1.52713221],\n",
       "       [-0.28081405,  0.78639149],\n",
       "       [ 0.11134522, -0.31253226],\n",
       "       [-1.35925203, -1.23794174],\n",
       "       [-0.5749335 , -1.49821316],\n",
       "       [ 0.79762394,  0.52612008],\n",
       "       [-0.28081405, -0.34145131],\n",
       "       [ 1.7780221 , -0.28361322],\n",
       "       [ 0.89566375, -1.03550842],\n",
       "       [ 0.20938504,  0.06341534],\n",
       "       [-0.5749335 ,  0.87314863],\n",
       "       [-1.84945111, -1.41145602],\n",
       "       [-1.26121221,  0.58395817],\n",
       "       [-0.28081405,  0.52612008],\n",
       "       [-0.96709276, -1.09334651],\n",
       "       [ 1.1897832 , -1.44037507],\n",
       "       [ 0.20938504, -0.31253226],\n",
       "       [ 1.1897832 , -0.74631796],\n",
       "       [-0.28081405,  0.06341534],\n",
       "       [ 0.20938504,  2.08774857],\n",
       "       [ 0.79762394, -1.09334651],\n",
       "       [ 0.11134522,  0.03449629],\n",
       "       [-1.7514113 ,  0.12125343],\n",
       "       [-0.86905295,  0.15017248],\n",
       "       [-0.67297331,  0.17909152],\n",
       "       [ 0.89566375, -1.29577984],\n",
       "       [ 0.20938504, -0.25469417],\n",
       "       [-0.37885386,  1.22017719],\n",
       "       [ 0.0133054 ,  0.29476771],\n",
       "       [ 0.40546467,  0.15017248],\n",
       "       [ 0.89566375, -0.65956082],\n",
       "       [ 0.11134522,  0.15017248],\n",
       "       [-1.84945111, -1.29577984],\n",
       "       [-0.08473441,  0.29476771],\n",
       "       [-0.18277423, -0.28361322],\n",
       "       [ 0.30742485, -0.51496559],\n",
       "       [-0.18277423,  1.59612479],\n",
       "       [ 0.99370357, -1.18010365],\n",
       "       [-0.18277423,  1.62504383],\n",
       "       [ 1.28782302,  1.8563962 ],\n",
       "       [-1.06513258, -0.37037036],\n",
       "       [ 0.0133054 ,  0.03449629],\n",
       "       [ 0.11134522, -0.25469417],\n",
       "       [-1.55533166, -1.23794174],\n",
       "       [-0.47689368, -0.28361322],\n",
       "       [ 0.99370357,  0.12125343],\n",
       "       [ 1.97410174, -1.35361793],\n",
       "       [ 1.48390265,  0.06341534],\n",
       "       [-0.5749335 ,  1.36477242],\n",
       "       [ 1.58194247,  0.00557724],\n",
       "       [-0.77101313,  0.29476771],\n",
       "       [ 1.97410174,  0.7285534 ],\n",
       "       [-1.1631724 , -0.51496559],\n",
       "       [ 0.69958412,  0.26584866],\n",
       "       [-1.35925203, -0.42820845],\n",
       "       [ 0.20938504,  0.15017248],\n",
       "       [-0.47689368, -1.2090227 ],\n",
       "       [ 0.6015443 ,  2.00099143],\n",
       "       [-1.55533166, -1.49821316],\n",
       "       [-0.47689368, -0.54388463],\n",
       "       [ 0.50350449,  1.82747716],\n",
       "       [-1.35925203, -1.09334651],\n",
       "       [ 0.79762394, -1.38253697],\n",
       "       [-0.28081405, -0.42820845],\n",
       "       [ 1.58194247,  0.98882482],\n",
       "       [ 0.99370357,  1.42261051],\n",
       "       [-0.28081405, -0.48604654],\n",
       "       [-0.08473441,  2.14558666],\n",
       "       [-1.45729185, -0.11009894],\n",
       "       [-0.08473441,  1.94315334],\n",
       "       [-0.67297331, -0.34145131],\n",
       "       [-0.47689368, -0.8330751 ],\n",
       "       [ 0.69958412, -1.38253697],\n",
       "       [-0.77101313, -1.5849703 ],\n",
       "       [-1.84945111, -1.46929411],\n",
       "       [ 1.09174339,  0.12125343],\n",
       "       [ 0.11134522,  1.50936765],\n",
       "       [-0.28081405,  0.09233438],\n",
       "       [ 0.11134522,  0.03449629],\n",
       "       [-1.35925203, -1.35361793],\n",
       "       [ 0.30742485,  0.06341534],\n",
       "       [-0.86905295,  0.38152485],\n",
       "       [ 1.58194247, -1.26686079],\n",
       "       [-0.28081405, -0.74631796],\n",
       "       [-0.08473441,  0.15017248],\n",
       "       [-0.86905295, -0.65956082],\n",
       "       [-0.67297331, -0.05226085],\n",
       "       [ 0.40546467, -0.45712749],\n",
       "       [-0.77101313,  1.88531525],\n",
       "       [ 1.38586284,  1.27801528],\n",
       "       [ 1.1897832 , -0.97767033],\n",
       "       [ 1.7780221 ,  1.82747716],\n",
       "       [-0.86905295, -0.25469417],\n",
       "       [-0.77101313,  0.55503912],\n",
       "       [-1.1631724 , -1.55605125],\n",
       "       [-0.47689368, -1.12226556],\n",
       "       [ 0.30742485,  0.06341534],\n",
       "       [-0.18277423, -1.06442747],\n",
       "       [ 1.67998229,  1.59612479],\n",
       "       [ 0.99370357,  1.76963906],\n",
       "       [ 0.30742485,  0.03449629],\n",
       "       [-0.77101313, -0.22577513],\n",
       "       [-0.08473441,  0.06341534],\n",
       "       [ 0.30742485, -0.19685608],\n",
       "       [ 1.97410174, -0.65956082],\n",
       "       [-0.77101313,  1.33585337],\n",
       "       [-1.7514113 , -0.60172273],\n",
       "       [-0.08473441,  0.12125343],\n",
       "       [ 0.30742485, -0.31253226],\n",
       "       [ 1.09174339,  0.55503912],\n",
       "       [-0.96709276,  0.26584866],\n",
       "       [ 1.48390265,  0.3526058 ],\n",
       "       [ 0.20938504, -0.37037036],\n",
       "       [ 2.17018137, -1.03550842],\n",
       "       [-0.28081405,  1.104501  ],\n",
       "       [-1.65337148,  0.06341534],\n",
       "       [ 0.0133054 ,  0.03449629],\n",
       "       [ 0.11134522,  1.04666291],\n",
       "       [-0.08473441, -0.37037036],\n",
       "       [-1.1631724 ,  0.06341534],\n",
       "       [-0.28081405, -1.35361793],\n",
       "       [ 1.58194247,  1.104501  ],\n",
       "       [-0.77101313, -1.52713221],\n",
       "       [ 0.11134522,  1.8563962 ],\n",
       "       [-0.86905295, -0.775237  ],\n",
       "       [-0.47689368, -0.775237  ],\n",
       "       [-0.28081405, -0.91983223],\n",
       "       [ 0.30742485, -0.71739891],\n",
       "       [ 0.30742485,  0.06341534],\n",
       "       [ 0.11134522,  1.8563962 ],\n",
       "       [-1.06513258,  1.94315334],\n",
       "       [-1.65337148, -1.55605125],\n",
       "       [-1.1631724 , -1.09334651],\n",
       "       [-0.67297331, -0.11009894],\n",
       "       [ 0.11134522,  0.09233438],\n",
       "       [ 0.30742485,  0.26584866],\n",
       "       [ 0.89566375, -0.57280368],\n",
       "       [ 0.30742485, -1.1511846 ],\n",
       "       [-0.08473441,  0.67071531],\n",
       "       [ 2.17018137, -0.68847986],\n",
       "       [-1.26121221, -1.38253697],\n",
       "       [-0.96709276, -0.94875128],\n",
       "       [ 0.0133054 , -0.42820845],\n",
       "       [-0.18277423, -0.45712749],\n",
       "       [-1.7514113 , -0.97767033],\n",
       "       [ 1.7780221 ,  0.98882482],\n",
       "       [ 0.20938504, -0.37037036],\n",
       "       [ 0.40546467,  1.104501  ],\n",
       "       [-1.7514113 , -1.35361793],\n",
       "       [ 0.20938504, -0.13901799],\n",
       "       [ 0.89566375, -1.44037507],\n",
       "       [-1.94749093,  0.46828198],\n",
       "       [-0.28081405,  0.26584866],\n",
       "       [ 1.87606192, -1.06442747],\n",
       "       [-0.37885386,  0.06341534],\n",
       "       [ 1.09174339, -0.89091319],\n",
       "       [-1.06513258, -1.12226556],\n",
       "       [-1.84945111,  0.00557724],\n",
       "       [ 0.11134522,  0.26584866],\n",
       "       [-1.1631724 ,  0.32368675],\n",
       "       [-1.26121221,  0.29476771],\n",
       "       [-0.96709276,  0.43936294],\n",
       "       [ 1.67998229, -0.89091319],\n",
       "       [ 1.1897832 ,  0.52612008],\n",
       "       [ 1.09174339,  0.52612008],\n",
       "       [ 1.38586284,  2.31910094],\n",
       "       [-0.28081405, -0.13901799],\n",
       "       [ 0.40546467, -0.45712749],\n",
       "       [-0.37885386, -0.775237  ],\n",
       "       [-0.08473441, -0.51496559],\n",
       "       [ 0.99370357, -1.1511846 ],\n",
       "       [-0.86905295, -0.775237  ],\n",
       "       [-0.18277423, -0.51496559],\n",
       "       [-1.06513258, -0.45712749],\n",
       "       [-1.1631724 ,  1.39369146]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d88ddc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.77101313,  0.49720103],\n",
       "       [ 0.0133054 , -0.57280368],\n",
       "       [-0.28081405,  0.15017248],\n",
       "       [-0.77101313,  0.26584866],\n",
       "       [-0.28081405, -0.57280368],\n",
       "       [-1.06513258, -1.44037507],\n",
       "       [-0.67297331, -1.5849703 ],\n",
       "       [-0.18277423,  2.14558666],\n",
       "       [-1.94749093, -0.05226085],\n",
       "       [ 0.89566375, -0.775237  ],\n",
       "       [-0.77101313, -0.60172273],\n",
       "       [-0.96709276, -0.42820845],\n",
       "       [-0.08473441, -0.42820845],\n",
       "       [ 0.11134522,  0.20801057],\n",
       "       [-1.7514113 ,  0.46828198],\n",
       "       [-0.5749335 ,  1.36477242],\n",
       "       [-0.08473441,  0.20801057],\n",
       "       [-1.84945111,  0.43936294],\n",
       "       [ 1.67998229,  1.74072002],\n",
       "       [-0.28081405, -1.38253697],\n",
       "       [-0.28081405, -0.65956082],\n",
       "       [ 0.89566375,  2.14558666],\n",
       "       [ 0.30742485, -0.54388463],\n",
       "       [ 0.89566375,  1.01774386],\n",
       "       [-1.45729185, -1.2090227 ],\n",
       "       [ 1.09174339,  2.05882953],\n",
       "       [-0.96709276,  0.49720103],\n",
       "       [-0.86905295,  0.29476771],\n",
       "       [-0.08473441, -0.22577513],\n",
       "       [-0.5749335 ,  0.46828198],\n",
       "       [-1.65337148,  0.52612008],\n",
       "       [-0.08473441,  0.26584866],\n",
       "       [ 1.87606192, -0.28361322],\n",
       "       [-0.08473441, -0.48604654],\n",
       "       [-1.35925203, -0.34145131],\n",
       "       [-1.94749093, -0.51496559],\n",
       "       [-1.55533166,  0.32368675],\n",
       "       [-0.37885386, -0.775237  ],\n",
       "       [-0.67297331, -1.03550842],\n",
       "       [ 1.09174339, -0.97767033],\n",
       "       [-1.06513258,  0.52612008],\n",
       "       [ 0.30742485, -0.51496559],\n",
       "       [-1.06513258,  0.41044389],\n",
       "       [-0.28081405, -1.44037507],\n",
       "       [ 0.50350449,  1.22017719],\n",
       "       [-1.06513258, -0.34145131],\n",
       "       [-0.08473441,  0.29476771],\n",
       "       [ 1.38586284,  0.58395817],\n",
       "       [-1.1631724 , -1.1511846 ],\n",
       "       [ 1.09174339,  0.46828198],\n",
       "       [ 1.87606192,  1.50936765],\n",
       "       [-0.37885386, -1.29577984],\n",
       "       [-0.28081405, -0.37037036],\n",
       "       [-0.37885386,  1.30693432],\n",
       "       [ 2.07214155,  0.52612008],\n",
       "       [ 0.69958412, -1.09334651],\n",
       "       [-0.86905295,  0.38152485],\n",
       "       [-1.1631724 ,  0.29476771],\n",
       "       [ 1.09174339, -1.2090227 ],\n",
       "       [-1.45729185, -1.44037507],\n",
       "       [-0.5749335 , -1.49821316],\n",
       "       [ 2.17018137, -0.80415605],\n",
       "       [-1.84945111,  0.17909152],\n",
       "       [-0.18277423,  0.84422958],\n",
       "       [-1.84945111, -1.26686079],\n",
       "       [ 2.17018137,  0.38152485],\n",
       "       [-1.35925203,  0.55503912],\n",
       "       [-1.06513258, -0.34145131],\n",
       "       [ 0.20938504, -0.65956082],\n",
       "       [ 0.40546467,  0.00557724],\n",
       "       [-0.5749335 ,  2.31910094],\n",
       "       [-0.28081405,  0.20801057],\n",
       "       [-1.55533166, -0.19685608],\n",
       "       [ 0.69958412, -1.38253697],\n",
       "       [-1.06513258,  0.55503912],\n",
       "       [-1.94749093,  0.3526058 ],\n",
       "       [ 0.40546467,  0.26584866],\n",
       "       [ 0.20938504, -0.28361322],\n",
       "       [ 1.48390265, -1.03550842],\n",
       "       [ 0.89566375,  1.07558195],\n",
       "       [ 1.97410174,  2.14558666],\n",
       "       [ 2.07214155,  0.38152485],\n",
       "       [-1.35925203, -0.42820845],\n",
       "       [-1.1631724 , -1.00658937],\n",
       "       [ 1.97410174, -0.91983223],\n",
       "       [ 0.40546467,  0.29476771],\n",
       "       [ 0.20938504,  0.15017248],\n",
       "       [ 2.07214155,  1.74072002],\n",
       "       [ 0.79762394, -0.8330751 ],\n",
       "       [ 0.30742485, -0.28361322],\n",
       "       [ 0.40546467, -0.16793703],\n",
       "       [-0.08473441,  2.20342476],\n",
       "       [-1.45729185, -0.63064177],\n",
       "       [-1.26121221, -1.06442747],\n",
       "       [-1.35925203,  0.41044389],\n",
       "       [-1.06513258,  0.75747245],\n",
       "       [-1.45729185, -0.19685608],\n",
       "       [ 0.99370357, -1.06442747],\n",
       "       [ 0.99370357,  0.58395817],\n",
       "       [ 0.40546467,  0.98882482],\n",
       "       [ 0.6015443 , -0.89091319],\n",
       "       [-0.5749335 ,  1.45152956],\n",
       "       [ 0.0133054 , -0.57280368],\n",
       "       [-0.5749335 ,  1.88531525],\n",
       "       [ 1.38586284, -1.41145602],\n",
       "       [ 1.48390265,  0.98882482],\n",
       "       [ 0.11134522, -0.80415605],\n",
       "       [ 0.0133054 , -0.25469417],\n",
       "       [-0.18277423, -0.57280368],\n",
       "       [-0.18277423, -0.19685608],\n",
       "       [-0.28081405, -1.29577984],\n",
       "       [-0.28081405, -0.57280368],\n",
       "       [ 0.40546467,  0.09233438],\n",
       "       [ 0.89566375, -0.60172273],\n",
       "       [ 2.07214155, -1.18010365],\n",
       "       [ 1.09174339, -0.13901799],\n",
       "       [ 0.69958412,  1.76963906],\n",
       "       [-0.67297331,  0.55503912],\n",
       "       [ 0.79762394,  0.3526058 ],\n",
       "       [ 0.89566375, -0.54388463]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a6ead300",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled=pd.DataFrame(x_train_scale,columns=x_train.columns)\n",
    "x_test_scaled=pd.DataFrame(x_test_scale,columns=x_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a821a3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>280.0</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.9</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age  EstimatedSalary\n",
       "count  280.0            280.0\n",
       "mean     0.0              0.0\n",
       "std      1.0              1.0\n",
       "min     -1.9             -1.6\n",
       "25%     -0.8             -0.8\n",
       "50%     -0.1              0.0\n",
       "75%      0.8              0.5\n",
       "max      2.2              2.3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(x_train_scaled.describe(),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2e0e7ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>280.0</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.9</td>\n",
       "      <td>69807.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.2</td>\n",
       "      <td>34641.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.0</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.0</td>\n",
       "      <td>43000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.0</td>\n",
       "      <td>70500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.0</td>\n",
       "      <td>88000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age  EstimatedSalary\n",
       "count  280.0            280.0\n",
       "mean    37.9          69807.1\n",
       "std     10.2          34641.2\n",
       "min     18.0          15000.0\n",
       "25%     30.0          43000.0\n",
       "50%     37.0          70500.0\n",
       "75%     46.0          88000.0\n",
       "max     60.0         150000.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(x_train.describe(),1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0916b8a",
   "metadata": {},
   "source": [
    "# Effect of Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e818a43f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After scaling')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x_train['Age'],x_train['EstimatedSalary'])\n",
    "plt.title('Before scaling')\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x_train_scaled['Age'],x_train_scaled['EstimatedSalary'],color='red')\n",
    "plt.title('After scaling')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "410aec5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prana\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\prana\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\prana\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\prana\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EstimatedSalary', ylabel='Density'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1,2,1)\n",
    "sns.distplot(x_train['Age'])\n",
    "sns.distplot(x_train['EstimatedSalary'])\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(x_train_scaled['Age'])\n",
    "sns.distplot(x_train_scaled['EstimatedSalary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0270167d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1,2,1)\n",
    "sns.kdeplot(x_train['Age'])\n",
    "sns.kdeplot(x_train['EstimatedSalary'])\n",
    "plt.subplot(1,2,2)\n",
    "sns.kdeplot(x_train_scaled['Age'])\n",
    "sns.kdeplot(x_train_scaled['EstimatedSalary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e03638",
   "metadata": {},
   "source": [
    "# Why Scaling is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4b308f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7c32c013",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression()\n",
    "lr_scaled=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2f1773a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)\n",
    "lr_scaled.fit(x_train_scaled,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "58759238",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(x_test)\n",
    "y_pred_scaled=lr_scaled.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3210293d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "670e6fdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual: 0.6583333333333333\n",
      "scaled: 0.8666666666666667\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual:\",accuracy_score(y_test,y_pred))\n",
    "print('scaled:',accuracy_score(y_test,y_pred_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96ea358f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}