Standardization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b60aede",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0998fccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a433d165",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10001f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47</td>\n",
       "      <td>25000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>22</td>\n",
       "      <td>81000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>26</td>\n",
       "      <td>86000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>23</td>\n",
       "      <td>63000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>19</td>\n",
       "      <td>21000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     Age  EstimatedSalary  Purchased\n",
       "16    47            25000          1\n",
       "90    22            81000          0\n",
       "108   26            86000          0\n",
       "135   23            63000          0\n",
       "104   19            21000          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4be88cbc",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33f8fce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((280, 2), (120, 2))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('Purchased', axis=1),\n",
    "                                                    df['Purchased'],\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a928225c",
   "metadata": {},
   "source": [
    "## StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6a404a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# fit the scaler to the train set, it will learn the parameters\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform train and test sets\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56b10e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.78642857e+01, 6.98071429e+04])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.mean_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9834218b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "</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>92</th>\n",
       "      <td>26</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>60</td>\n",
       "      <td>102000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>38</td>\n",
       "      <td>112000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>40</td>\n",
       "      <td>107000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>42</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>48</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>29</td>\n",
       "      <td>43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>36</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>27</td>\n",
       "      <td>54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>26</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EstimatedSalary\n",
       "92    26            15000\n",
       "223   60           102000\n",
       "234   38           112000\n",
       "232   40           107000\n",
       "377   42            53000\n",
       "..   ...              ...\n",
       "323   48            30000\n",
       "192   29            43000\n",
       "117   36            52000\n",
       "47    27            54000\n",
       "172   26           118000\n",
       "\n",
       "[280 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6370997e",
   "metadata": {},
   "outputs": [
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       "       [-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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4a5f148",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f4ca83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(X_train.describe(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c541901b",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(X_train_scaled.describe(), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb768b7f",
   "metadata": {},
   "source": [
    "## Effect of Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6120629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "ax1.scatter(X_train['Age'], X_train['EstimatedSalary'])\n",
    "ax1.set_title(\"Before Scaling\")\n",
    "ax2.scatter(X_train_scaled['Age'], X_train_scaled['EstimatedSalary'],color='red')\n",
    "ax2.set_title(\"After Scaling\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c325e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# before scaling\n",
    "ax1.set_title('Before Scaling')\n",
    "sns.kdeplot(X_train['Age'], ax=ax1)\n",
    "sns.kdeplot(X_train['EstimatedSalary'], ax=ax1)\n",
    "\n",
    "# after scaling\n",
    "ax2.set_title('After Standard Scaling')\n",
    "sns.kdeplot(X_train_scaled['Age'], ax=ax2)\n",
    "sns.kdeplot(X_train_scaled['EstimatedSalary'], ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c8efdec",
   "metadata": {},
   "source": [
    "## Comparison of Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "198b0eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# before scaling\n",
    "ax1.set_title('Age Distribution Before Scaling')\n",
    "sns.kdeplot(X_train['Age'], ax=ax1)\n",
    "\n",
    "# after scaling\n",
    "ax2.set_title('Age Distribution After Standard Scaling')\n",
    "sns.kdeplot(X_train_scaled['Age'], ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6bed8bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "# before scaling\n",
    "ax1.set_title('Salary Distribution Before Scaling')\n",
    "sns.kdeplot(X_train['EstimatedSalary'], ax=ax1)\n",
    "\n",
    "# after scaling\n",
    "ax2.set_title('Salary Distribution Standard Scaling')\n",
    "sns.kdeplot(X_train_scaled['EstimatedSalary'], ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd8f132a",
   "metadata": {},
   "source": [
    "## Why scaling is important?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "522e6cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae172477",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()\n",
    "lr_scaled = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4089ddc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)\n",
    "lr_scaled.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "20ee8981",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "y_pred_scaled = lr_scaled.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f598042d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ccc8beff",
   "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": 24,
   "id": "c37ee15b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f0e2830d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt_scaled = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c496d904",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(X_train,y_train)\n",
    "dt_scaled.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e250d242",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dt.predict(X_test)\n",
    "y_pred_scaled = dt_scaled.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "62bfa737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual 0.8666666666666667\n",
      "Scaled 0.875\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": 29,
   "id": "7949995a",
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71be669",
   "metadata": {},
   "source": [
    "## Effect of Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb3b0743",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_5260\\2823321922.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(pd.DataFrame({'Age': [5, 90, 95],\n"
     ]
    }
   ],
   "source": [
    "df = df.append(pd.DataFrame({'Age': [5, 90, 95], \n",
    "                             'EstimatedSalary': [1000, 250000, 350000], \n",
    "                             'Purchased': [0, 1, 1]}), ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5831f9bf",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>90</td>\n",
       "      <td>250000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>95</td>\n",
       "      <td>350000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 3 columns</p>\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\n",
       "..   ...              ...        ...\n",
       "398   36            33000          0\n",
       "399   49            36000          1\n",
       "400    5             1000          0\n",
       "401   90           250000          1\n",
       "402   95           350000          1\n",
       "\n",
       "[403 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b142b4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20aec22eac0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Age'], df['EstimatedSalary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d9025014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((282, 2), (121, 2))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('Purchased', axis=1),\n",
    "                                                    df['Purchased'],\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c4c1a0b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# fit the scaler to the train set, it will learn the parameters\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform train and test sets\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3dee8783",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "08f31a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "ax1.scatter(X_train['Age'], X_train['EstimatedSalary'])\n",
    "ax1.set_title(\"Before Scaling\")\n",
    "ax2.scatter(X_train_scaled['Age'], X_train_scaled['EstimatedSalary'],color='red')\n",
    "ax2.set_title(\"After Scaling\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}