Data Scaling & Regularization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c58664a",
   "metadata": {},
   "source": [
    "# Data Scaling & Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc6f0bcd",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about data scaling & regularization\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfe68cb",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d5b41a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24f0116a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"wine.data\", header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88de1a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', \n",
    "              'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', \n",
    "              'Proanthocyanins', 'Color intensity', 'Hue', \n",
    "              'OD280/OD315 of diluted wines', 'Proline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "068814fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .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>Class label</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class label  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0            1    14.23        1.71  2.43               15.6        127   \n",
       "1            1    13.20        1.78  2.14               11.2        100   \n",
       "2            1    13.16        2.36  2.67               18.6        101   \n",
       "3            1    14.37        1.95  2.50               16.8        113   \n",
       "4            1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "198330bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df[\"Class label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ccc64fd",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c9068a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 1:].values\n",
    "y = df.iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "960b3d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41d56448",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,\n",
    "                                                   random_state=0,\n",
    "                                                   stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0af73e5",
   "metadata": {},
   "source": [
    "## Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c0a3466",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f507e796",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa0a7862",
   "metadata": {},
   "outputs": [],
   "source": [
    "mmscaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "935ab030",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_norm = mmscaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7b4217c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_norm = mmscaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b4b43b",
   "metadata": {},
   "source": [
    "### Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ed76e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb2f1c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "stdscaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3687b626",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_std = stdscaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc2a5977",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_std = stdscaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cdf153c",
   "metadata": {},
   "source": [
    "## Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "426861ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4128b17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(penalty = \"l1\", C = 1, solver = \"liblinear\",\n",
    "                       multi_class=\"ovr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da08ea9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, multi_class='ovr', penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "971e3ad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(X_train_std, y_train))\n",
    "print(lr.score(X_test_std, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "08967667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.30076426, -1.36983652, -2.32942517])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f3d32f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.22003455,  0.17750645,  0.71760952, -1.20444351,  0.        ,\n",
       "         0.        ,  1.34687918,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.4013148 ,  2.60815195],\n",
       "       [-1.38427374, -0.42701815, -0.93813201,  0.44982016, -0.01737509,\n",
       "         0.11361268,  0.59259624,  0.        ,  0.        , -1.94883053,\n",
       "         1.09509999,  0.        , -2.56863301],\n",
       "       [ 0.21291056,  0.08564038,  0.49567413,  0.        ,  0.        ,\n",
       "         0.        , -2.55411124,  0.        ,  0.        ,  1.64944561,\n",
       "        -1.03265586, -0.36856246,  0.        ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546f7845",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
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