14 Feature Scaling_ Standardization Normalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling\n",
    "\n",
    "The range of values of raw data varies widely, in some machine learning algorithms, functions will not work properly without normalization.\n",
    "\n",
    "2 types of Feature Scaling:\n",
    "\n",
    " - Standardization (or Z-score normalization)\n",
    "\n",
    " - Normalization (or Min-Max scaling)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Values\n",
       "0       47\n",
       "1        7\n",
       "2       21\n",
       "3       28\n",
       "4       41\n",
       "5       49\n",
       "6       50\n",
       "7       25\n",
       "8       25\n",
       "9       35\n",
       "10      24"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame({'Values':[47,7,21,28,41,49,50,25,25,35,24]})\n",
    "data"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardization (Z-score normalization)\n",
    "Rescaling of the features so that they have the properties of a standard normal distribution with μ=0 and σ=1,\n",
    "where μ is the mean (average) and σ is the standard deviation from the mean.\n",
    "\n",
    "About 68% of the values will lie be between -1 and 1.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scale = StandardScaler().fit(data)\n",
    "data_scaled = scale.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.15602469],\n",
       "       [-1.92670782],\n",
       "       [-0.84775144],\n",
       "       [-0.30827325],\n",
       "       [ 0.69361482],\n",
       "       [ 1.31016132],\n",
       "       [ 1.38722963],\n",
       "       [-0.53947819],\n",
       "       [-0.53947819],\n",
       "       [ 0.23120494],\n",
       "       [-0.6165465 ]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_scaled"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalization(Min-Max scaling)\n",
    "The data is scaled to a fixed range usually 0 to 1.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "norm = MinMaxScaler().fit(data) \n",
    "data_norm = norm.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93023256],\n",
       "       [0.        ],\n",
       "       [0.3255814 ],\n",
       "       [0.48837209],\n",
       "       [0.79069767],\n",
       "       [0.97674419],\n",
       "       [1.        ],\n",
       "       [0.41860465],\n",
       "       [0.41860465],\n",
       "       [0.65116279],\n",
       "       [0.39534884]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Standardized']=data_scaled\n",
    "\n",
    "data['Normalized']=data_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Values</th>\n",
       "      <th>Standardized</th>\n",
       "      <th>Normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>1.156025</td>\n",
       "      <td>0.930233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>-1.926708</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>-0.847751</td>\n",
       "      <td>0.325581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>-0.308273</td>\n",
       "      <td>0.488372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0.693615</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49</td>\n",
       "      <td>1.310161</td>\n",
       "      <td>0.976744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>1.387230</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25</td>\n",
       "      <td>-0.539478</td>\n",
       "      <td>0.418605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25</td>\n",
       "      <td>-0.539478</td>\n",
       "      <td>0.418605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35</td>\n",
       "      <td>0.231205</td>\n",
       "      <td>0.651163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "      <td>-0.616547</td>\n",
       "      <td>0.395349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Values  Standardized  Normalized\n",
       "0       47      1.156025    0.930233\n",
       "1        7     -1.926708    0.000000\n",
       "2       21     -0.847751    0.325581\n",
       "3       28     -0.308273    0.488372\n",
       "4       41      0.693615    0.790698\n",
       "5       49      1.310161    0.976744\n",
       "6       50      1.387230    1.000000\n",
       "7       25     -0.539478    0.418605\n",
       "8       25     -0.539478    0.418605\n",
       "9       35      0.231205    0.651163\n",
       "10      24     -0.616547    0.395349"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['SNo']=data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(1,3)\n",
    "data.plot(kind='scatter',x='SNo', y='Values',color='r',ax=axs[0],figsize=(9,5))\n",
    "data.plot(kind='scatter',x='SNo', y='Standardized',color='b',ax=axs[1])\n",
    "data.plot(kind='scatter',x='SNo', y='Normalized',color='g',ax=axs[2])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}