Feature Scaling.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f707c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4bc3f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set = pd.read_csv('feature scaling data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ea6e221",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30</td>\n",
       "      <td>54000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38</td>\n",
       "      <td>61000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40</td>\n",
       "      <td>70000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35</td>\n",
       "      <td>58000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>31</td>\n",
       "      <td>52000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48</td>\n",
       "      <td>79000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50</td>\n",
       "      <td>83000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37</td>\n",
       "      <td>67000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Country  Age  Salary Purchased\n",
       "0   France   44   72000        No\n",
       "1    Spain   27   48000       Yes\n",
       "2  Germany   30   54000        No\n",
       "3    Spain   38   61000        No\n",
       "4  Germany   40   70000       Yes\n",
       "5   France   35   58000       Yes\n",
       "6    Spain   31   52000        No\n",
       "7   France   48   79000       Yes\n",
       "8  Germany   50   83000        No\n",
       "9   France   37   67000       Yes"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94c2a285",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data_set.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "233bb300",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
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       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
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       "      <th>2</th>\n",
       "      <td>30</td>\n",
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       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48</td>\n",
       "      <td>79000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>83000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37</td>\n",
       "      <td>67000</td>\n",
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      "text/plain": [
       "   Age  Salary\n",
       "0   44   72000\n",
       "1   27   48000\n",
       "2   30   54000\n",
       "3   38   61000\n",
       "4   40   70000\n",
       "5   35   58000\n",
       "6   31   52000\n",
       "7   48   79000\n",
       "8   50   83000\n",
       "9   37   67000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "950b27f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MIN MAX Scaler\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c7f9445",
   "metadata": {},
   "outputs": [],
   "source": [
    "MMS =MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b3eef616",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_after_min_max_scaler=MMS.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a45c185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.73913043, 0.68571429],\n",
       "       [0.        , 0.        ],\n",
       "       [0.13043478, 0.17142857],\n",
       "       [0.47826087, 0.37142857],\n",
       "       [0.56521739, 0.62857143],\n",
       "       [0.34782609, 0.28571429],\n",
       "       [0.17391304, 0.11428571],\n",
       "       [0.91304348, 0.88571429],\n",
       "       [1.        , 1.        ],\n",
       "       [0.43478261, 0.54285714]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_after_min_max_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c87742d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardization\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ebf799ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "Standardisation=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0119068c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_after_Standardisation = Standardisation.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f9d35823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.82572282,  0.6829409 ],\n",
       "       [-1.51382518, -1.47371457],\n",
       "       [-1.10096377, -0.9345507 ],\n",
       "       [ 0.        , -0.30552619],\n",
       "       [ 0.27524094,  0.50321961],\n",
       "       [-0.41286141, -0.57510812],\n",
       "       [-0.96334329, -1.11427199],\n",
       "       [ 1.37620471,  1.31196541],\n",
       "       [ 1.65144565,  1.67140798],\n",
       "       [-0.13762047,  0.23363768]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_after_Standardisation"
   ]
  }
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