Linear Discriminant Analysis (LDA).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Wine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Ash_Alcanity</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</th>\n",
       "      <th>Proline</th>\n",
       "      <th>Customer_Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>5.640000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>4.380000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>5.680000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>7.800000</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>4.320000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1295</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.27</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1510</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.32</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1280</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1320</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.4</td>\n",
       "      <td>91</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.38</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.96</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.63</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>17.2</td>\n",
       "      <td>112</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1310</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.30</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.72</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1280</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.62</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.19</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.48</td>\n",
       "      <td>16.5</td>\n",
       "      <td>108</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.86</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.64</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.56</td>\n",
       "      <td>15.2</td>\n",
       "      <td>116</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.36</td>\n",
       "      <td>845</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.06</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>126</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.71</td>\n",
       "      <td>780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.93</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.65</td>\n",
       "      <td>18.6</td>\n",
       "      <td>102</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.52</td>\n",
       "      <td>770</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.71</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.11</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1035</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.52</td>\n",
       "      <td>17.8</td>\n",
       "      <td>95</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.82</td>\n",
       "      <td>845</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>124</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.20</td>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.62</td>\n",
       "      <td>16.1</td>\n",
       "      <td>93</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.45</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1195</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13.30</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.14</td>\n",
       "      <td>17.0</td>\n",
       "      <td>94</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13.87</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.80</td>\n",
       "      <td>19.4</td>\n",
       "      <td>107</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.40</td>\n",
       "      <td>915</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.02</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.21</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1035</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>13.32</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.5</td>\n",
       "      <td>92</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62</td>\n",
       "      <td>650</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>13.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>123</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.30</td>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12.79</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>480</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>116</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>425</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>13.23</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.5</td>\n",
       "      <td>98</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.87</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.51</td>\n",
       "      <td>675</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>12.58</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.55</td>\n",
       "      <td>640</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>13.17</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.55</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>725</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>12.45</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.64</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.73</td>\n",
       "      <td>880</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>13.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.64</td>\n",
       "      <td>22.5</td>\n",
       "      <td>89</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.29</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.78</td>\n",
       "      <td>620</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>13.69</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>107</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.82</td>\n",
       "      <td>680</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>12.96</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.5</td>\n",
       "      <td>106</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.75</td>\n",
       "      <td>675</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>13.73</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.5</td>\n",
       "      <td>88</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>520</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>13.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.56</td>\n",
       "      <td>695</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.58</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.69</td>\n",
       "      <td>24.5</td>\n",
       "      <td>105</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>13.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.92</td>\n",
       "      <td>630</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>12.77</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.28</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.899999</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.63</td>\n",
       "      <td>470</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>14.16</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.48</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.24</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.71</td>\n",
       "      <td>660</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Alcohol  Malic_Acid   Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
       "0      14.23        1.71  2.43          15.6        127           2.80   \n",
       "1      13.20        1.78  2.14          11.2        100           2.65   \n",
       "2      13.16        2.36  2.67          18.6        101           2.80   \n",
       "3      14.37        1.95  2.50          16.8        113           3.85   \n",
       "4      13.24        2.59  2.87          21.0        118           2.80   \n",
       "5      14.20        1.76  2.45          15.2        112           3.27   \n",
       "6      14.39        1.87  2.45          14.6         96           2.50   \n",
       "7      14.06        2.15  2.61          17.6        121           2.60   \n",
       "8      14.83        1.64  2.17          14.0         97           2.80   \n",
       "9      13.86        1.35  2.27          16.0         98           2.98   \n",
       "10     14.10        2.16  2.30          18.0        105           2.95   \n",
       "11     14.12        1.48  2.32          16.8         95           2.20   \n",
       "12     13.75        1.73  2.41          16.0         89           2.60   \n",
       "13     14.75        1.73  2.39          11.4         91           3.10   \n",
       "14     14.38        1.87  2.38          12.0        102           3.30   \n",
       "15     13.63        1.81  2.70          17.2        112           2.85   \n",
       "16     14.30        1.92  2.72          20.0        120           2.80   \n",
       "17     13.83        1.57  2.62          20.0        115           2.95   \n",
       "18     14.19        1.59  2.48          16.5        108           3.30   \n",
       "19     13.64        3.10  2.56          15.2        116           2.70   \n",
       "20     14.06        1.63  2.28          16.0        126           3.00   \n",
       "21     12.93        3.80  2.65          18.6        102           2.41   \n",
       "22     13.71        1.86  2.36          16.6        101           2.61   \n",
       "23     12.85        1.60  2.52          17.8         95           2.48   \n",
       "24     13.50        1.81  2.61          20.0         96           2.53   \n",
       "25     13.05        2.05  3.22          25.0        124           2.63   \n",
       "26     13.39        1.77  2.62          16.1         93           2.85   \n",
       "27     13.30        1.72  2.14          17.0         94           2.40   \n",
       "28     13.87        1.90  2.80          19.4        107           2.95   \n",
       "29     14.02        1.68  2.21          16.0         96           2.65   \n",
       "..       ...         ...   ...           ...        ...            ...   \n",
       "148    13.32        3.24  2.38          21.5         92           1.93   \n",
       "149    13.08        3.90  2.36          21.5        113           1.41   \n",
       "150    13.50        3.12  2.62          24.0        123           1.40   \n",
       "151    12.79        2.67  2.48          22.0        112           1.48   \n",
       "152    13.11        1.90  2.75          25.5        116           2.20   \n",
       "153    13.23        3.30  2.28          18.5         98           1.80   \n",
       "154    12.58        1.29  2.10          20.0        103           1.48   \n",
       "155    13.17        5.19  2.32          22.0         93           1.74   \n",
       "156    13.84        4.12  2.38          19.5         89           1.80   \n",
       "157    12.45        3.03  2.64          27.0         97           1.90   \n",
       "158    14.34        1.68  2.70          25.0         98           2.80   \n",
       "159    13.48        1.67  2.64          22.5         89           2.60   \n",
       "160    12.36        3.83  2.38          21.0         88           2.30   \n",
       "161    13.69        3.26  2.54          20.0        107           1.83   \n",
       "162    12.85        3.27  2.58          22.0        106           1.65   \n",
       "163    12.96        3.45  2.35          18.5        106           1.39   \n",
       "164    13.78        2.76  2.30          22.0         90           1.35   \n",
       "165    13.73        4.36  2.26          22.5         88           1.28   \n",
       "166    13.45        3.70  2.60          23.0        111           1.70   \n",
       "167    12.82        3.37  2.30          19.5         88           1.48   \n",
       "168    13.58        2.58  2.69          24.5        105           1.55   \n",
       "169    13.40        4.60  2.86          25.0        112           1.98   \n",
       "170    12.20        3.03  2.32          19.0         96           1.25   \n",
       "171    12.77        2.39  2.28          19.5         86           1.39   \n",
       "172    14.16        2.51  2.48          20.0         91           1.68   \n",
       "173    13.71        5.65  2.45          20.5         95           1.68   \n",
       "174    13.40        3.91  2.48          23.0        102           1.80   \n",
       "175    13.27        4.28  2.26          20.0        120           1.59   \n",
       "176    13.17        2.59  2.37          20.0        120           1.65   \n",
       "177    14.13        4.10  2.74          24.5         96           2.05   \n",
       "\n",
       "     Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity   Hue  \\\n",
       "0          3.06                  0.28             2.29         5.640000  1.04   \n",
       "1          2.76                  0.26             1.28         4.380000  1.05   \n",
       "2          3.24                  0.30             2.81         5.680000  1.03   \n",
       "3          3.49                  0.24             2.18         7.800000  0.86   \n",
       "4          2.69                  0.39             1.82         4.320000  1.04   \n",
       "5          3.39                  0.34             1.97         6.750000  1.05   \n",
       "6          2.52                  0.30             1.98         5.250000  1.02   \n",
       "7          2.51                  0.31             1.25         5.050000  1.06   \n",
       "8          2.98                  0.29             1.98         5.200000  1.08   \n",
       "9          3.15                  0.22             1.85         7.220000  1.01   \n",
       "10         3.32                  0.22             2.38         5.750000  1.25   \n",
       "11         2.43                  0.26             1.57         5.000000  1.17   \n",
       "12         2.76                  0.29             1.81         5.600000  1.15   \n",
       "13         3.69                  0.43             2.81         5.400000  1.25   \n",
       "14         3.64                  0.29             2.96         7.500000  1.20   \n",
       "15         2.91                  0.30             1.46         7.300000  1.28   \n",
       "16         3.14                  0.33             1.97         6.200000  1.07   \n",
       "17         3.40                  0.40             1.72         6.600000  1.13   \n",
       "18         3.93                  0.32             1.86         8.700000  1.23   \n",
       "19         3.03                  0.17             1.66         5.100000  0.96   \n",
       "20         3.17                  0.24             2.10         5.650000  1.09   \n",
       "21         2.41                  0.25             1.98         4.500000  1.03   \n",
       "22         2.88                  0.27             1.69         3.800000  1.11   \n",
       "23         2.37                  0.26             1.46         3.930000  1.09   \n",
       "24         2.61                  0.28             1.66         3.520000  1.12   \n",
       "25         2.68                  0.47             1.92         3.580000  1.13   \n",
       "26         2.94                  0.34             1.45         4.800000  0.92   \n",
       "27         2.19                  0.27             1.35         3.950000  1.02   \n",
       "28         2.97                  0.37             1.76         4.500000  1.25   \n",
       "29         2.33                  0.26             1.98         4.700000  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "148        0.76                  0.45             1.25         8.420000  0.55   \n",
       "149        1.39                  0.34             1.14         9.400000  0.57   \n",
       "150        1.57                  0.22             1.25         8.600000  0.59   \n",
       "151        1.36                  0.24             1.26        10.800000  0.48   \n",
       "152        1.28                  0.26             1.56         7.100000  0.61   \n",
       "153        0.83                  0.61             1.87        10.520000  0.56   \n",
       "154        0.58                  0.53             1.40         7.600000  0.58   \n",
       "155        0.63                  0.61             1.55         7.900000  0.60   \n",
       "156        0.83                  0.48             1.56         9.010000  0.57   \n",
       "157        0.58                  0.63             1.14         7.500000  0.67   \n",
       "158        1.31                  0.53             2.70        13.000000  0.57   \n",
       "159        1.10                  0.52             2.29        11.750000  0.57   \n",
       "160        0.92                  0.50             1.04         7.650000  0.56   \n",
       "161        0.56                  0.50             0.80         5.880000  0.96   \n",
       "162        0.60                  0.60             0.96         5.580000  0.87   \n",
       "163        0.70                  0.40             0.94         5.280000  0.68   \n",
       "164        0.68                  0.41             1.03         9.580000  0.70   \n",
       "165        0.47                  0.52             1.15         6.620000  0.78   \n",
       "166        0.92                  0.43             1.46        10.680000  0.85   \n",
       "167        0.66                  0.40             0.97        10.260000  0.72   \n",
       "168        0.84                  0.39             1.54         8.660000  0.74   \n",
       "169        0.96                  0.27             1.11         8.500000  0.67   \n",
       "170        0.49                  0.40             0.73         5.500000  0.66   \n",
       "171        0.51                  0.48             0.64         9.899999  0.57   \n",
       "172        0.70                  0.44             1.24         9.700000  0.62   \n",
       "173        0.61                  0.52             1.06         7.700000  0.64   \n",
       "174        0.75                  0.43             1.41         7.300000  0.70   \n",
       "175        0.69                  0.43             1.35        10.200000  0.59   \n",
       "176        0.68                  0.53             1.46         9.300000  0.60   \n",
       "177        0.76                  0.56             1.35         9.200000  0.61   \n",
       "\n",
       "     OD280  Proline  Customer_Segment  \n",
       "0     3.92     1065                 1  \n",
       "1     3.40     1050                 1  \n",
       "2     3.17     1185                 1  \n",
       "3     3.45     1480                 1  \n",
       "4     2.93      735                 1  \n",
       "5     2.85     1450                 1  \n",
       "6     3.58     1290                 1  \n",
       "7     3.58     1295                 1  \n",
       "8     2.85     1045                 1  \n",
       "9     3.55     1045                 1  \n",
       "10    3.17     1510                 1  \n",
       "11    2.82     1280                 1  \n",
       "12    2.90     1320                 1  \n",
       "13    2.73     1150                 1  \n",
       "14    3.00     1547                 1  \n",
       "15    2.88     1310                 1  \n",
       "16    2.65     1280                 1  \n",
       "17    2.57     1130                 1  \n",
       "18    2.82     1680                 1  \n",
       "19    3.36      845                 1  \n",
       "20    3.71      780                 1  \n",
       "21    3.52      770                 1  \n",
       "22    4.00     1035                 1  \n",
       "23    3.63     1015                 1  \n",
       "24    3.82      845                 1  \n",
       "25    3.20      830                 1  \n",
       "26    3.22     1195                 1  \n",
       "27    2.77     1285                 1  \n",
       "28    3.40      915                 1  \n",
       "29    3.59     1035                 1  \n",
       "..     ...      ...               ...  \n",
       "148   1.62      650                 3  \n",
       "149   1.33      550                 3  \n",
       "150   1.30      500                 3  \n",
       "151   1.47      480                 3  \n",
       "152   1.33      425                 3  \n",
       "153   1.51      675                 3  \n",
       "154   1.55      640                 3  \n",
       "155   1.48      725                 3  \n",
       "156   1.64      480                 3  \n",
       "157   1.73      880                 3  \n",
       "158   1.96      660                 3  \n",
       "159   1.78      620                 3  \n",
       "160   1.58      520                 3  \n",
       "161   1.82      680                 3  \n",
       "162   2.11      570                 3  \n",
       "163   1.75      675                 3  \n",
       "164   1.68      615                 3  \n",
       "165   1.75      520                 3  \n",
       "166   1.56      695                 3  \n",
       "167   1.75      685                 3  \n",
       "168   1.80      750                 3  \n",
       "169   1.92      630                 3  \n",
       "170   1.83      510                 3  \n",
       "171   1.63      470                 3  \n",
       "172   1.71      660                 3  \n",
       "173   1.74      740                 3  \n",
       "174   1.56      750                 3  \n",
       "175   1.56      835                 3  \n",
       "176   1.62      840                 3  \n",
       "177   1.60      560                 3  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, 0:13].values\n",
    "y = dataset.iloc[:, 13].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Dataset into the Training Set and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.87668336,  0.79842885,  0.64412971, ...,  0.0290166 ,\n",
       "        -1.06412236, -0.2059076 ],\n",
       "       [-0.36659076, -0.7581304 , -0.39779858, ...,  0.0290166 ,\n",
       "        -0.73083231, -0.81704676],\n",
       "       [-1.69689407, -0.34424759, -0.32337513, ...,  0.90197362,\n",
       "         0.51900537, -1.31256499],\n",
       "       ..., \n",
       "       [-0.70227477, -0.68615078, -0.65828065, ...,  0.46549511,\n",
       "         0.51900537, -1.31256499],\n",
       "       [ 1.13777093, -0.62316862, -0.91876272, ..., -0.18922266,\n",
       "         1.03282752,  0.80164614],\n",
       "       [ 1.4610222 ,  0.12361993,  0.42085937, ..., -1.45501034,\n",
       "        -1.2168803 , -0.2719767 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sc.fit_transform(X_train)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.38847070e-01,  -6.32166068e-01,  -4.35010303e-01,\n",
       "         -9.19695615e-01,   1.26324041e+00,   5.59998633e-01,\n",
       "          9.77754158e-01,  -1.20637533e+00,   2.36680192e-02,\n",
       "          3.39284695e-01,  -1.45574805e-01,   8.52295413e-01,\n",
       "          1.04940526e+00],\n",
       "       [ -2.42263344e-01,   2.67579163e-01,   4.20859365e-01,\n",
       "          7.12764102e-01,   8.40672358e-01,  -1.27747161e+00,\n",
       "         -6.05828120e-01,  -9.70634096e-01,  -5.87397203e-01,\n",
       "          2.42611713e+00,  -2.06608025e+00,  -1.55017035e+00,\n",
       "         -8.66598582e-01],\n",
       "       [ -7.64438475e-01,  -1.11802849e+00,  -7.69915825e-01,\n",
       "         -1.61767889e-01,  -9.20027861e-01,   2.03653722e+00,\n",
       "          1.18341419e+00,  -1.36353615e+00,   4.48018868e-01,\n",
       "         -2.50930538e-01,   1.16386073e+00,   3.94021597e-01,\n",
       "         -1.06480588e+00],\n",
       "       [  7.15057728e-01,  -5.78181354e-01,   3.46435916e-01,\n",
       "          2.75498106e-01,   1.12238439e+00,   1.15061407e+00,\n",
       "          8.54358136e-01,  -1.28495574e+00,   1.43251284e+00,\n",
       "          5.07917619e-01,   1.16312302e-01,   6.16214963e-01,\n",
       "          1.24761256e+00],\n",
       "       [  8.09879269e-02,   3.08378173e+00,  -8.81550999e-01,\n",
       "          5.67008770e-01,  -9.90455870e-01,   5.92810602e-01,\n",
       "          7.20679112e-01,  -4.99151627e-01,   6.85655343e-01,\n",
       "         -1.03085781e+00,  -9.74883977e-01,   7.13424560e-01,\n",
       "         -1.19694407e+00],\n",
       "       [ -1.77149051e+00,  -2.90262880e-01,   3.21173872e+00,\n",
       "          2.60758342e+00,   1.33366842e+00,   1.51154572e+00,\n",
       "          3.21944855e+00,   8.36715367e-01,   4.48018868e-01,\n",
       "          4.02522042e-01,  -1.01926954e-01,   1.53276259e+00,\n",
       "         -9.16150406e-01],\n",
       "       [  1.33669479e+00,  -2.00288357e-01,   9.04611787e-01,\n",
       "         -5.69882819e-01,   1.47452444e+00,   5.59998633e-01,\n",
       "          5.76717087e-01,  -4.20571216e-01,  -6.04371237e-01,\n",
       "          2.01884783e-03,   4.65495111e-01,   1.38000466e+00,\n",
       "          1.82571717e+00],\n",
       "       [ -7.76871216e-01,   1.31128363e+00,   4.87421183e-02,\n",
       "          4.21253438e-01,  -8.49599852e-01,   6.78191041e-02,\n",
       "         -1.05828020e+00,   1.07245660e+00,  -9.60825950e-01,\n",
       "          1.09813285e+00,  -1.71689745e+00,  -1.39741241e+00,\n",
       "         -7.34460386e-01],\n",
       "       [ -9.13631369e-01,  -5.78181354e-01,  -9.18762724e-01,\n",
       "         -1.61767889e-01,  -1.41302392e+00,  -9.98569875e-01,\n",
       "          8.31330005e-02,   5.09112525e-02,   4.06420531e-02,\n",
       "         -6.93591962e-01,   2.03608004e-01,   8.10634157e-01,\n",
       "         -7.67494935e-01],\n",
       "       [ -1.12498797e+00,  -4.88206831e-01,  -1.74528230e-01,\n",
       "         -3.07523221e-01,  -1.34259591e+00,  -1.08059980e+00,\n",
       "         -4.61866095e-01,   1.22961742e+00,   5.76160871e-02,\n",
       "         -1.11517427e+00,   5.52790813e-01,  -4.39203523e-01,\n",
       "         -8.66598582e-01],\n",
       "       [  4.66402904e-01,   1.68607187e-01,  -6.28930558e-02,\n",
       "          1.29742774e-01,  -7.79171843e-01,  -1.40871948e+00,\n",
       "         -1.49016628e+00,   5.09112525e-02,  -1.63978731e+00,\n",
       "          2.33889118e-01,  -1.10582753e+00,  -1.61461816e-01,\n",
       "          1.24437889e-01],\n",
       "       [  1.11290545e+00,   2.39997536e+00,  -5.09433752e-01,\n",
       "          1.29742774e-01,  -1.41302392e+00,  -2.09777082e+00,\n",
       "         -1.65469430e+00,   2.86652487e-01,  -1.57189117e+00,\n",
       "         -6.12184986e-02,  -1.62960174e+00,  -1.74458954e+00,\n",
       "         -1.08132315e+00],\n",
       "       [  1.51075317e+00,  -5.51188997e-01,   3.09224191e-01,\n",
       "         -1.26950841e+00,   8.40672358e-01,   1.65919958e+00,\n",
       "          1.48162125e+00,  -1.84829982e-01,   6.17759208e-01,\n",
       "          7.18708773e-01,   4.21847260e-01,   3.66247427e-01,\n",
       "          2.33775268e+00],\n",
       "       [ -7.64438475e-01,  -1.17201321e+00,  -9.93186174e-01,\n",
       "         -3.07523221e-01,  -8.49599852e-01,   2.06934919e+00,\n",
       "          1.85180931e+00,  -9.70634096e-01,   5.83811140e-01,\n",
       "         -2.29851422e-01,   3.78199409e-01,   2.55150744e-01,\n",
       "         -2.71976699e-01],\n",
       "       [  7.39923211e-01,   1.86602092e-01,   1.20230558e+00,\n",
       "          1.44154076e+00,   3.47676297e-01,  -1.16262972e+00,\n",
       "         -1.14054421e+00,   2.08072075e-01,  -1.12124252e-01,\n",
       "          1.52393098e+00,  -9.31236125e-01,  -1.09189653e+00,\n",
       "          2.53342422e-02],\n",
       "       [ -1.22444990e+00,  -1.30697499e+00,  -1.36530342e+00,\n",
       "         -1.61767889e-01,  -9.90455870e-01,   2.64690916e-01,\n",
       "          3.19642042e-01,  -4.99151627e-01,  -2.98838626e-01,\n",
       "         -1.07301604e+00,   1.86222635e+00,   7.41198731e-01,\n",
       "         -1.53389647e+00],\n",
       "       [  9.63712552e-01,  -7.58130400e-01,   1.23951731e+00,\n",
       "         -1.60125576e-02,   2.24923253e+00,   1.13420808e+00,\n",
       "          8.13226129e-01,   1.07245660e+00,  -4.34630898e-01,\n",
       "          1.49572656e-01,   1.29480428e+00,   5.74553707e-01,\n",
       "          1.62750987e+00],\n",
       "       [  1.49832042e+00,  -7.04145686e-01,   4.20859365e-01,\n",
       "         -8.90544549e-01,   5.58960323e-01,   1.70841753e+00,\n",
       "          2.03690334e+00,  -3.41990805e-01,   4.31044834e-01,\n",
       "          1.54079428e+00,   1.20750858e+00,   3.24586171e-01,\n",
       "          3.09754730e+00],\n",
       "       [ -4.28754462e-01,  -9.11087089e-01,  -1.29087997e+00,\n",
       "         -8.03091350e-01,  -4.46374703e-03,  -3.91548456e-01,\n",
       "         -5.54413111e-01,   1.30819784e+00,  -1.67373538e+00,\n",
       "          2.97126464e-01,   1.16312302e-01,  -1.38352533e+00,\n",
       "         -9.65702229e-01],\n",
       "       [  1.05074174e+00,  -6.50160973e-01,   8.67400062e-01,\n",
       "         -6.86487084e-01,  -4.27031800e-01,   3.13908869e-01,\n",
       "          1.07030117e+00,  -1.12779492e+00,   1.16092829e+00,\n",
       "          2.33889118e-01,   1.25115643e+00,   1.08837586e+00,\n",
       "          1.72661352e+00],\n",
       "       [  1.55584374e-01,  -1.22599792e+00,  -2.48165516e+00,\n",
       "         -1.32781054e+00,  -1.55387994e+00,   1.18342604e+00,\n",
       "          1.26567821e+00,  -8.13473273e-01,   1.14395426e+00,\n",
       "          1.07414425e-01,   7.27382218e-01,   8.24521243e-01,\n",
       "         -7.93922574e-01],\n",
       "       [  8.09879269e-02,  -6.50160973e-01,   6.81341438e-01,\n",
       "         -4.53278553e-01,  -1.45319765e-01,   3.13908869e-01,\n",
       "          4.94453073e-01,  -5.77732039e-01,  -2.81864592e-01,\n",
       "         -3.35247000e-01,   7.27382218e-01,  -1.05913475e-01,\n",
       "          1.19806073e+00],\n",
       "       [  3.17210010e-01,   1.95599544e-01,   1.87211663e+00,\n",
       "          4.21253438e-01,   1.26324041e+00,   8.88118319e-01,\n",
       "          7.61811120e-01,   2.08072075e-01,   3.63148698e-01,\n",
       "         -3.05736238e-01,   3.78199409e-01,   4.77344109e-01,\n",
       "         -2.42175814e-02],\n",
       "       [ -5.90380098e-01,  -5.78181354e-01,  -1.43972687e+00,\n",
       "          2.75498106e-01,  -1.06088388e+00,  -9.62407389e-02,\n",
       "         -2.99800193e-02,  -3.41990805e-01,  -2.13968456e-01,\n",
       "         -8.87519825e-01,   3.78199409e-01,   1.36611757e+00,\n",
       "         -2.32335240e-01],\n",
       "       [ -8.14169440e-01,  -1.14502085e+00,  -3.23375129e-01,\n",
       "         -1.03629988e+00,   6.59642617e-02,  -3.42330503e-01,\n",
       "         -8.83469169e-01,   2.09400195e+00,  -2.03019009e+00,\n",
       "         -7.48397663e-01,   1.29480428e+00,  -1.27242864e+00,\n",
       "         -2.05907601e-01],\n",
       "       [ -5.77947357e-01,   5.16403072e-02,  -7.32704101e-01,\n",
       "          4.21253438e-01,  -8.49599852e-01,   4.77968712e-01,\n",
       "          3.29925044e-01,  -8.13473273e-01,  -6.55293339e-01,\n",
       "         -1.28380720e+00,  -2.32870507e-01,   2.69037829e-01,\n",
       "         -1.37863409e+00],\n",
       "       [ -6.89842028e-01,  -7.58130400e-01,  -2.86163404e-01,\n",
       "          5.67008770e-01,  -9.90455870e-01,   7.89682413e-01,\n",
       "          1.23482920e+00,   2.08072075e-01,   2.78278529e-01,\n",
       "         -4.65937515e-01,  -1.14947538e+00,   3.52360341e-01,\n",
       "         -1.28944081e+00],\n",
       "       [ -1.02552604e+00,  -6.86150782e-01,  -2.11739955e-01,\n",
       "          9.45972633e-01,  -7.08743835e-01,  -7.85292079e-01,\n",
       "         -2.66489061e-01,   5.22393721e-01,  -7.81761846e-02,\n",
       "         -1.09409516e+00,   1.64398709e+00,  -4.53090608e-01,\n",
       "         -8.17046759e-01],\n",
       "       [ -1.52283569e+00,   2.76576615e-01,   2.05817525e+00,\n",
       "          1.29742774e-01,   2.06820279e-01,  -8.34510032e-01,\n",
       "          8.31330005e-02,   1.85826072e+00,  -9.43851916e-01,\n",
       "         -5.24959039e-01,   1.20750858e+00,  -1.19800560e-01,\n",
       "         -4.47059809e-01],\n",
       "       [  9.88578035e-01,   3.48556233e-01,  -2.48951679e-01,\n",
       "          7.12764102e-01,  -7.08743835e-01,  -1.49074940e+00,\n",
       "         -1.30507224e+00,   3.65232898e-01,  -9.77799984e-01,\n",
       "          1.91178671e+00,  -1.10582753e+00,  -1.25854156e+00,\n",
       "         -4.20632170e-01],\n",
       "       [  5.03701128e-01,  -5.42191545e-01,   9.41823511e-01,\n",
       "         -1.00714881e+00,  -4.97459808e-01,   9.70148241e-01,\n",
       "          1.01888616e+00,  -1.84829982e-01,  -2.64890558e-01,\n",
       "         -1.03376729e-01,  -1.45574805e-01,   8.80069584e-01,\n",
       "          1.49537167e+00],\n",
       "       [  1.53561865e+00,  -6.05173711e-01,  -2.48951679e-01,\n",
       "         -9.48846682e-01,   1.26324041e+00,   1.54435769e+00,\n",
       "          1.08058418e+00,  -8.13473273e-01,   7.19603411e-01,\n",
       "          5.62723319e-01,  -5.82791026e-02,   1.00505335e+00,\n",
       "          7.52094321e-01],\n",
       "       [ -1.17471893e+00,   1.73416389e+00,   4.87421183e-02,\n",
       "          7.12764102e-01,  -1.41302392e+00,  -2.60300582e-01,\n",
       "         -2.04791050e-01,   4.43813310e-01,  -4.34630898e-01,\n",
       "         -1.03085781e+00,  -7.12996870e-01,  -2.25909628e-02,\n",
       "         -5.36253092e-01],\n",
       "       [  1.52318591e+00,   1.50023013e+00,   2.72012467e-01,\n",
       "         -1.90918956e-01,   7.70244349e-01,   9.70148241e-01,\n",
       "          7.20679112e-01,  -4.99151627e-01,  -6.04371237e-01,\n",
       "          8.21194866e-02,  -3.63814061e-01,   1.03282752e+00,\n",
       "          1.11547436e+00],\n",
       "       [  2.29401586e+00,  -6.59158425e-01,  -7.32704101e-01,\n",
       "         -1.61932121e+00,  -2.15747773e-01,   8.88118319e-01,\n",
       "          1.06001817e+00,  -5.77732039e-01,   6.34733242e-01,\n",
       "          6.52561942e-02,   5.52790813e-01,   3.66247427e-01,\n",
       "          9.99853439e-01],\n",
       "       [  8.09879269e-02,  -5.42191545e-01,  -9.93186174e-01,\n",
       "         -7.44789217e-01,   4.88532314e-01,   1.21623801e+00,\n",
       "          1.08058418e+00,  -6.56312450e-01,   7.19603411e-01,\n",
       "         -2.19697526e-03,  -3.20166209e-01,   1.06060169e+00,\n",
       "          4.71300654e-01]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = sc.transform(X_test)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lda = LDA(n_components = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.57315552,  1.94018924],\n",
       "       [ 0.85475898, -2.08182977],\n",
       "       [ 0.62173655, -3.06234453],\n",
       "       [ 4.80786412,  2.00638739],\n",
       "       [-3.8579759 ,  0.14987256],\n",
       "       [-3.59455458,  1.24961706],\n",
       "       [-0.53772906, -3.0852704 ],\n",
       "       [ 0.04058577, -2.47312446],\n",
       "       [ 0.99835348, -3.36989668],\n",
       "       [-3.74095658,  1.94844242],\n",
       "       [ 3.76035226,  0.82126218],\n",
       "       [-0.15106412, -1.86820292],\n",
       "       [ 3.62762899,  2.05460026],\n",
       "       [-3.94229781,  2.80328429],\n",
       "       [ 3.33429017,  0.73627798],\n",
       "       [ 3.90206871,  1.03276135],\n",
       "       [-3.55835472,  0.18783108],\n",
       "       [ 5.63175281,  2.40524214],\n",
       "       [-5.56217254,  0.85694946],\n",
       "       [ 0.23296188, -3.94615581],\n",
       "       [ 5.03141997,  3.23313754],\n",
       "       [ 3.52861651,  0.94605778],\n",
       "       [-1.17815662, -2.17294825],\n",
       "       [ 3.58320131,  0.67947364],\n",
       "       [ 5.21649905,  2.41090952],\n",
       "       [-3.01647841,  1.24411621],\n",
       "       [ 1.86178658, -0.47484926],\n",
       "       [ 3.93816398, -0.2204059 ],\n",
       "       [-1.0836235 , -3.32496762],\n",
       "       [ 1.8691488 , -0.63362283],\n",
       "       [ 3.27717205,  1.51263542],\n",
       "       [-0.47842302, -1.16766723],\n",
       "       [-4.14433134,  1.37391708],\n",
       "       [ 2.45009727, -2.49336285],\n",
       "       [-1.20844631, -2.30679956],\n",
       "       [ 2.55631466, -0.98550214],\n",
       "       [-1.6091476 ,  0.55066705],\n",
       "       [-5.52462148,  2.19178828],\n",
       "       [-2.44685583, -2.28937848],\n",
       "       [-1.95474568, -2.02963924],\n",
       "       [ 5.54394234,  1.5236766 ],\n",
       "       [ 5.74409562,  1.85156779],\n",
       "       [ 1.13553056, -3.93865462],\n",
       "       [-1.2483554 , -3.08106324],\n",
       "       [-0.00961488, -3.62708415],\n",
       "       [ 5.21418108,  2.66981962],\n",
       "       [ 4.2290474 ,  0.3886969 ],\n",
       "       [-3.94237521,  0.76214343],\n",
       "       [ 5.30822458,  2.18894363],\n",
       "       [-0.20862902, -3.05785486],\n",
       "       [ 0.47295413, -2.560251  ],\n",
       "       [ 0.46692465, -1.86886738],\n",
       "       [-1.05818513, -2.61576658],\n",
       "       [ 0.33551985, -3.26643922],\n",
       "       [-4.74777848,  2.23081211],\n",
       "       [-2.80968166,  1.32816126],\n",
       "       [-1.02804047, -2.60107642],\n",
       "       [-6.15432728,  2.12945198],\n",
       "       [ 4.33944259,  1.23494233],\n",
       "       [-3.63172128,  0.54074799],\n",
       "       [ 4.79575236,  1.25996976],\n",
       "       [-4.13914056,  0.36020476],\n",
       "       [-3.94468876,  3.0153646 ],\n",
       "       [-0.42472714, -2.30200526],\n",
       "       [-5.17777666,  2.36899585],\n",
       "       [-1.3044572 , -2.87041347],\n",
       "       [ 0.06991014, -2.78082083],\n",
       "       [-4.98245326,  3.83183665],\n",
       "       [ 4.3064623 ,  2.4129711 ],\n",
       "       [ 0.29208614, -2.06937039],\n",
       "       [-5.0934408 ,  3.0899463 ],\n",
       "       [ 0.61101399,  0.17866792],\n",
       "       [ 1.12081287, -2.87291369],\n",
       "       [-0.77339273, -2.03777551],\n",
       "       [ 2.57603424,  0.08537633],\n",
       "       [-4.29104423,  2.28661799],\n",
       "       [ 5.255722  ,  0.61832811],\n",
       "       [ 5.7318737 ,  0.19267558],\n",
       "       [-6.24884704,  3.55128212],\n",
       "       [-5.38587866,  1.77559442],\n",
       "       [ 1.31025756, -2.52845345],\n",
       "       [ 5.00526015,  2.31115606],\n",
       "       [-4.31499585,  1.99154644],\n",
       "       [-3.92894005,  2.13127776],\n",
       "       [-0.05838671, -2.60549973],\n",
       "       [-0.42566332, -1.68426706],\n",
       "       [-3.5253992 ,  0.16028802],\n",
       "       [-4.42913323,  2.47869828],\n",
       "       [-2.85063973,  1.1163819 ],\n",
       "       [ 4.46460374,  3.55045935],\n",
       "       [-1.21751159, -1.31441447],\n",
       "       [-4.94290391,  1.29321013],\n",
       "       [-1.70880192, -2.10638157],\n",
       "       [ 3.42314438,  1.21989584],\n",
       "       [-5.58393165,  2.49598525],\n",
       "       [-0.71859407, -2.67293812],\n",
       "       [ 4.01611408,  0.13560479],\n",
       "       [ 4.64009352, -0.06178578],\n",
       "       [-3.85066489,  1.66509736],\n",
       "       [-3.47123425, -0.07349273],\n",
       "       [ 4.17391188,  0.51767626],\n",
       "       [-4.09941682,  3.11353194],\n",
       "       [ 4.29592104,  0.18486038],\n",
       "       [-0.15570245, -3.78905263],\n",
       "       [-3.29789207,  1.48134709],\n",
       "       [-4.0655232 ,  1.02548943],\n",
       "       [-0.63468603, -2.68007872],\n",
       "       [-4.4641561 ,  2.38128912],\n",
       "       [ 4.52246935,  1.96313747],\n",
       "       [ 0.79712615, -3.04743012],\n",
       "       [ 4.91056287,  1.22473167],\n",
       "       [-3.77150311,  0.30882908],\n",
       "       [ 3.5444653 ,  1.05962644],\n",
       "       [ 4.36398757,  1.78816415],\n",
       "       [ 4.86008035,  2.32823762],\n",
       "       [-3.46481941,  1.07890379],\n",
       "       [-1.65664035, -3.72251678],\n",
       "       [ 0.26593072, -2.87995582],\n",
       "       [-0.66345544, -2.36083579],\n",
       "       [-0.33965319, -5.76922451],\n",
       "       [ 5.14221842,  1.93740967],\n",
       "       [ 5.75746454,  2.94776345],\n",
       "       [ 0.42253477, -1.5887479 ],\n",
       "       [ 0.96267148, -1.57482982],\n",
       "       [-2.5640305 ,  0.20647539],\n",
       "       [-2.30398129, -1.49865287],\n",
       "       [ 4.91741587,  2.32620455],\n",
       "       [ 5.20879938,  2.96660159],\n",
       "       [-3.60895143,  2.468315  ],\n",
       "       [-4.58647662,  2.42583146],\n",
       "       [ 2.89493473,  1.15627605],\n",
       "       [-0.09551518, -1.90444969],\n",
       "       [ 1.30043304, -0.87538895],\n",
       "       [ 0.16805348, -3.21257614],\n",
       "       [-3.19016989,  1.66680508],\n",
       "       [-2.08858151,  0.83647089],\n",
       "       [-4.25960623,  1.54637835],\n",
       "       [ 1.68647094, -3.83427605],\n",
       "       [-0.9020576 , -2.62989337],\n",
       "       [-0.19105618, -3.66017053],\n",
       "       [-4.20632725,  0.8310719 ],\n",
       "       [ 4.52910794,  3.07839306]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = lda.fit_transform(X_train, y_train)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.83960313,  1.54567265],\n",
       "       [ 5.24601727,  1.72358501],\n",
       "       [-0.66064228, -3.32231072],\n",
       "       [-2.16667061,  1.0147427 ],\n",
       "       [-0.16079996, -1.27171643],\n",
       "       [-5.31374258, -2.22014536],\n",
       "       [-4.78386714,  3.11508054],\n",
       "       [ 5.55863966,  0.88556084],\n",
       "       [-1.29772139, -2.40714074],\n",
       "       [ 0.53068371, -2.37759837],\n",
       "       [ 2.36900593,  1.81859204],\n",
       "       [ 5.14502202,  1.88061526],\n",
       "       [-5.27947032,  3.32510044],\n",
       "       [-2.70452577, -2.61522323],\n",
       "       [ 4.32314426,  2.14569837],\n",
       "       [-0.65835601, -4.67751669],\n",
       "       [-4.07332713,  1.95266123],\n",
       "       [-6.55291385,  4.01098911],\n",
       "       [ 2.40020179, -1.14618328],\n",
       "       [-5.14219986,  2.02120623],\n",
       "       [-2.46475469, -2.81225673],\n",
       "       [-2.5190649 ,  1.07965428],\n",
       "       [-1.79798575,  0.47987658],\n",
       "       [-1.43423986, -2.41298601],\n",
       "       [ 1.11433225, -0.98310413],\n",
       "       [ 0.28604133, -2.81262488],\n",
       "       [-1.21198193, -2.73115246],\n",
       "       [ 0.84900431, -3.34996047],\n",
       "       [-1.03105294, -0.53029203],\n",
       "       [ 4.83489722,  2.08108766],\n",
       "       [-4.75464421,  2.30157612],\n",
       "       [-3.33786142,  1.51860576],\n",
       "       [ 1.02045007, -1.04261267],\n",
       "       [-3.1347464 ,  2.97819832],\n",
       "       [-4.25385909,  1.84683986],\n",
       "       [-3.16058359, -0.39860037]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = lda.transform(X_test)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Logistic Regression to the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)\n",
    "classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2, 1, 2, 2, 1, 3, 2, 2, 3, 3, 1, 2, 3, 2, 1, 1, 2, 1, 2, 1, 1,\n",
       "       2, 2, 2, 2, 2, 2, 3, 1, 1, 2, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  0,  0],\n",
       "       [ 0, 16,  0],\n",
       "       [ 0,  0,  6]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Training Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9SU08zphBNcXHqvSZlJYqHabWqTnlLuE4im8FeZ8apIBQUE8/f7UuoSRnQaWzbsmSeN1J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\nQgFBREQABQQREQlZXrNzopj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22db9022550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
    "plt.title('Logistic Regression (Training Set)')\n",
    "plt.xlabel('LD1')\n",
    "plt.ylabel('LD2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fuEcSwAzgbkmHR8Qfq5SzCFgEMHn/yW5eMjNrk46vZRQRvwb2Kj2W9AAwJyIe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\ngpmZAU4IZmaWckIwMzPACcHMzFL/HzKrl1NcPsDrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22dbec278d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('LD1')\n",
    "plt.ylabel('LD2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}