PCA Wine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import Library\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <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>173</th>\n",
       "      <td>3</td>\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>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\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>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\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>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\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>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\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>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0       1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1       1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2       1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3       1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4       1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..    ...      ...    ...   ...         ...        ...      ...         ...   \n",
       "173     3    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174     3    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175     3    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176     3    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177     3    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \n",
       "0             0.28             2.29   5.64  1.04      3.92     1065  \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050  \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185  \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480  \n",
       "4             0.39             1.82   4.32  1.04      2.93      735  \n",
       "..             ...              ...    ...   ...       ...      ...  \n",
       "173           0.52             1.06   7.70  0.64      1.74      740  \n",
       "174           0.43             1.41   7.30  0.70      1.56      750  \n",
       "175           0.43             1.35  10.20  0.59      1.56      835  \n",
       "176           0.53             1.46   9.30  0.60      1.62      840  \n",
       "177           0.56             1.35   9.20  0.61      1.60      560  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read CSV File\n",
    "wine = pd.read_csv(\"~/Downloads/Data Science/data set/wine.csv\")\n",
    "wine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.21394365,  1.51861254, -0.5622498 , ...,  0.36217728,\n",
       "         1.84791957,  1.01300893],\n",
       "       [-1.21394365,  0.24628963, -0.49941338, ...,  0.40605066,\n",
       "         1.1134493 ,  0.96524152],\n",
       "       [-1.21394365,  0.19687903,  0.02123125, ...,  0.31830389,\n",
       "         0.78858745,  1.39514818],\n",
       "       ...,\n",
       "       [ 1.37386437,  0.33275817,  1.74474449, ..., -1.61212515,\n",
       "        -1.48544548,  0.28057537],\n",
       "       [ 1.37386437,  0.20923168,  0.22769377, ..., -1.56825176,\n",
       "        -1.40069891,  0.29649784],\n",
       "       [ 1.37386437,  1.39508604,  1.58316512, ..., -1.52437837,\n",
       "        -1.42894777, -0.59516041]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing the numerical data \n",
    "wine_norm = scale(wine)\n",
    "wine_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#implement PCA And Then Fit\n",
    "pca = PCA(n_components = 6)\n",
    "pca_values = pca.fit_transform(wine_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.39366953, -0.13632501,  0.22267638, -0.00225793,  0.22429849,\n",
       "       -0.12463016, -0.35926404, -0.39071171,  0.2670012 , -0.2790625 ,\n",
       "        0.08931829, -0.27682265, -0.35052618, -0.26951525])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The amount of variance that each PCA explains is \n",
    "var = pca.explained_variance_ratio_\n",
    "var\n",
    "pca.components_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39.54, 57.38, 67.71, 74.34, 80.61, 85.42])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cumulative variance \n",
    "var1 = np.cumsum(np.round(var,decimals = 4)*100)\n",
    "var1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1278a2390>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variance plot for PCA components obtained \n",
    "plt.plot(var1,color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot between PCA1 and PCA2 \n",
    "x = pca_values[:,0]\n",
    "y = pca_values[:,1]\n",
    "z = pca_values[:2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "Axes3D.scatter(np.array(x),np.array(y),np.array(z),c=[\"green\",\"blue\",\"red\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 2, 3, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 3, 3, 3,\n",
       "       1, 1, 3, 3, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 3, 3, 1, 1, 3,\n",
       "       1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 0, 3, 0, 3, 0, 0, 3,\n",
       "       0, 0, 3, 3, 1, 0, 0, 1, 1, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0,\n",
       "       3, 3, 0, 0, 0, 0, 0, 1, 3, 0, 3, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3,\n",
       "       0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0,\n",
       "       0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 3, 0, 3, 3, 0, 0, 0, 0, 3,\n",
       "       3, 3, 0, 1, 3, 3, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3,\n",
       "       3, 0], dtype=int32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import KMeans\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#Give 4 cluster And then fit\n",
    "kmeans = KMeans(n_clusters = 4)\n",
    "kmeans.fit(wine)\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot KMeans cluster\n",
    "plt.scatter(np.array(x),np.array(y),c=kmeans,color=[\"red\",\"blue\"])"
   ]
  }
 ],
 "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": "3.7.7"
  }
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