Assignment PCA

Assignment PCA.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd \nimport numpy as np\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import scale ",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "wine = pd.read_csv(\"wine.csv\")\nwine.describe()\nwine.head()",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "   Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n0     1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n1     1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n2     1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n3     1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n4     1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n\n   Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \n0           0.28             2.29   5.64  1.04      3.92     1065  \n1           0.26             1.28   4.38  1.05      3.40     1050  \n2           0.30             2.81   5.68  1.03      3.17     1185  \n3           0.24             2.18   7.80  0.86      3.45     1480  \n4           0.39             1.82   4.32  1.04      2.93      735  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "wine.data = wine.iloc[:,1:]\nwine.data.head()\n#nummpy array\nWINE= wine.data.values\nWINE",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<ipython-input-3-823564df317b>:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n  wine.data = wine.iloc[:,1:]\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n        1.065e+03],\n       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n        1.050e+03],\n       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n        1.185e+03],\n       ...,\n       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n        8.350e+02],\n       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n        8.400e+02],\n       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n        5.600e+02]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "wine_normal = scale(WINE)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "wine_normal",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "array([[ 1.51861254, -0.5622498 ,  0.23205254, ...,  0.36217728,\n         1.84791957,  1.01300893],\n       [ 0.24628963, -0.49941338, -0.82799632, ...,  0.40605066,\n         1.1134493 ,  0.96524152],\n       [ 0.19687903,  0.02123125,  1.10933436, ...,  0.31830389,\n         0.78858745,  1.39514818],\n       ...,\n       [ 0.33275817,  1.74474449, -0.38935541, ..., -1.61212515,\n        -1.48544548,  0.28057537],\n       [ 0.20923168,  0.22769377,  0.01273209, ..., -1.56825176,\n        -1.40069891,  0.29649784],\n       [ 1.39508604,  1.58316512,  1.36520822, ..., -1.52437837,\n        -1.42894777, -0.59516041]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pca = PCA(n_components = 6)\npca_values = pca.fit_transform(wine_normal)",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "var = pca.explained_variance_ratio_\nvar",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "array([0.36198848, 0.1920749 , 0.11123631, 0.0706903 , 0.06563294,\n       0.04935823])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "var1 = np.cumsum(np.round(var,decimals = 4)*100)\nvar1",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "array([36.2 , 55.41, 66.53, 73.6 , 80.16, 85.1 ])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pca.components_",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "array([[ 0.1443294 , -0.24518758, -0.00205106, -0.23932041,  0.14199204,\n         0.39466085,  0.4229343 , -0.2985331 ,  0.31342949, -0.0886167 ,\n         0.29671456,  0.37616741,  0.28675223],\n       [-0.48365155, -0.22493093, -0.31606881,  0.0105905 , -0.299634  ,\n        -0.06503951,  0.00335981, -0.02877949, -0.03930172, -0.52999567,\n         0.27923515,  0.16449619, -0.36490283],\n       [-0.20738262,  0.08901289,  0.6262239 ,  0.61208035,  0.13075693,\n         0.14617896,  0.1506819 ,  0.17036816,  0.14945431, -0.13730621,\n         0.08522192,  0.16600459, -0.12674592],\n       [-0.0178563 ,  0.53689028, -0.21417556,  0.06085941, -0.35179658,\n         0.19806835,  0.15229479, -0.20330102,  0.39905653,  0.06592568,\n        -0.42777141,  0.18412074, -0.23207086],\n       [-0.26566365,  0.03521363, -0.14302547,  0.06610294,  0.72704851,\n        -0.14931841, -0.10902584, -0.50070298,  0.13685982, -0.07643678,\n        -0.17361452, -0.10116099, -0.1578688 ],\n       [-0.21353865, -0.53681385, -0.15447466,  0.10082451, -0.03814394,\n         0.0841223 ,  0.01892002,  0.25859401,  0.53379539,  0.41864414,\n        -0.10598274, -0.26585107, -0.11972557]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(var1,color=\"red\")",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x256a1673a60>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": " pca_values[:,0:1]",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "array([[ 3.31675081],\n       [ 2.20946492],\n       [ 2.51674015],\n       [ 3.75706561],\n       [ 1.00890849],\n       [ 3.05025392],\n       [ 2.44908967],\n       [ 2.05943687],\n       [ 2.5108743 ],\n       [ 2.75362819],\n       [ 3.47973668],\n       [ 1.7547529 ],\n       [ 2.11346234],\n       [ 3.45815682],\n       [ 4.31278391],\n       [ 2.3051882 ],\n       [ 2.17195527],\n       [ 1.89897118],\n       [ 3.54198508],\n       [ 2.0845222 ],\n       [ 3.12440254],\n       [ 1.08657007],\n       [ 2.53522408],\n       [ 1.64498834],\n       [ 1.76157587],\n       [ 0.9900791 ],\n       [ 1.77527763],\n       [ 1.23542396],\n       [ 2.18840633],\n       [ 2.25610898],\n       [ 2.50022003],\n       [ 2.67741105],\n       [ 1.62857912],\n       [ 1.90269086],\n       [ 1.41038853],\n       [ 1.90382623],\n       [ 1.38486223],\n       [ 1.12220741],\n       [ 1.5021945 ],\n       [ 2.52980109],\n       [ 2.58809543],\n       [ 0.66848199],\n       [ 3.07080699],\n       [ 0.46220914],\n       [ 2.10135193],\n       [ 1.13616618],\n       [ 2.72660096],\n       [ 2.82133927],\n       [ 2.00985085],\n       [ 2.7074913 ],\n       [ 3.21491747],\n       [ 2.85895983],\n       [ 3.50560436],\n       [ 2.22479138],\n       [ 2.14698782],\n       [ 2.46932948],\n       [ 2.74151791],\n       [ 2.17374092],\n       [ 3.13938015],\n       [-0.92858197],\n       [-1.54248014],\n       [-1.83624976],\n       [ 0.03060683],\n       [ 2.05026161],\n       [-0.60968083],\n       [ 0.90022784],\n       [ 2.24850719],\n       [ 0.18338403],\n       [-0.81280503],\n       [ 1.9756205 ],\n       [-1.57221622],\n       [ 1.65768181],\n       [-0.72537239],\n       [ 2.56222717],\n       [ 1.83256757],\n       [-0.8679929 ],\n       [ 0.3700144 ],\n       [-1.45737704],\n       [ 1.26293085],\n       [ 0.37615037],\n       [ 0.7620639 ],\n       [ 1.03457797],\n       [-0.49487676],\n       [-2.53897708],\n       [ 0.83532015],\n       [ 0.78790461],\n       [-0.80683216],\n       [-0.55804262],\n       [-1.11511104],\n       [-0.55572283],\n       [-1.34928528],\n       [-1.56448261],\n       [-1.93255561],\n       [ 0.74666594],\n       [ 0.95745536],\n       [ 2.54386518],\n       [-0.54395259],\n       [ 1.03104975],\n       [ 2.25190942],\n       [ 1.41021602],\n       [ 0.79771979],\n       [-0.54953173],\n       [-0.16117374],\n       [-0.65979494],\n       [ 0.39235441],\n       [-1.77249908],\n       [-0.36626736],\n       [-1.62067257],\n       [ 0.08253578],\n       [ 1.57827507],\n       [ 1.42056925],\n       [-0.27870275],\n       [-1.30314497],\n       [-0.45707187],\n       [-0.49418585],\n       [ 0.48207441],\n       [-0.25288888],\n       [-0.10722764],\n       [-2.4330126 ],\n       [-0.55108954],\n       [ 0.73962193],\n       [ 1.33632173],\n       [-1.177087  ],\n       [-0.46233501],\n       [ 0.97847408],\n       [-0.09680973],\n       [ 0.03848715],\n       [-1.5971585 ],\n       [-0.47956492],\n       [-1.79283347],\n       [-1.32710166],\n       [-2.38450083],\n       [-2.9369401 ],\n       [-2.14681113],\n       [-2.36986949],\n       [-3.06384157],\n       [-3.91575378],\n       [-3.93646339],\n       [-3.09427612],\n       [-2.37447163],\n       [-2.77881295],\n       [-2.28656128],\n       [-2.98563349],\n       [-2.3751947 ],\n       [-2.20986553],\n       [-2.625621  ],\n       [-4.28063878],\n       [-3.58264137],\n       [-2.80706372],\n       [-2.89965933],\n       [-2.32073698],\n       [-2.54983095],\n       [-1.81254128],\n       [-2.76014464],\n       [-2.7371505 ],\n       [-3.60486887],\n       [-2.889826  ],\n       [-3.39215608],\n       [-1.0481819 ],\n       [-1.60991228],\n       [-3.14313097],\n       [-2.2401569 ],\n       [-2.84767378],\n       [-2.59749706],\n       [-2.94929937],\n       [-3.53003227],\n       [-2.40611054],\n       [-2.92908473],\n       [-2.18141278],\n       [-2.38092779],\n       [-3.21161722],\n       [-3.67791872],\n       [-2.4655558 ],\n       [-3.37052415],\n       [-2.60195585],\n       [-2.67783946],\n       [-2.38701709],\n       [-3.20875816]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = pca_values[:,0:1]\ny = pca_values[:,1:2]\n\nplt.scatter(x,y)",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "<matplotlib.collections.PathCollection at 0x256a1748fa0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Assignment PCA",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}