pree62/PCA Assignment.ipynb

## PCA Assignment.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Q. Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)df\n"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.260529Z",
          "start_time": "2023-08-01T11:27:00.036326Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# import the basic libraries\nimport pandas as pd\nfrom sklearn.preprocessing import scale\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.cluster import KMeans\nimport scipy .cluster.hierarchy as sch",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.338793Z",
          "start_time": "2023-08-01T11:27:04.265580Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# reading the .csv file data\ndata = pd.read_csv('wine.csv')\ndata.head()",
      "execution_count": 2,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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>",
            "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  "
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.354615Z",
          "start_time": "2023-08-01T11:27:04.341741Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#No of columns and rows \ndata.shape",
      "execution_count": 3,
      "outputs": [
        {
          "data": {
            "text/plain": "(178, 14)"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.385403Z",
          "start_time": "2023-08-01T11:27:04.358618Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Underlying information of data\ndata.info()",
      "execution_count": 4,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 178 entries, 0 to 177\nData columns (total 14 columns):\n #   Column           Non-Null Count  Dtype  \n---  ------           --------------  -----  \n 0   Type             178 non-null    int64  \n 1   Alcohol          178 non-null    float64\n 2   Malic            178 non-null    float64\n 3   Ash              178 non-null    float64\n 4   Alcalinity       178 non-null    float64\n 5   Magnesium        178 non-null    int64  \n 6   Phenols          178 non-null    float64\n 7   Flavanoids       178 non-null    float64\n 8   Nonflavanoids    178 non-null    float64\n 9   Proanthocyanins  178 non-null    float64\n 10  Color            178 non-null    float64\n 11  Hue              178 non-null    float64\n 12  Dilution         178 non-null    float64\n 13  Proline          178 non-null    int64  \ndtypes: float64(11), int64(3)\nmemory usage: 19.6 KB\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.464312Z",
          "start_time": "2023-08-01T11:27:04.389405Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Describing the data\ndata.describe()",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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>count</th>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n      <td>178.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.938202</td>\n      <td>13.000618</td>\n      <td>2.336348</td>\n      <td>2.366517</td>\n      <td>19.494944</td>\n      <td>99.741573</td>\n      <td>2.295112</td>\n      <td>2.029270</td>\n      <td>0.361854</td>\n      <td>1.590899</td>\n      <td>5.058090</td>\n      <td>0.957449</td>\n      <td>2.611685</td>\n      <td>746.893258</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.775035</td>\n      <td>0.811827</td>\n      <td>1.117146</td>\n      <td>0.274344</td>\n      <td>3.339564</td>\n      <td>14.282484</td>\n      <td>0.625851</td>\n      <td>0.998859</td>\n      <td>0.124453</td>\n      <td>0.572359</td>\n      <td>2.318286</td>\n      <td>0.228572</td>\n      <td>0.709990</td>\n      <td>314.907474</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>11.030000</td>\n      <td>0.740000</td>\n      <td>1.360000</td>\n      <td>10.600000</td>\n      <td>70.000000</td>\n      <td>0.980000</td>\n      <td>0.340000</td>\n      <td>0.130000</td>\n      <td>0.410000</td>\n      <td>1.280000</td>\n      <td>0.480000</td>\n      <td>1.270000</td>\n      <td>278.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>12.362500</td>\n      <td>1.602500</td>\n      <td>2.210000</td>\n      <td>17.200000</td>\n      <td>88.000000</td>\n      <td>1.742500</td>\n      <td>1.205000</td>\n      <td>0.270000</td>\n      <td>1.250000</td>\n      <td>3.220000</td>\n      <td>0.782500</td>\n      <td>1.937500</td>\n      <td>500.500000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.000000</td>\n      <td>13.050000</td>\n      <td>1.865000</td>\n      <td>2.360000</td>\n      <td>19.500000</td>\n      <td>98.000000</td>\n      <td>2.355000</td>\n      <td>2.135000</td>\n      <td>0.340000</td>\n      <td>1.555000</td>\n      <td>4.690000</td>\n      <td>0.965000</td>\n      <td>2.780000</td>\n      <td>673.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.000000</td>\n      <td>13.677500</td>\n      <td>3.082500</td>\n      <td>2.557500</td>\n      <td>21.500000</td>\n      <td>107.000000</td>\n      <td>2.800000</td>\n      <td>2.875000</td>\n      <td>0.437500</td>\n      <td>1.950000</td>\n      <td>6.200000</td>\n      <td>1.120000</td>\n      <td>3.170000</td>\n      <td>985.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>14.830000</td>\n      <td>5.800000</td>\n      <td>3.230000</td>\n      <td>30.000000</td>\n      <td>162.000000</td>\n      <td>3.880000</td>\n      <td>5.080000</td>\n      <td>0.660000</td>\n      <td>3.580000</td>\n      <td>13.000000</td>\n      <td>1.710000</td>\n      <td>4.000000</td>\n      <td>1680.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "             Type     Alcohol       Malic         Ash  Alcalinity   Magnesium  \\\ncount  178.000000  178.000000  178.000000  178.000000  178.000000  178.000000   \nmean     1.938202   13.000618    2.336348    2.366517   19.494944   99.741573   \nstd      0.775035    0.811827    1.117146    0.274344    3.339564   14.282484   \nmin      1.000000   11.030000    0.740000    1.360000   10.600000   70.000000   \n25%      1.000000   12.362500    1.602500    2.210000   17.200000   88.000000   \n50%      2.000000   13.050000    1.865000    2.360000   19.500000   98.000000   \n75%      3.000000   13.677500    3.082500    2.557500   21.500000  107.000000   \nmax      3.000000   14.830000    5.800000    3.230000   30.000000  162.000000   \n\n          Phenols  Flavanoids  Nonflavanoids  Proanthocyanins       Color  \\\ncount  178.000000  178.000000     178.000000       178.000000  178.000000   \nmean     2.295112    2.029270       0.361854         1.590899    5.058090   \nstd      0.625851    0.998859       0.124453         0.572359    2.318286   \nmin      0.980000    0.340000       0.130000         0.410000    1.280000   \n25%      1.742500    1.205000       0.270000         1.250000    3.220000   \n50%      2.355000    2.135000       0.340000         1.555000    4.690000   \n75%      2.800000    2.875000       0.437500         1.950000    6.200000   \nmax      3.880000    5.080000       0.660000         3.580000   13.000000   \n\n              Hue    Dilution      Proline  \ncount  178.000000  178.000000   178.000000  \nmean     0.957449    2.611685   746.893258  \nstd      0.228572    0.709990   314.907474  \nmin      0.480000    1.270000   278.000000  \n25%      0.782500    1.937500   500.500000  \n50%      0.965000    2.780000   673.500000  \n75%      1.120000    3.170000   985.000000  \nmax      1.710000    4.000000  1680.000000  "
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.480331Z",
          "start_time": "2023-08-01T11:27:04.468312Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Removing the fist column\ndata_1 = data.iloc[:,1:]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.496210Z",
          "start_time": "2023-08-01T11:27:04.483257Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Scaling the data\nuni_normal = scale(data_1)\nuni_normal",
      "execution_count": 7,
      "outputs": [
        {
          "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]])"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## PCA Algorithm"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.558173Z",
          "start_time": "2023-08-01T11:27:04.499431Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Applying the PCA algorithm\npca = PCA(n_components = 13)\npca_values = pca.fit_transform(uni_normal)\npca_values ",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[ 3.31675081e+00, -1.44346263e+00, -1.65739045e-01, ...,\n        -4.51563395e-01,  5.40810414e-01, -6.62386309e-02],\n       [ 2.20946492e+00,  3.33392887e-01, -2.02645737e+00, ...,\n        -1.42657306e-01,  3.88237741e-01,  3.63650247e-03],\n       [ 2.51674015e+00, -1.03115130e+00,  9.82818670e-01, ...,\n        -2.86672847e-01,  5.83573183e-04,  2.17165104e-02],\n       ...,\n       [-2.67783946e+00, -2.76089913e+00, -9.40941877e-01, ...,\n         5.12492025e-01,  6.98766451e-01,  7.20776948e-02],\n       [-2.38701709e+00, -2.29734668e+00, -5.50696197e-01, ...,\n         2.99821968e-01,  3.39820654e-01, -2.18657605e-02],\n       [-3.20875816e+00, -2.76891957e+00,  1.01391366e+00, ...,\n        -2.29964331e-01, -1.88787963e-01, -3.23964720e-01]])"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.573325Z",
          "start_time": "2023-08-01T11:27:04.559862Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pca.components_",
      "execution_count": 9,
      "outputs": [
        {
          "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],\n       [-0.05639636,  0.42052391, -0.14917061, -0.28696914,  0.3228833 ,\n        -0.02792498, -0.06068521,  0.59544729,  0.37213935, -0.22771214,\n         0.23207564, -0.0447637 ,  0.0768045 ],\n       [-0.39613926, -0.06582674,  0.17026002, -0.42797018,  0.15636143,\n         0.40593409,  0.18724536,  0.23328465, -0.36822675,  0.03379692,\n        -0.43662362,  0.07810789, -0.12002267],\n       [ 0.50861912, -0.07528304, -0.30769445,  0.20044931,  0.27140257,\n         0.28603452,  0.04957849,  0.19550132, -0.20914487,  0.05621752,\n         0.08582839,  0.1372269 , -0.57578611],\n       [ 0.21160473, -0.30907994, -0.02712539,  0.05279942,  0.06787022,\n        -0.32013135, -0.16315051,  0.21553507,  0.1341839 , -0.29077518,\n        -0.52239889,  0.52370587,  0.162116  ],\n       [-0.22591696,  0.07648554, -0.49869142,  0.47931378,  0.07128891,\n         0.30434119, -0.02569409,  0.11689586, -0.23736257,  0.0318388 ,\n        -0.04821201,  0.0464233 ,  0.53926983],\n       [-0.26628645,  0.12169604, -0.04962237, -0.05574287,  0.06222011,\n        -0.30388245, -0.04289883,  0.04235219, -0.09555303,  0.60422163,\n         0.259214  ,  0.60095872, -0.07940162],\n       [ 0.01496997,  0.02596375, -0.14121803,  0.09168285,  0.05677422,\n        -0.46390791,  0.83225706,  0.11403985, -0.11691707, -0.0119928 ,\n        -0.08988884, -0.15671813,  0.01444734]])"
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.588814Z",
          "start_time": "2023-08-01T11:27:04.578597Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Individual Varience\nvar = pca.explained_variance_ratio_\nvar",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "array([0.36198848, 0.1920749 , 0.11123631, 0.0706903 , 0.06563294,\n       0.04935823, 0.04238679, 0.02680749, 0.02222153, 0.01930019,\n       0.01736836, 0.01298233, 0.00795215])"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.604162Z",
          "start_time": "2023-08-01T11:27:04.589811Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Cumulative varience\nvar1 = np.cumsum(np.round(var,decimals = 4)*100)\nvar1",
      "execution_count": 11,
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 36.2 ,  55.41,  66.53,  73.6 ,  80.16,  85.1 ,  89.34,  92.02,\n        94.24,  96.17,  97.91,  99.21, 100.01])"
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.776312Z",
          "start_time": "2023-08-01T11:27:04.606156Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Variance plot for PCA components obtained \nplt.plot(var1,color=\"red\")",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x1f73b9e56a0>]"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:04.806700Z",
          "start_time": "2023-08-01T11:27:04.780585Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Final data frame constructed with the new reduced 3 features(from 13 features) PC1,PC2, PC3\nfinal_df = pd.concat([pd.DataFrame(pca_values[:,0:3],columns=['pc1','pc2','pc3']),data[['Type']]], axis = 1)\nfinal_df",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pc1</th>\n      <th>pc2</th>\n      <th>pc3</th>\n      <th>Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.316751</td>\n      <td>-1.443463</td>\n      <td>-0.165739</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.209465</td>\n      <td>0.333393</td>\n      <td>-2.026457</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.516740</td>\n      <td>-1.031151</td>\n      <td>0.982819</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.757066</td>\n      <td>-2.756372</td>\n      <td>-0.176192</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.008908</td>\n      <td>-0.869831</td>\n      <td>2.026688</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    </tr>\n    <tr>\n      <th>173</th>\n      <td>-3.370524</td>\n      <td>-2.216289</td>\n      <td>-0.342570</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>-2.601956</td>\n      <td>-1.757229</td>\n      <td>0.207581</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>-2.677839</td>\n      <td>-2.760899</td>\n      <td>-0.940942</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>-2.387017</td>\n      <td>-2.297347</td>\n      <td>-0.550696</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>177</th>\n      <td>-3.208758</td>\n      <td>-2.768920</td>\n      <td>1.013914</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>178 rows × 4 columns</p>\n</div>",
            "text/plain": "          pc1       pc2       pc3  Type\n0    3.316751 -1.443463 -0.165739     1\n1    2.209465  0.333393 -2.026457     1\n2    2.516740 -1.031151  0.982819     1\n3    3.757066 -2.756372 -0.176192     1\n4    1.008908 -0.869831  2.026688     1\n..        ...       ...       ...   ...\n173 -3.370524 -2.216289 -0.342570     3\n174 -2.601956 -1.757229  0.207581     3\n175 -2.677839 -2.760899 -0.940942     3\n176 -2.387017 -2.297347 -0.550696     3\n177 -3.208758 -2.768920  1.013914     3\n\n[178 rows x 4 columns]"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:05.376898Z",
          "start_time": "2023-08-01T11:27:04.810643Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Plotting the clusters using the final 3 features\np1 = sns.scatterplot(data=final_df,x='pc1',y='pc2',s = 40)  \nfor line in range(0,final_df.shape[0]):\n     p1.text(final_df.pc1[line], final_df.pc2[line],final_df.Type[line], horizontalalignment='left', size='medium')",
      "execution_count": 14,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:05.629298Z",
          "start_time": "2023-08-01T11:27:05.380137Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Clustring (to view better)\nsns.scatterplot(data=final_df,x='pc1',y='pc2',s = 100,hue= 'Type')  ",
      "execution_count": 15,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='pc1', ylabel='pc2'>"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-12-26T18:37:22.090743Z",
          "start_time": "2021-12-26T18:37:22.084746Z"
        }
      },
      "cell_type": "markdown",
      "source": "#### - With the use of PCA, we have reduced the no features form 13 to 3 without losing much information."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## K-Means Clustering"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:06.767074Z",
          "start_time": "2023-08-01T11:27:05.631232Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Taking the scaled data and finding the k-value using the Elbow-Plot\n#Elbow-Plot\nWCSS = []\nfor i in range(1,11):\n    kmeans = KMeans(n_clusters=i,random_state=0)\n    kmeans.fit(uni_normal)\n    WCSS.append(kmeans.inertia_)\nplt.plot(range(1,11),WCSS)\nplt.xlabel('No of clusters')\nplt.ylabel('WCSS')\nplt.title('Elbow-plot')\nplt.show()",
      "execution_count": 16,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "C:\\Users\\preet\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n  warnings.warn(\n"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### - From the above plot we can say K=3. The optimum no of clusters can be created for the scaled data using the K-Means clustering is 3."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Heirarchial clustering"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-08-01T11:27:09.209960Z",
          "start_time": "2023-08-01T11:27:06.769904Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Creating the dendrogram using the scaled data\n#Dendrogram plot\ndendrogram = sch.dendrogram(sch.linkage(uni_normal, method = 'complete'),color_threshold = 1)",
      "execution_count": 17,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### - From the above plot we can get the optimum no of clusters as 3. "
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Final conclusion:"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### - After applying the mutiple techiniques of clustering and the dimentionality reduction techniques, we can take the optimum no of clusters to be used is 3."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
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      "language": "python"
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      "data": {
        "description": "!excelR/assignments/Gists/PCA Assignment.ipynb",
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  "nbformat": 4,
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