ClAuto.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoCluster.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN9kLK+0ZOIIHSY0KtxM7jC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/ClAuto.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "auto=pd.read_csv('https://raw.githubusercontent.com/ogut77/DataScience/main/data/Auto2.csv')\n",
        "auto"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gDEUgVMcPeY9",
        "outputId": "33c86eb5-b885-4bbf-dfd0-e466f071b196"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Country                        Car   MPG  Weight  Drive_Ratio  Horsepower  \\\n",
              "0      U.S.         Buick Estate Wagon  16.9   4.360         2.73         155   \n",
              "1      U.S.  Ford Country Squire Wagon  15.5   4.054         2.26         142   \n",
              "2      U.S.         Chevy Malibu Wagon  19.2   3.605         2.56         125   \n",
              "3      U.S.     Chrysler LeBaron Wagon  18.5   3.940         2.45         150   \n",
              "4      U.S.                   Chevette  30.0   2.155         3.70          68   \n",
              "5     Japan              Toyota Corona  27.5   2.560         3.05          95   \n",
              "6     Japan                 Datsun 510  27.2   2.300         3.54          97   \n",
              "7      U.S.                 Dodge Omni  30.9   2.230         3.37          75   \n",
              "8   Germany                  Audi 5000  20.3   2.830         3.90         103   \n",
              "9    Sweden               Volvo 240 GL  17.0   3.140         3.50         125   \n",
              "10   Sweden                Saab 99 GLE  21.6   2.795         3.77         115   \n",
              "11   France             Peugeot 694 SL  16.2   3.410         3.58         133   \n",
              "12     U.S.      Buick Century Special  20.6   3.380         2.73         105   \n",
              "13     U.S.             Mercury Zephyr  20.8   3.070         3.08          85   \n",
              "14     U.S.                Dodge Aspen  18.6   3.620         2.71         110   \n",
              "15     U.S.            AMC Concord D/L  18.1   3.410         2.73         120   \n",
              "16     U.S.      Chevy Caprice Classic  17.0   3.840         2.41         130   \n",
              "17     U.S.                   Ford LTD  17.6   3.725         2.26         129   \n",
              "18     U.S.      Mercury Grand Marquis  16.5   3.955         2.26         138   \n",
              "19     U.S.             Dodge St Regis  18.2   3.830         2.45         135   \n",
              "20     U.S.             Ford Mustang 4  26.5   2.585         3.08          88   \n",
              "21     U.S.          Ford Mustang Ghia  21.9   2.910         3.08         109   \n",
              "22    Japan                  Mazda GLC  34.1   1.975         3.73          65   \n",
              "23    Japan                 Dodge Colt  35.1   1.915         2.97          80   \n",
              "24     U.S.                 AMC Spirit  27.4   2.670         3.08          80   \n",
              "25  Germany                VW Scirocco  31.5   1.990         3.78          71   \n",
              "26    Japan            Honda Accord LX  29.5   2.135         3.05          68   \n",
              "27     U.S.              Buick Skylark  28.4   2.670         2.53          90   \n",
              "28     U.S.             Chevy Citation  28.8   2.595         2.69         115   \n",
              "29     U.S.                 Olds Omega  26.8   2.700         2.84         115   \n",
              "30     U.S.            Pontiac Phoenix  33.5   2.556         2.69          90   \n",
              "31     U.S.           Plymouth Horizon  34.2   2.200         3.37          70   \n",
              "32    Japan                 Datsun 210  31.8   2.020         3.70          65   \n",
              "33    Italy                Fiat Strada  37.3   2.130         3.10          69   \n",
              "34  Germany                  VW Dasher  30.5   2.190         3.70          78   \n",
              "35    Japan                 Datsun 810  22.0   2.815         3.70          97   \n",
              "36  Germany                   BMW 320i  21.5   2.600         3.64         110   \n",
              "37  Germany                  VW Rabbit  31.9   1.925         3.78          71   \n",
              "\n",
              "    Displacement  Cylinders  \n",
              "0            350          8  \n",
              "1            351          8  \n",
              "2            267          8  \n",
              "3            360          8  \n",
              "4             98          4  \n",
              "5            134          4  \n",
              "6            119          4  \n",
              "7            105          4  \n",
              "8            131          5  \n",
              "9            163          6  \n",
              "10           121          4  \n",
              "11           163          6  \n",
              "12           231          6  \n",
              "13           200          6  \n",
              "14           225          6  \n",
              "15           258          6  \n",
              "16           305          8  \n",
              "17           302          8  \n",
              "18           351          8  \n",
              "19           318          8  \n",
              "20           140          4  \n",
              "21           171          6  \n",
              "22            86          4  \n",
              "23            98          4  \n",
              "24           121          4  \n",
              "25            89          4  \n",
              "26            98          4  \n",
              "27           151          4  \n",
              "28           173          6  \n",
              "29           173          6  \n",
              "30           151          4  \n",
              "31           105          4  \n",
              "32            85          4  \n",
              "33            91          4  \n",
              "34            97          4  \n",
              "35           146          6  \n",
              "36           121          4  \n",
              "37            89          4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32693f16-1ba6-46c6-b5cc-8bc21c255710\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Car</th>\n",
              "      <th>MPG</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Drive_Ratio</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>Displacement</th>\n",
              "      <th>Cylinders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Buick Estate Wagon</td>\n",
              "      <td>16.9</td>\n",
              "      <td>4.360</td>\n",
              "      <td>2.73</td>\n",
              "      <td>155</td>\n",
              "      <td>350</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Ford Country Squire Wagon</td>\n",
              "      <td>15.5</td>\n",
              "      <td>4.054</td>\n",
              "      <td>2.26</td>\n",
              "      <td>142</td>\n",
              "      <td>351</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Chevy Malibu Wagon</td>\n",
              "      <td>19.2</td>\n",
              "      <td>3.605</td>\n",
              "      <td>2.56</td>\n",
              "      <td>125</td>\n",
              "      <td>267</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Chrysler LeBaron Wagon</td>\n",
              "      <td>18.5</td>\n",
              "      <td>3.940</td>\n",
              "      <td>2.45</td>\n",
              "      <td>150</td>\n",
              "      <td>360</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Chevette</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2.155</td>\n",
              "      <td>3.70</td>\n",
              "      <td>68</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Toyota Corona</td>\n",
              "      <td>27.5</td>\n",
              "      <td>2.560</td>\n",
              "      <td>3.05</td>\n",
              "      <td>95</td>\n",
              "      <td>134</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Datsun 510</td>\n",
              "      <td>27.2</td>\n",
              "      <td>2.300</td>\n",
              "      <td>3.54</td>\n",
              "      <td>97</td>\n",
              "      <td>119</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Dodge Omni</td>\n",
              "      <td>30.9</td>\n",
              "      <td>2.230</td>\n",
              "      <td>3.37</td>\n",
              "      <td>75</td>\n",
              "      <td>105</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Audi 5000</td>\n",
              "      <td>20.3</td>\n",
              "      <td>2.830</td>\n",
              "      <td>3.90</td>\n",
              "      <td>103</td>\n",
              "      <td>131</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>Volvo 240 GL</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3.140</td>\n",
              "      <td>3.50</td>\n",
              "      <td>125</td>\n",
              "      <td>163</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>Saab 99 GLE</td>\n",
              "      <td>21.6</td>\n",
              "      <td>2.795</td>\n",
              "      <td>3.77</td>\n",
              "      <td>115</td>\n",
              "      <td>121</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>France</td>\n",
              "      <td>Peugeot 694 SL</td>\n",
              "      <td>16.2</td>\n",
              "      <td>3.410</td>\n",
              "      <td>3.58</td>\n",
              "      <td>133</td>\n",
              "      <td>163</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Buick Century Special</td>\n",
              "      <td>20.6</td>\n",
              "      <td>3.380</td>\n",
              "      <td>2.73</td>\n",
              "      <td>105</td>\n",
              "      <td>231</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Mercury Zephyr</td>\n",
              "      <td>20.8</td>\n",
              "      <td>3.070</td>\n",
              "      <td>3.08</td>\n",
              "      <td>85</td>\n",
              "      <td>200</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Dodge Aspen</td>\n",
              "      <td>18.6</td>\n",
              "      <td>3.620</td>\n",
              "      <td>2.71</td>\n",
              "      <td>110</td>\n",
              "      <td>225</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>AMC Concord D/L</td>\n",
              "      <td>18.1</td>\n",
              "      <td>3.410</td>\n",
              "      <td>2.73</td>\n",
              "      <td>120</td>\n",
              "      <td>258</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Chevy Caprice Classic</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3.840</td>\n",
              "      <td>2.41</td>\n",
              "      <td>130</td>\n",
              "      <td>305</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Ford LTD</td>\n",
              "      <td>17.6</td>\n",
              "      <td>3.725</td>\n",
              "      <td>2.26</td>\n",
              "      <td>129</td>\n",
              "      <td>302</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Mercury Grand Marquis</td>\n",
              "      <td>16.5</td>\n",
              "      <td>3.955</td>\n",
              "      <td>2.26</td>\n",
              "      <td>138</td>\n",
              "      <td>351</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Dodge St Regis</td>\n",
              "      <td>18.2</td>\n",
              "      <td>3.830</td>\n",
              "      <td>2.45</td>\n",
              "      <td>135</td>\n",
              "      <td>318</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Ford Mustang 4</td>\n",
              "      <td>26.5</td>\n",
              "      <td>2.585</td>\n",
              "      <td>3.08</td>\n",
              "      <td>88</td>\n",
              "      <td>140</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Ford Mustang Ghia</td>\n",
              "      <td>21.9</td>\n",
              "      <td>2.910</td>\n",
              "      <td>3.08</td>\n",
              "      <td>109</td>\n",
              "      <td>171</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Mazda GLC</td>\n",
              "      <td>34.1</td>\n",
              "      <td>1.975</td>\n",
              "      <td>3.73</td>\n",
              "      <td>65</td>\n",
              "      <td>86</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Dodge Colt</td>\n",
              "      <td>35.1</td>\n",
              "      <td>1.915</td>\n",
              "      <td>2.97</td>\n",
              "      <td>80</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>AMC Spirit</td>\n",
              "      <td>27.4</td>\n",
              "      <td>2.670</td>\n",
              "      <td>3.08</td>\n",
              "      <td>80</td>\n",
              "      <td>121</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Germany</td>\n",
              "      <td>VW Scirocco</td>\n",
              "      <td>31.5</td>\n",
              "      <td>1.990</td>\n",
              "      <td>3.78</td>\n",
              "      <td>71</td>\n",
              "      <td>89</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Honda Accord LX</td>\n",
              "      <td>29.5</td>\n",
              "      <td>2.135</td>\n",
              "      <td>3.05</td>\n",
              "      <td>68</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Buick Skylark</td>\n",
              "      <td>28.4</td>\n",
              "      <td>2.670</td>\n",
              "      <td>2.53</td>\n",
              "      <td>90</td>\n",
              "      <td>151</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Chevy Citation</td>\n",
              "      <td>28.8</td>\n",
              "      <td>2.595</td>\n",
              "      <td>2.69</td>\n",
              "      <td>115</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Olds Omega</td>\n",
              "      <td>26.8</td>\n",
              "      <td>2.700</td>\n",
              "      <td>2.84</td>\n",
              "      <td>115</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Pontiac Phoenix</td>\n",
              "      <td>33.5</td>\n",
              "      <td>2.556</td>\n",
              "      <td>2.69</td>\n",
              "      <td>90</td>\n",
              "      <td>151</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Plymouth Horizon</td>\n",
              "      <td>34.2</td>\n",
              "      <td>2.200</td>\n",
              "      <td>3.37</td>\n",
              "      <td>70</td>\n",
              "      <td>105</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Datsun 210</td>\n",
              "      <td>31.8</td>\n",
              "      <td>2.020</td>\n",
              "      <td>3.70</td>\n",
              "      <td>65</td>\n",
              "      <td>85</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Italy</td>\n",
              "      <td>Fiat Strada</td>\n",
              "      <td>37.3</td>\n",
              "      <td>2.130</td>\n",
              "      <td>3.10</td>\n",
              "      <td>69</td>\n",
              "      <td>91</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Germany</td>\n",
              "      <td>VW Dasher</td>\n",
              "      <td>30.5</td>\n",
              "      <td>2.190</td>\n",
              "      <td>3.70</td>\n",
              "      <td>78</td>\n",
              "      <td>97</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Datsun 810</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2.815</td>\n",
              "      <td>3.70</td>\n",
              "      <td>97</td>\n",
              "      <td>146</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Germany</td>\n",
              "      <td>BMW 320i</td>\n",
              "      <td>21.5</td>\n",
              "      <td>2.600</td>\n",
              "      <td>3.64</td>\n",
              "      <td>110</td>\n",
              "      <td>121</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Germany</td>\n",
              "      <td>VW Rabbit</td>\n",
              "      <td>31.9</td>\n",
              "      <td>1.925</td>\n",
              "      <td>3.78</td>\n",
              "      <td>71</td>\n",
              "      <td>89</td>\n",
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              "    MPG  Weight  Drive_Ratio  Horsepower  Displacement  Cylinders\n",
              "0  16.9   4.360         2.73         155           350          8\n",
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              "2  19.2   3.605         2.56         125           267          8\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-744dbd0b-f46e-4087-b0a9-30a8ffb12fe2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-744dbd0b-f46e-4087-b0a9-30a8ffb12fe2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# huge jump in distance is typically what we're interested in\n",
        "# if we want to argue for a certain number of clusters. \n",
        "import scipy.cluster.hierarchy as shc\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.title(\"Customer Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(oto, method='ward'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "Zn9BJ0vtUYOS",
        "outputId": "70181ff8-6ede-4385-9e68-ecf31b63992c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "cluster = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')\n",
        "cl=cluster.fit_predict(oto)\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(oto['MPG'], oto['Horsepower'], c=cluster.labels_, cmap='rainbow')\n",
        "plt.xlabel('MPG')\n",
        "plt.ylabel('Horsepower')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "JKSB3L0eUy18",
        "outputId": "d1a1d41f-cdfd-425b-fc85-fd4131d78797"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Horsepower')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q09LkNP6GQ-i",
        "outputId": "aaf78745-1ca9-45ad-85fe-a5372a35d596"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
              "       2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Centroids\n",
        "cl2=pd.DataFrame(cl)\n",
        "cl2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kmRPhNX-Wdfg",
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    {
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        "oto2=pd.concat([oto,cl2],axis=1)\n",
        "oto2\n",
        "#"
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      "execution_count": 8,
      "outputs": [
        {
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              "     MPG  Weight  Drive_Ratio  Horsepower  Displacement  Cylinders  0\n",
              "0   16.9   4.360         2.73         155           350          8  1\n",
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              "30  33.5   2.556         2.69          90           151          4  0\n",
              "31  34.2   2.200         3.37          70           105          4  2\n",
              "32  31.8   2.020         3.70          65            85          4  2\n",
              "33  37.3   2.130         3.10          69            91          4  2\n",
              "34  30.5   2.190         3.70          78            97          4  2\n",
              "35  22.0   2.815         3.70          97           146          6  0\n",
              "36  21.5   2.600         3.64         110           121          4  0\n",
              "37  31.9   1.925         3.78          71            89          4  2"
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              "      <td>120</td>\n",
              "      <td>258</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17.0</td>\n",
              "      <td>3.840</td>\n",
              "      <td>2.41</td>\n",
              "      <td>130</td>\n",
              "      <td>305</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>17.6</td>\n",
              "      <td>3.725</td>\n",
              "      <td>2.26</td>\n",
              "      <td>129</td>\n",
              "      <td>302</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>16.5</td>\n",
              "      <td>3.955</td>\n",
              "      <td>2.26</td>\n",
              "      <td>138</td>\n",
              "      <td>351</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>18.2</td>\n",
              "      <td>3.830</td>\n",
              "      <td>2.45</td>\n",
              "      <td>135</td>\n",
              "      <td>318</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>26.5</td>\n",
              "      <td>2.585</td>\n",
              "      <td>3.08</td>\n",
              "      <td>88</td>\n",
              "      <td>140</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>21.9</td>\n",
              "      <td>2.910</td>\n",
              "      <td>3.08</td>\n",
              "      <td>109</td>\n",
              "      <td>171</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>34.1</td>\n",
              "      <td>1.975</td>\n",
              "      <td>3.73</td>\n",
              "      <td>65</td>\n",
              "      <td>86</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>35.1</td>\n",
              "      <td>1.915</td>\n",
              "      <td>2.97</td>\n",
              "      <td>80</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>27.4</td>\n",
              "      <td>2.670</td>\n",
              "      <td>3.08</td>\n",
              "      <td>80</td>\n",
              "      <td>121</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>31.5</td>\n",
              "      <td>1.990</td>\n",
              "      <td>3.78</td>\n",
              "      <td>71</td>\n",
              "      <td>89</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>29.5</td>\n",
              "      <td>2.135</td>\n",
              "      <td>3.05</td>\n",
              "      <td>68</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28.4</td>\n",
              "      <td>2.670</td>\n",
              "      <td>2.53</td>\n",
              "      <td>90</td>\n",
              "      <td>151</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>28.8</td>\n",
              "      <td>2.595</td>\n",
              "      <td>2.69</td>\n",
              "      <td>115</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>26.8</td>\n",
              "      <td>2.700</td>\n",
              "      <td>2.84</td>\n",
              "      <td>115</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>33.5</td>\n",
              "      <td>2.556</td>\n",
              "      <td>2.69</td>\n",
              "      <td>90</td>\n",
              "      <td>151</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>34.2</td>\n",
              "      <td>2.200</td>\n",
              "      <td>3.37</td>\n",
              "      <td>70</td>\n",
              "      <td>105</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>31.8</td>\n",
              "      <td>2.020</td>\n",
              "      <td>3.70</td>\n",
              "      <td>65</td>\n",
              "      <td>85</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>37.3</td>\n",
              "      <td>2.130</td>\n",
              "      <td>3.10</td>\n",
              "      <td>69</td>\n",
              "      <td>91</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>30.5</td>\n",
              "      <td>2.190</td>\n",
              "      <td>3.70</td>\n",
              "      <td>78</td>\n",
              "      <td>97</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>22.0</td>\n",
              "      <td>2.815</td>\n",
              "      <td>3.70</td>\n",
              "      <td>97</td>\n",
              "      <td>146</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>21.5</td>\n",
              "      <td>2.600</td>\n",
              "      <td>3.64</td>\n",
              "      <td>110</td>\n",
              "      <td>121</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>31.9</td>\n",
              "      <td>1.925</td>\n",
              "      <td>3.78</td>\n",
              "      <td>71</td>\n",
              "      <td>89</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oto2.groupby(0).mean().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "b0ZETKFEH9pm",
        "outputId": "bbbdd862-b222-4600-c142-9fba10349cfb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                      0           1          2\n",
              "MPG            23.482353   17.500000  32.016667\n",
              "Weight          2.855059    3.857667   2.127917\n",
              "Drive_Ratio     3.182941    2.456667   3.444167\n",
              "Horsepower    104.823529  136.000000  71.666667\n",
              "Displacement  159.588235  318.000000  96.833333\n",
              "Cylinders       5.117647    7.777778   4.000000"
            ],
            "text/html": [
              "\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MPG</th>\n",
              "      <td>23.482353</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>32.016667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>2.855059</td>\n",
              "      <td>3.857667</td>\n",
              "      <td>2.127917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Drive_Ratio</th>\n",
              "      <td>3.182941</td>\n",
              "      <td>2.456667</td>\n",
              "      <td>3.444167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>104.823529</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>71.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>159.588235</td>\n",
              "      <td>318.000000</td>\n",
              "      <td>96.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>5.117647</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-ae81da55-2a5c-4e67-967c-e725d46946c9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-means"
      ],
      "metadata": {
        "id": "aejK1gXmKT-k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Number of clusters\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "# Fitting the input data\n",
        "kmeans = kmeans.fit(oto)\n",
        "# Getting the cluster labels\n",
        "labels = kmeans.predict(oto)"
      ],
      "metadata": {
        "id": "8fUagHZkKWE-",
        "outputId": "e858958d-f679-4c04-d08d-e643aa44289e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#k means clusters\n",
        "labels"
      ],
      "metadata": {
        "id": "gFnnwX4wKcKU",
        "outputId": "7dd55bd0-da20-49b0-ac3d-5be194e1e47d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2,\n",
              "       1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Hiearchical method's clusters\n",
        "cl"
      ],
      "metadata": {
        "id": "eh8tX1vYKjBg",
        "outputId": "ac84352f-65b5-438a-e6ca-c6f92e84707d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
              "       2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "distortions = []\n",
        "K = range(1,10)\n",
        "for k in K:\n",
        "    kmeanModel = KMeans(n_clusters=k)\n",
        "    kmeanModel.fit(oto)\n",
        "    distortions.append(kmeanModel.inertia_)\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.plot(K, distortions, 'bx-')\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('Distortion')\n",
        "plt.title('The Elbow Method showing the optimal k')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "L8272HYXJtGH",
        "outputId": "c2fc7128-c3b3-4125-b1bd-646881afa189",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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    }
  ]
}