ClusterAuto.ipynb

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  "metadata": {
    "colab": {
      "name": "AutoCluster.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN2dT+YBQ82NfwpTVHtI6Hm",
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      "name": "python"
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/ClusterAuto.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
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        },
        "id": "qJNGGL2FGem0",
        "outputId": "f9c834f2-facc-4d8d-c753-04f2640805e5"
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        {
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          "data": {
            "text/plain": [
              "    Economy  Speed  Color  Price\n",
              "0     63.38  35.47  27.11  74.88\n",
              "1     63.32  33.43  34.71  63.31\n",
              "2     43.06  22.04  35.99  56.62\n",
              "3     60.13  34.40  37.12  53.70\n",
              "4     75.73  22.90  28.03  64.21\n",
              "5     49.79  44.06  36.22  62.03\n",
              "6     56.46  43.61  30.65  65.49\n",
              "7     51.04  21.89  24.68  57.60\n",
              "8     55.09  40.33  37.08  71.70\n",
              "9     52.90  15.89  33.48  59.17\n",
              "10    63.90  31.38  27.66  65.70\n",
              "11    62.37  14.22  26.00  58.50\n",
              "12    60.22  37.15  30.59  60.70\n",
              "13    46.04  38.63  40.34  64.15\n",
              "14    72.55   8.08  38.98  71.76\n",
              "15    71.56  38.76  28.34  66.61\n",
              "16    97.18  60.12  53.56  76.21\n",
              "17    99.58  56.40  72.12  76.94\n",
              "18   100.50  58.27  70.14  78.87\n",
              "19    98.95  52.55  63.08  80.46\n",
              "20    95.79  49.77  69.94  76.79\n",
              "21    99.04  60.79  61.94  71.47\n",
              "22   117.40  66.14  74.09  78.38\n",
              "23    95.64  56.88  64.29  76.59\n",
              "24   109.27  54.04  58.45  77.34\n",
              "25   103.39  40.78  70.58  78.83\n",
              "26   117.48  59.56  65.73  83.66\n",
              "27   108.75  56.38  57.13  75.28\n",
              "28   106.02  55.41  72.94  77.24\n",
              "29   112.65  66.01  50.41  73.21\n",
              "30   106.16  51.80  64.08  73.03\n",
              "31   112.04  51.97  63.42  82.84\n",
              "32   109.91  66.20  64.63  78.81\n",
              "33     7.16  29.95  83.45  13.82\n",
              "34     2.15  23.08  95.79  21.51\n",
              "35    16.71  34.43  75.45  13.96\n",
              "36    14.65  27.46  90.05  10.17\n",
              "37     6.75  33.29  81.37   4.41\n",
              "38    27.29  23.36  94.14   8.06\n",
              "39     5.91  17.72  80.67   2.81\n",
              "40    23.72  26.10  83.28  13.04\n",
              "41    28.82  28.74  87.12  20.08\n",
              "42     1.99  25.98  79.42  13.42\n",
              "43     1.93  22.72  86.82  20.18\n",
              "44    19.53  35.12  90.31   2.47\n",
              "45    21.13  32.05  89.84  12.44\n",
              "46    32.26  23.98  82.14   7.61\n",
              "47    31.67  35.83  85.42  15.89\n",
              "48    19.35  30.36  78.95   5.32\n",
              "49    21.40  20.72  80.09  19.67"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Economy</th>\n",
              "      <th>Speed</th>\n",
              "      <th>Color</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.38</td>\n",
              "      <td>35.47</td>\n",
              "      <td>27.11</td>\n",
              "      <td>74.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>63.32</td>\n",
              "      <td>33.43</td>\n",
              "      <td>34.71</td>\n",
              "      <td>63.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43.06</td>\n",
              "      <td>22.04</td>\n",
              "      <td>35.99</td>\n",
              "      <td>56.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60.13</td>\n",
              "      <td>34.40</td>\n",
              "      <td>37.12</td>\n",
              "      <td>53.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75.73</td>\n",
              "      <td>22.90</td>\n",
              "      <td>28.03</td>\n",
              "      <td>64.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>49.79</td>\n",
              "      <td>44.06</td>\n",
              "      <td>36.22</td>\n",
              "      <td>62.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>56.46</td>\n",
              "      <td>43.61</td>\n",
              "      <td>30.65</td>\n",
              "      <td>65.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>51.04</td>\n",
              "      <td>21.89</td>\n",
              "      <td>24.68</td>\n",
              "      <td>57.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>55.09</td>\n",
              "      <td>40.33</td>\n",
              "      <td>37.08</td>\n",
              "      <td>71.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>52.90</td>\n",
              "      <td>15.89</td>\n",
              "      <td>33.48</td>\n",
              "      <td>59.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>63.90</td>\n",
              "      <td>31.38</td>\n",
              "      <td>27.66</td>\n",
              "      <td>65.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>62.37</td>\n",
              "      <td>14.22</td>\n",
              "      <td>26.00</td>\n",
              "      <td>58.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>60.22</td>\n",
              "      <td>37.15</td>\n",
              "      <td>30.59</td>\n",
              "      <td>60.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>46.04</td>\n",
              "      <td>38.63</td>\n",
              "      <td>40.34</td>\n",
              "      <td>64.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>72.55</td>\n",
              "      <td>8.08</td>\n",
              "      <td>38.98</td>\n",
              "      <td>71.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>71.56</td>\n",
              "      <td>38.76</td>\n",
              "      <td>28.34</td>\n",
              "      <td>66.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>97.18</td>\n",
              "      <td>60.12</td>\n",
              "      <td>53.56</td>\n",
              "      <td>76.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>99.58</td>\n",
              "      <td>56.40</td>\n",
              "      <td>72.12</td>\n",
              "      <td>76.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>100.50</td>\n",
              "      <td>58.27</td>\n",
              "      <td>70.14</td>\n",
              "      <td>78.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>98.95</td>\n",
              "      <td>52.55</td>\n",
              "      <td>63.08</td>\n",
              "      <td>80.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>95.79</td>\n",
              "      <td>49.77</td>\n",
              "      <td>69.94</td>\n",
              "      <td>76.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>99.04</td>\n",
              "      <td>60.79</td>\n",
              "      <td>61.94</td>\n",
              "      <td>71.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>117.40</td>\n",
              "      <td>66.14</td>\n",
              "      <td>74.09</td>\n",
              "      <td>78.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>95.64</td>\n",
              "      <td>56.88</td>\n",
              "      <td>64.29</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>109.27</td>\n",
              "      <td>54.04</td>\n",
              "      <td>58.45</td>\n",
              "      <td>77.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>103.39</td>\n",
              "      <td>40.78</td>\n",
              "      <td>70.58</td>\n",
              "      <td>78.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>117.48</td>\n",
              "      <td>59.56</td>\n",
              "      <td>65.73</td>\n",
              "      <td>83.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>108.75</td>\n",
              "      <td>56.38</td>\n",
              "      <td>57.13</td>\n",
              "      <td>75.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>106.02</td>\n",
              "      <td>55.41</td>\n",
              "      <td>72.94</td>\n",
              "      <td>77.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>112.65</td>\n",
              "      <td>66.01</td>\n",
              "      <td>50.41</td>\n",
              "      <td>73.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>106.16</td>\n",
              "      <td>51.80</td>\n",
              "      <td>64.08</td>\n",
              "      <td>73.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>112.04</td>\n",
              "      <td>51.97</td>\n",
              "      <td>63.42</td>\n",
              "      <td>82.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>109.91</td>\n",
              "      <td>66.20</td>\n",
              "      <td>64.63</td>\n",
              "      <td>78.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>7.16</td>\n",
              "      <td>29.95</td>\n",
              "      <td>83.45</td>\n",
              "      <td>13.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2.15</td>\n",
              "      <td>23.08</td>\n",
              "      <td>95.79</td>\n",
              "      <td>21.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>16.71</td>\n",
              "      <td>34.43</td>\n",
              "      <td>75.45</td>\n",
              "      <td>13.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>14.65</td>\n",
              "      <td>27.46</td>\n",
              "      <td>90.05</td>\n",
              "      <td>10.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>6.75</td>\n",
              "      <td>33.29</td>\n",
              "      <td>81.37</td>\n",
              "      <td>4.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>27.29</td>\n",
              "      <td>23.36</td>\n",
              "      <td>94.14</td>\n",
              "      <td>8.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>5.91</td>\n",
              "      <td>17.72</td>\n",
              "      <td>80.67</td>\n",
              "      <td>2.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>23.72</td>\n",
              "      <td>26.10</td>\n",
              "      <td>83.28</td>\n",
              "      <td>13.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>28.82</td>\n",
              "      <td>28.74</td>\n",
              "      <td>87.12</td>\n",
              "      <td>20.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1.99</td>\n",
              "      <td>25.98</td>\n",
              "      <td>79.42</td>\n",
              "      <td>13.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1.93</td>\n",
              "      <td>22.72</td>\n",
              "      <td>86.82</td>\n",
              "      <td>20.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>19.53</td>\n",
              "      <td>35.12</td>\n",
              "      <td>90.31</td>\n",
              "      <td>2.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>21.13</td>\n",
              "      <td>32.05</td>\n",
              "      <td>89.84</td>\n",
              "      <td>12.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>32.26</td>\n",
              "      <td>23.98</td>\n",
              "      <td>82.14</td>\n",
              "      <td>7.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>31.67</td>\n",
              "      <td>35.83</td>\n",
              "      <td>85.42</td>\n",
              "      <td>15.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>19.35</td>\n",
              "      <td>30.36</td>\n",
              "      <td>78.95</td>\n",
              "      <td>5.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>21.40</td>\n",
              "      <td>20.72</td>\n",
              "      <td>80.09</td>\n",
              "      <td>19.67</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-17b15f85-c1e9-457e-a586-5cabc66ab0a6');\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": 7
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "bn=pd.read_csv('https://raw.githubusercontent.com/ogut77/DataScience/main/data/Bonus.csv')\n",
        "bn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bn.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "bO4zfe1Qyyr2",
        "outputId": "a7c99c57-9405-4b46-86f9-86910830e4ea"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Economy     Speed     Color     Price\n",
              "Economy  1.000000  0.771567 -0.354676  0.910017\n",
              "Speed    0.771567  1.000000  0.022898  0.620146\n",
              "Color   -0.354676  0.022898  1.000000 -0.618228\n",
              "Price    0.910017  0.620146 -0.618228  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a9ac101-f001-45c3-ba33-ff1c048637e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Economy</th>\n",
              "      <th>Speed</th>\n",
              "      <th>Color</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Economy</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.771567</td>\n",
              "      <td>-0.354676</td>\n",
              "      <td>0.910017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Speed</th>\n",
              "      <td>0.771567</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.022898</td>\n",
              "      <td>0.620146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Color</th>\n",
              "      <td>-0.354676</td>\n",
              "      <td>0.022898</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.618228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>0.910017</td>\n",
              "      <td>0.620146</td>\n",
              "      <td>-0.618228</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a9ac101-f001-45c3-ba33-ff1c048637e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a9ac101-f001-45c3-ba33-ff1c048637e3 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-0a9ac101-f001-45c3-ba33-ff1c048637e3');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 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",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.title(\"Customer Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(bn, method='ward'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "Tb5qciFGJaoq",
        "outputId": "00211b71-e468-4e33-edcc-05afd998dd40"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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YSZIkVTJ/thsgST2tWAErV852KzSIifc1/5cePbvt0NSWLYPly2e7FXOawUrSaFq5EiYmYMmS2W6JprBqiYFqLExMNP8NVkNlsJI0upYsgVWrZrsV0tywdOlst2CL4BgrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlUwariNg9Is6PiEsi4uKIeF2Z/9CIOCciflb+P6TMj4h4f0SsiYgLI+KJw34RkiRJo2CQHqsNwBsyc29gf+A1EbE38Cbg3MzcCzi33AY4GNir/C0HPlS91ZIkSSNoymCVmWsz84dl+nbgUmARcBhwcil2MnB4mT4MOCUb3wMWRMSu1VsuSZI0YlqNsYqIxcA+wPeBnTNzbbnrOmDnMr0IuKrjYVeXeZIkSXPawMEqIrYHPg0cnZm3dd6XmQlkmyeOiOURsToiVq9fv77NQyVJkkbSQMEqIramCVX/mZlnltnrJk/xlf/Xl/nXALt3PHy3Mu8+MnNFZu6bmfsuXLhwuu2XJEkaGYN8KzCAE4FLM/O9HXedDRxZpo8EzuqY/4ry7cD9gVs7ThlKkiTNWfMHKPNU4OXARRExUea9BTgWOCMijgKuBF5c7vsicAiwBrgLeFXVFkuSJI2oKYNVZn4LiD53H9ijfAKvmWG7JEmSxo5XXpckSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZVMGawi4mMRcX1E/KRj3kMj4pyI+Fn5/5AyPyLi/RGxJiIujIgnDrPxkiRJo2SQHquTgIO65r0JODcz9wLOLbcBDgb2Kn/LgQ/VaaYkSdLomzJYZeY3gJu6Zh8GnFymTwYO75h/Sja+ByyIiF1rNVaSJGmUTXeM1c6ZubZMXwfsXKYXAVd1lLu6zJMkSZrzZjx4PTMTyLaPi4jlEbE6IlavX79+ps2QJEmaddMNVusmT/GV/9eX+dcAu3eU263Mu5/MXJGZ+2bmvgsXLpxmMyRJkkbHdIPV2cCRZfpI4KyO+a8o3w7cH7i145ShJEnSnDZ/qgIR8QlgKbBTRFwN/BNwLHBGRBwFXAm8uBT/InAIsAa4C3jVENosSZI0kqYMVpn50j53HdijbAKvmWmjJEmSxpFXXpckSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIl82e7AZIkjawVK2DlytluRR0TE83/pUtntRnVLFsGy5fPdivuxx4rSZL6Wbny3kAy7pYsaf7mgomJkQ28c77HasUFK1h50Wgu/LYmrnsfAEtPOnqWW1LHssctY/mTRu/ThiTdx5IlsGrVbLdCnUa4123OB6uVF61k4roJluwy/il9yZvmRqACmLiu+QRosJIkzSVzPlgBLNllCateuWq2m6EOS09aOttNkCSpOsdYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkirZIi4QKkmSZskwfsh6GD8oXelHnQ1W0lw0jAPZ5jaMA+dsqXTAlsbS5A9Z1/wB6No/Jj15vDFYSeppGAeyzW2c296p4gFbGluj/kPWFT/AGaykuWrUD2RbirnQ4yZpYA5elyRJqsRgJUmSVImnAiVpmIP9hz0I34Hx473+wHU4x9hjJUmTg/2HYcmS4Q3En5gY/29/1jCu6w9ch3OQPVaSBOM52N+B8fcax/UHrsM5yB4rSZKkSsaqx2rFBStYeVG7LtOJ65ru4aUnLW39fMset4zlT5q7572nszxrmcl6qWWur19J0uY3Vj1WKy9a+Zs35EEt2WUJS3Zpf3584rqJWQsdm8t0lmct010vtWwJ61eStPmNVY8VNG/Iq165aujPM5s9KZvT5lqeo2ZLWb+SpM1rrHqsJEmSRpnBSpIkqZKxOxUoSdLIme5FSqd7AVIvKjqyDFYae5vr26JD/xZhzatH17xatAfw+mqt61rr2XU8c5MXKW17MdHpXHx0cr27ztrrt+/125emsW8YrMZUjUsl9AsXa29fy7o71w1cT5tv9w0jnEx+u7FNO9p+I3FyWQ01WE33wNxLrStFewAfjlrrevLxa9fCusH32fu49daZXf3bUHavzXWR0lG6qOggHxLafAAY9vbUb9/rtS9O8/i3xQWrQQNJmx6N2bge0nTCRLd+j1135zru+PUdbL/N9tOuu5dhhpNhf7txs32LcNSuHj1KB/C22vQKte35qXHwr7muly5tgtUwf3qlF4O3BvmQMOh2ubm2p0H3vWke/7a4YDVoIBk0sGyWnow+hhUmJkNE7bq9xIE2qza9Qm0CyaiGidkI5eMcvFVPrW1vjmxPW1ywgrqBxLCgkVVjHE+NMTyzeapoGGFjjhz8R96o9zgOw6CveZROrel+tshgNW56nb7c1KlKf6pFQJ1xPDM9tTSqvTsafVtajyMM/ppH7dTaoKYKjoMGxhEPiwarTRhkPNYgY7FmGnR6nb7sd6pyNk9NjrOp1vWgY+5GLtTO9pitce/d6fVGsKmD/4gf8MfOqPY4DnO7qD32bpRMFRwHCYyjFhZ7MFhtwiDjsaYai1Ur6Ax6+nKun5ocVu/dVOt6kDF3hto5qNcbQb+D/xgc8FWJ28X0DRocN9W7NTFR5bIIwzKywWpUTn/NdDzWXA86vfTr/em3/tqsu2H23rmup2EUuvbbXpem7fMN+RtEU6r1dfaZvvGMW+/dXN8u5rrNcFmEYRnZYDVup7+GGSaGZVht7tf702v9TWfdzXbv3Tiu66EZha79NgfgGs+3udX4OnuN1zysXpphXbBxrm8XoxK4h2lMw+vIBiuY/TfQNoYdJoZhmG0ep3XX1sis6xq9RZvzekzD7NpvMy5lxA7CA5npuJtar3kYb3TD7JmYy9tFm8Dd7wKym7o47CgHrhE30sFq3IxjmBjHNo+CkVhuM+0t2tyf0Me4a19DNqY9E7OuzXJrcwFZ978ZMVhJ42wmPRmz8SblG6g0O+Zy792IGUqwioiDgBOAecBHM/PYYTyPpAGN28BjTc+wB2xLmtJWtSuMiHnAvwEHA3sDL42IvWs/j6QWJk/DdVqypP9puJlesV2zo9d6Bte1tBkNo8dqP2BNZl4OEBGnAYcBlwzhuSQNytNwWwZP+UizqnqPFbAIuKrj9tVlniRJ0pwWmVm3wogXAgdl5p+X2y8HnpyZr+0qtxyYPLH/aOCnVRsiSZI0HHtk5sJedwzjVOA1wO4dt3cr8+4jM1cAK4bw/JIkSbNiGKcCfwDsFRF7RsQ2wBHA2UN4HkmSpJFSvccqMzdExGuBr9BcbuFjmXlx7eeRJEkaNdXHWEmSJG2phnEqUJIkaYtksJIkSarEYCVJklSJP8IsSZoVEfFY4LHAZZn5gxnWtWtmro2IoPm1j98Dfg58KjM39Ci/NXAQcGNmficiXgY8GPjPzLylR/knAU8BFgC3AN/LzNUzaXNHvVcBNwKHAr/IzK/OtN5NPN/vV1jWjwE2ZuZ/d8x7cmZ+fwZ1Pg/4WmbeNWD5xwF/QLM+1gFfycy1Az72NZn5b9Nt65T1j/Lg9U29+DYb46ArYBortk0bBt4Q2+zwbQ8mfdp2vx1tmstioIPOdHeIqQ7CbQ+UPR7fc3ub7jIe5E0jIhZMti0iDp0sX+rOrrIPBf6UZns7E/g74EHABzPz511lB15/Lbe3ecDhdK1r4LP9lsUg6zsitqXZh35Gs2z/DPgFcEpm/rJHnftk5o8i4oHAq4HfLY/790HWdamj13YfwCHARuCrmXlPmX9YZp7Vo46B9utS76Fdy+ELm9guZvQmHhFvz8x/7DH/b4DPT/7k2BR11Di2PDczP9c178uZeVBEHA0cCHwBeCpwdWa+uU89Ux5rI+K8zDwgIk6g2XbOA5YA+2bmi3vU+RmaywMtAJ4EfBG4AViWmc/pKns8sC3wNeBWmv3uj4ANmfm6Tbz+qY5ZJwIB/Ap4GM01H28DHpaZPX8Ze9BtIyJ6nZEK4MuZ+awe5Qfdlo8DdgbuBnYC/iwz108u/15t7vF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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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(bn)\n"
      ],
      "metadata": {
        "id": "JKSB3L0eUy18"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate Centroids\n",
        "cl2=pd.DataFrame(cl)\n",
        "bon2=pd.concat([bn,cl2],axis=1)\n",
        "bon2.groupby(0).mean().T\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "kmRPhNX-Wdfg",
        "outputId": "ba2b49a0-6628-4562-ee5c-f16161ef21ef"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                0          1           2\n",
              "Economy  59.221250  16.612941  105.279412\n",
              "Speed    30.140000  27.699412   56.651176\n",
              "Color    32.311250  84.959412   64.501765\n",
              "Price    63.508125  12.050588   77.408824"
            ],
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc466cae-d0a8-4d0d-8898-dd66404e8535 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-cc466cae-d0a8-4d0d-8898-dd66404e8535');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(bn['Economy'], bn['Color'], c=cluster.labels_, cmap='rainbow')\n",
        "plt.xlabel('Economy')\n",
        "plt.ylabel('Color')"
      ],
      "metadata": {
        "id": "UjrNPLsszeZ6",
        "outputId": "1463e460-9ecf-47a5-8a5c-8a1b074ac42c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Color')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}