Application_(Pima_diabetes).ipynb

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      "name": "Application (Pima_diabetes).ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/arghac14/UndErNsembled/blob/master/Application_(Pima_diabetes).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t15oqb9wsGW"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxdVHpWJwx5P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "7bf7f968-4fb2-429e-be0a-43fd41b78180"
      },
      "source": [
        "import pandas as pd \n",
        "import io \n",
        "#colon_df = pd.read_csv(io.BytesIO(uploaded['colon.csv']),index_col=0)\n",
        "#colon_df = pd.read_csv(\"https://raw.githubusercontent.com/disha2sinha/Reducing-Imbalanced-dataset-by-Under-sampling-approach-Consensus-Clustering/main/Datasets/colon.csv\",index_col=0)\n",
        "colon_df = pd.read_csv(\"https://raw.githubusercontent.com/arghac14/UndErNsembled/master/Datasets/pima-diabetes.csv\")\n",
        "colon_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72  ...                     0.627   50        1\n",
              "1            1       85             66  ...                     0.351   31        0\n",
              "2            8      183             64  ...                     0.672   32        1\n",
              "3            1       89             66  ...                     0.167   21        0\n",
              "4            0      137             40  ...                     2.288   33        1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1398
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nRGkBidAfOU"
      },
      "source": [
        "target = 'Outcome'\n",
        "maj_val = 0\n",
        "min_val = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdyFo6S41GYq",
        "outputId": "6a8423ba-2656-413a-d83b-40f4290dacfa"
      },
      "source": [
        "colon_majority_df=colon_df[colon_df[target]==maj_val]\n",
        "colon_majority_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1400
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAWaJl6a1htd",
        "outputId": "d741b2e9-c35b-48ce-f790-ff92da4d2c30"
      },
      "source": [
        "colon_minority_df=colon_df[colon_df[target]==min_val]\n",
        "colon_minority_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(268, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1401
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RnsGY5Si70j"
      },
      "source": [
        "# colon_df['Class'].replace(to_replace=[['Abnormal'], ['Normal']],value=[0,1],inplace=True)\n",
        "# colon_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "6WiGc2xOqpWl",
        "outputId": "33950bb9-48bd-4552-9fd9-71a75a75068f"
      },
      "source": [
        "colon_majority_df=colon_df[colon_df[target]==maj_val]\n",
        "colon_majority_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
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              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
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            "text/plain": [
              "    Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
              "1             1       85             66  ...                     0.351   31        0\n",
              "3             1       89             66  ...                     0.167   21        0\n",
              "5             5      116             74  ...                     0.201   30        0\n",
              "7            10      115              0  ...                     0.134   29        0\n",
              "10            4      110             92  ...                     0.191   30        0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1403
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "LWzywXyZrEY5",
        "outputId": "3ff0d7de-ff12-47d0-b82d-52b021a5b267"
      },
      "source": [
        "colon_minority_df=colon_df[colon_df[target]==min_val]\n",
        "colon_minority_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
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              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>78</td>\n",
              "      <td>50</td>\n",
              "      <td>32</td>\n",
              "      <td>88</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.248</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>197</td>\n",
              "      <td>70</td>\n",
              "      <td>45</td>\n",
              "      <td>543</td>\n",
              "      <td>30.5</td>\n",
              "      <td>0.158</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72  ...                     0.627   50        1\n",
              "2            8      183             64  ...                     0.672   32        1\n",
              "4            0      137             40  ...                     2.288   33        1\n",
              "6            3       78             50  ...                     0.248   26        1\n",
              "8            2      197             70  ...                     0.158   53        1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1404
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NZfct52rmss",
        "outputId": "a1e8733d-ff31-41c9-c86b-6a426ee2c8d7"
      },
      "source": [
        "colon_df.isnull().values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1405
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jdy08_6tbir"
      },
      "source": [
        "![WhatsApp Image 2021-01-03 at 1.38.50 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "-vrFYiCSti73",
        "outputId": "c8570de5-fc37-4046-b971-8b4e8a37359a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "\n",
        "count_classes = pd.value_counts(colon_df[target], sort = True)\n",
        "\n",
        "count_classes.plot(kind = 'bar', rot=0)\n",
        "\n",
        "plt.title(\"Transaction Class Distribution\")\n",
        "LABELS = [\"0\", \"1\"]\n",
        "plt.xticks(range(2), LABELS)\n",
        "\n",
        "plt.xlabel(\"Class\")\n",
        "\n",
        "plt.ylabel(\"Frequency\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1406
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ibv0BJ33uxbM",
        "outputId": "ff658857-1004-4b61-990c-576c4ddab70a"
      },
      "source": [
        "print(colon_minority_df.shape,colon_majority_df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(268, 9) (500, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14MpFzho2T7e"
      },
      "source": [
        "# majority=colon_majority_df['Class']\n",
        "# colon_majority_df=colon_majority_df.drop('Class',axis=1)\n",
        "# colon_majority_df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkHzN_850l2R"
      },
      "source": [
        "# Different Clustering :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiAi-63-5Dp3"
      },
      "source": [
        "import pandas as pd\n",
        "columns=['Kmeans','Kmeans++','Minibatch']\n",
        "clustering_result=pd.DataFrame(columns=columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NVKVBWeyIpy"
      },
      "source": [
        "num_of_clusters=7"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsHbq5AoE4xp"
      },
      "source": [
        "##**KMEANS CLUSTERING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "kS52Leu52tRW",
        "outputId": "989c7c71-bcc3-47b0-fc49-fb05d49ce395"
      },
      "source": [
        "# Elbow method to find optimal K\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "sse={}\n",
        "for k in range(2,10):\n",
        "  kmeans=KMeans(n_clusters=k,init='random',max_iter=500).fit(colon_majority_df)\n",
        "  sse[k]=kmeans.inertia_\n",
        "plt.plot(list(sse.keys()),list(sse.values()),'bx-')\n",
        "plt.xlabel(\"Number of Cluster\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.savefig(\"elbow_method.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NmXUH7U1zPVnSkOy9JF0m6U1JsyUN75wQDeCII2DQIA9Wm1nnae8R02nAS9n7Y0lVVrcEvgqcl19Y1tIvfwkHHgh33w3PPpvaGhrg3HOLjcvMuq72EsTSiCgVejgMuDIi3oiIPwF98g3Nyu26K9x8M/TqlXoRDQ0wdmxqNzPLQ3sJYpmkjSStAewH/Kns2Jr5hWUtjR4N112XFs9dfDEcdVSq9jp6dNGRmVlX1V6C+D7QSHrMdEtEPAUgaW9gTlsXZuMV8yVV3C5U0j7ZeMas7PX9smMHSXpO0guSvrUiv1BXNno0jB8PS5fCW2+laq9mZnlpL0G8BuwObBcRJ0v6jKSbgeOAce1cewVwUDvnPBARO2evswEk9QTOBw4GhgHHShrWzn26hYaGtGDuK1+BCDj00NSrMDPLQ3sJ4mLgrYhYJGkUcA5wJSlx/LqtCyPifmBl1v6OBF6IiDkRsQS4BjhiJe7TpZTGHKZMgV/8Am68EST41KfgUq9IMbMctJcgekZE6Y/8p4CJETE1Is4Etu6A799d0uOSbpf04axtEPCPsnPmZm0VSRonqVFS44IFCzogpOo0fXrzMYePfQxuuQWGDIGTT4af/azY+Mys62k3QUgqLabbD7in7Fibi+yWw0xg84jYCfgNK1kdNiImRkRdRNQNHNh1q3+cccYHB6QPPhieeCL1Is44A7797fToycysI7T3R34ycJ+k14F3gAcAJG0NvLkqXxwRi8ve3ybpAknrA/OATctO3SRrswpWWy2NS/TrB+ecA4sWpZIcPXsWHZmZ1br2Sm38T7YPxEbAXRH//fdpD6B+Vb5Y0oeA1yIiJI3M7vkG8C9giKQtSInhGODTq/JdXV3Pnmlr0v79U5L417/gyitT8jAzW1ntPiaKiIcrtP21veskTQb2AdaXNBc4C+idXX8R8EngFElLSb2TY7IEtFTSBOBOoCdwWWl6rbVOgp/8JCWJb34TFi+G66+HtdYqOjIzq1WKLvTQuq6uLhobG4sOo3CXXAJf/CJ89KPwhz+kx09mZpVImhERdZWOtTdIbTXo5JPh2mvh0Udhn33gtdeKjsjMapETRBd19NFw663w/POw557w0ktFR2RmtcYJogs78ED405/g9ddTknj66aIjMrNa4gTRxe2+O9x3X6rfNGpUWnBnZrY8nCC6gR13hAcfhL59Yd99U9kOM7P2OEF0E1tvnZLEZpulFdg331x0RGZW7ZwgupFBg+D++2GnneATn0iL6czMWuME0c0MGJAGrvfeG048Ec7zxrFm1goniG6ob1/44x/hyCPhtNPghz90kT8z+yAniG5qjTXSZkOf/Sz84Adw+umwbFnRUZlZNVnVkt1Ww3r1gkmTUimOX/0qFfmbNCm1m5n5T0E316NH2qFuwAA488yUJK69NvUwzKx78yMmQ4LvfQ9+85u0S90hh6RqsGbWvTlB2H9NmABXXZWmwu63XyrRYWbdlxOENXPccXDjjfDkk7DXXjB3btERmVlRcksQki6TNF/Sk60cP07SbElPSPqLpJ3Kjr2Utc+S5A0eOtnHPgZ33AHz5sEee6SKsGbW/eTZg7gCOKiN4y8Ce0fEDsCPgIktjo+OiJ1b28jC8rX33nDvvfD226kS7KxZRUdkZp0ttwQREfcDC9s4/peIWJR9fBjYJK9YbOWMGAEPPACrr542HnrwwaIjMrPOVC1jECcBt5d9DuAuSTMkjWvrQknjJDVKalywYEGuQXZH226bEsOGG8KYMXD77e1fY2ZdQ+EJQtJoUoL4ZlnznhExAjgYOFXSqNauj4iJEVEXEXUDBw7MOdruabPNUk9i223h8MPTOgkz6/oKTRCSdgQuBY6IiDdK7RExL/s5H7gRGFlMhFaywQZpH4ndd4djj4WLLy46IjPLW2EJQtJmwA3ACRHx17L2PpL6lt4DY4CKM6Gsc627bprddPDBMH48nHNO0RGZWZ5yK7UhaTKwD7C+pLnAWUBvgIi4CPg+MAC4QBLA0mzG0obAjVlbL+DqiLgjrzhtxay1Ftx0UyoV/u1vw8KF8NOfptXYZta15JYgIuLYdo5/AfhChfY5wE4fvMKqRe/eacV1v37ws5+lJHHxxdCzZ9GRmVlHcrE+Wyk9esD558N668H//A+8+WZKGquvXnRkZtZRnCBspUnw4x9D//7w9a+nAn833AB9+hQdmZl1hMKnuVrt+9rX0j4Sf/oTHHAALFrU/jVmVv2cIKxDfP7zaYe6GTNSmY5XXy06IjNbVU4Q1mGOOirtdT1nDmy/PVx9dfPjDQ1w7rnFxGZmK84JwjrU/vvDtGmwZAmccAJcfnlqb2iAsWNh112Ljc/Mlp8ThHW43XaDhx5K02BPOiktrPvEJ1KJjtGji47OzJaXE4TlYvvtYfp0WH/9tPp60SI4/vj0uvxy+Pvfi47QzNrjBGG5+fvfISJtZbr22jB0KNx1VxrQHjwYtt4axo2Da66B+fOLjtbMWvI6CMtFacxhypT0WOmoo9Lna69Nhf+mTYN77kmfL7kkXbP99mkv7H33hVGj0iMqMyuOIqLoGDpMXV1dNDZ6h9JqcO65aUC6fMyhoSE9djrjjKa2pUth5syULO65J+098c47aaV2XV1KFvvum7Y+XWutzv89zLo6STNa27nTCcKqyrvvwsMPp2QxbRo88khKIqutlkqNl3oYI0emmlBmtmqcIKxmvfVW2qyo1MN47LE0rtGnT3oMVeph7Lxz6nWY2YpxgrAu44034L77mnoYzz6b2tdbL+2bve++qZcxdKhLkJstDycI67JeeSWNbUybll4vv5zaN964qXex776w+ebFxmlWrZwgrFuIgBdfbJohdc89TdNnt9qqqXcxenSaSVWyvAPqZl1RWwki16e2ki6TNF9SxS1DlZwn6QVJsyWNKDt2oqTns9eJecZpXYMEW24JJ58MkyfDP/8JTzwBv/41fPjDaUrtMcfAhhvCDjvA6afDLbfAdtulKbgNDek+LgtiluTag5A0CngLuDIitq9w/BCgHjgE2A34dUTsJmk9oBGoAwKYAewSEW0WknYPwtqydGka5C71MMqn1G6zTVrYN3ZsKjhYWr9h1tUV1oOIiPuBhW2ccgQpeUREPAz0k7QRcCBwd0QszJLC3cBBecZqXV+vXqlX8K1vpRXdixbBvffC974HAwbAf/4Dv/1tmiHlnfHMii+1MQj4R9nnuVlba+0fIGmcpEZJjQsWLMgtUOt6Vl897V3xwx/Cj36UZkIddFAa6N5jDzjsMJg1q+gozYpTdIJYZRExMSLqIqJu4MCBRYdjNag05nDddXD77fCHP6RexL33wvDhadziueeKjtKs8xWdIOYBm5Z93iRra63drMNNn958zOGQQ+DWW+Eb34DvfjcljGHDUulyV6G17iT3aa6SBgN/aGWQ+lBgAk2D1OdFxMhskHoGUJrVNJM0SN3WeIYHqS0X8+fDT34CF1yQPo8fD9/5TpoNZVbripzmOhl4CBgqaa6kkySNlzQ+O+U2YA7wAnAJ8CWALBH8CJievc5uLzmY5WWDDeCXv4Tnn4fPfAbOPz9Np/3ud9NAt1lX5YVyZivo+efhrLPSWot+/dKjqC9/Oe15YVZrCutBmHVFQ4bA1VenGU577ZV6ElttBeedl6rRmnUVThBmK2mnndJK7L/8JQ1in3ZaSh6XXZYW5ZnVOicIs1W0++5pZfbdd8NGG6XZTqXSHsuWFR2d2cpzgjDrABLsv3/a7Oimm9IGR8ccAyNGpNIdXWioz7oRJwizDiTBEUek8YmrrkobHh12WFqZfe+9RUdntmKcIMxy0LMnHHccPPMMXHxxKt8xejSMGZMW5pnVAicIsxz17g3jxqWpsT//OcycmfbTPuooeOqpoqMza5sThFknWHNN+OpXYc6cVBxw2rS0J8UJJ8Df/lZ0dGaVOUGYdaJ11oHvfz8lim98A6ZOhW23hVNOgXmuNmZVxgnCrAADBsBPfwovvJAeQU2aBFtvDV//Orz+etHRmSVOEGYF2njjVNvpuefgU59KNZ+23BJ+8ANYvLjo6Ky7c4IwqwJbbAFXXJH20B4zJo1TbLkl/O//pm1RzYrgBGFWRYYNg+uvh8bGtD3qN76RHj1deCEsWVJ0dNbdOEGYVaFddkm72913X+pJfOlLaTD7d7+Dc85Ju+CVa2iAc88tJlbrupwgzKrYqFFw//1w222ptPhnPgMXXQRHHpnqP0HTlqm77lpsrNb1OEGYVTkJDj44PXa67rq0pmLxYjjwQDjgAPj4x1MF2dKWqWYdJdcNgyQdBPwa6AlcGhHntDj+S6D0P+u1gA0iol927H3giezYyxFxeHvf5w2DrDtYujTVefra12Bh2T6LW2yRigOWXsOHe1tUa19bGwb1yvFLewLnAwcAc4Hpkm6JiKdL50TEV8rOrweGl93inYjYOa/4zGpVr16w+ebQo0danT1pEhx9NLz5ZirlMXVq07kbb9w8YYwYAZtumnolZu3JLUEAI4EXImIOgKRrgCOAp1s5/1jgrBzjMesSSmMOU6akx0qHHdb885tvpmqyM2c2vW67rWlvigEDmieMESPSjng9/MDZWsgzQQwC/lH2eS6wW6UTJW0ObAHcU9a8hqRGYClwTkTc1Mq144BxAJtttlkHhG1W3aZPb0oGkH5OmZLaR4+GddeFvfdOr5K334bZs5sSxmOPpUV5772XjvftCzvv3PwR1bbbpt6KdV+5jUFI+iRwUER8Ift8ArBbREyocO43gU0ior6sbVBEzJO0JSlx7BcRbZY18xiE2fJbsiRVlC0ljJkzU8+jtDBvjTVgxx2bP6LafvvUbl1HIWMQwDxg07LPm2RtlRwDnFreEBHzsp9zJN1LGp9w3UuzDrLaaumP/vCykb/3309lP0oJY+ZMmDw5Ta2F1KP48IebP6LaaSdYe+1ifgfLV549iF7AX4H9SIlhOvDpiHiqxXnbAncAW0QWjKT+wNsR8a6k9YGHgCPKB7grcQ/CrONFwIsvNh/TmDkTFixIxyUYOrT5mMbw4dC/f1q8t+uuzafgNjSkx2FnnFHM72PNFdKDiIilkiYAd5KmuV4WEU9JOhtojIhbslOPAa6J5plqO+BiSctIazXOaS85mFk+pLSae8st4ZOfTG0R8MorzR9PPfhg6m2UDB6cZkz98Idw5pnw2c+mHfZKA+pW/XJdB9HZ3IMwK9brrzd/PDVzZippXtKjR+pR7L9/U49j8GBPuy1SWz0IJwgzy9Wbb8KXvwxXXpkGvZctSz2J999Px/v1a5pBVUoaQ4emfb0tf0UNUpuZ/Xcdxplnpqq0U6bARz4CTz7Z9IjqscfgggvgP/9J16y5ZkompYThGVTFcIIws9y0XNQ3enTzz+UFBpcuhWefbXpE9dhjcPXVzWdQDRvWNPOqNINqnXWK+d26Az9iMrPcrOospmXL0gyqUi+jlDhee63pnK23bupllF4bbNDxv0tX5TEIM+tSXn21+eOpmTPhpZeajg8a1Pzx1PDhsNlmHgyvxGMQZtalbLQRHHpoepUsWtRUg6qUOMprUK23XvPHU8OHw5AhzQfDvW6jOScIM+sS+vdvGucoKdWgKn88dd55Tdu39umTxjFKiaNfv+ZjJOVjKN2RHzGZWbfy3ntpmm3LGlRvvZWOl3oUW20FL7+cSqnvtlvaW6P8tc46XeORlccgzMzasGwZ/O1vTUnj+uvT57XWSlNvS4+pyq2xxgeTRsvXhz6Ufq67bvUmE49BmJm1oUePNB4xZEiaATVpUtO6jZtvhh12SDOnWr7++c/08+WX0zjFggVNCwDLrbba8iWSDTdMj8qWJ5l0xniJE4SZWaa9dRvtWbYM3nijchIpvV55JfVS5s9Paz9a6t07Jan2Esk22+Q/XuIEYWaWaW8zpvb06AEDB6bX9tu3fe6yZWnmVWuJpPR64on0s7S5U8vv23//NK33nXeWP5EtL49BmJlVuQj4178qJ5HbbkuD7GeeCWefveL39hiEmVkNk9LYRP/+sN12Te0NDTBxYtN4SctpvqvK25SbmdWg8jGHs89OP8eOTe0dJdcEIekgSc9JekHStyoc/6ykBZJmZa8vlB07UdLz2evEPIPLodcAAAi/SURBVOM0M6s1bY2XdJQ8txztSdpy9ABgLmnL0WPLd4aT9FmgLiImtLh2PaARqAMCmAHsEhGL2vpOj0GYma2YtsYg8uxBjAReiIg5EbEEuAY4YjmvPRC4OyIWZknhbuCgnOI0M7MK8kwQg4B/lH2em7W19AlJsyVdL2nTFbzWzMxyUvQg9a3A4IjYkdRL+O2K3kDSOEmNkhoXLFjQ4QGamXVXeSaIecCmZZ83ydr+KyLeiIh3s4+XArss77Vl95gYEXURUTdw4MAOCdzMzPJNENOBIZK2kLQacAxwS/kJkjYq+3g48Ez2/k5gjKT+kvoDY7I2MzPrJLktlIuIpZImkP6w9wQui4inJJ0NNEbELcCXJR0OLAUWAp/Nrl0o6UekJANwdkQsbO87Z8yY8bqkv69kyOsDr6/ktZ2tlmKF2oq3lmKF2oq3lmKF2op3VWLdvLUDXarUxqqQ1NjaVK9qU0uxQm3FW0uxQm3FW0uxQm3Fm1esRQ9Sm5lZlXKCMDOzipwgmkwsOoAVUEuxQm3FW0uxQm3FW0uxQm3Fm0usHoMwM7OK3IMwM7OKnCDMzKyibp0gJG0qqUHS05KeknRa0TG1RdIakh6V9HgW7w+Ljqk9knpKekzSH4qOpT2SXpL0RFZ6vqrLAkvql9Uve1bSM5J2Lzqm1kgaWlbSf5akxZJOLzqu1kj6Svbf15OSJktao+iY2iLptCzWpzr6/67degwiW8m9UUTMlNSXVFb8yPKS5NVEkoA+EfGWpN7Ag8BpEfFwwaG1StJXSWXb14mIw4qOpy2SXiKVn6/6xVGSfgs8EBGXZpUK1oqIfxUdV3uybQDmAbtFxMouas2NpEGk/66GRcQ7kqYAt0XEFcVGVpmk7UmVskcCS4A7gPER8UJH3L9b9yAi4tWImJm9/zep1EfVVo2N5K3sY+/sVbUZXtImwKGkOlvWQSStC4wCJgFExJJaSA6Z/YC/VWNyKNMLWFNSL2At4JWC42nLdsAjEfF2RCwF7gOO6qibd+sEUU7SYGA48EixkbQte2QzC5hP2jOjmuP9FXAGsKzoQJZTAHdJmiFpXNHBtGELYAFwefb47lJJfYoOajkdA0wuOojWRMQ84H+Bl4FXgTcj4q5io2rTk8BekgZIWgs4hOaFTleJEwQgaW1gKnB6RCwuOp62RMT7EbEzqcLtyKyLWXUkHQbMj4gZRceyAvaMiBHAwcCpkkYVHVAregEjgAsjYjjwf8AHtvStNtmjsMOB64qOpTVZcdAjSEl4Y6CPpOOLjap1EfEM8FPgLtLjpVnA+x11/26fILJn+VOB30fEDUXHs7yyRwoNVO9Oe3sAh2fP9a8B9pV0VbEhtS371yMRMR+4kfRctxrNBeaW9R6vJyWMancwMDMiXis6kDbsD7wYEQsi4j3gBuCjBcfUpoiYFBG7RMQoYBFpq+cO0a0TRDboOwl4JiJ+UXQ87ZE0UFK/7P2apP2+ny02qsoi4tsRsUlEDCY9VrgnIqr2X2KS+mQTFcge14whdd+rTkT8E/iHpKFZ035AVU6saOFYqvjxUuZl4COS1sr+PuxH0zYEVUnSBtnPzUjjD1d31L1zK/ddI/YATgCeyJ7rA3wnIm4rMKa2bAT8NpsJ0gOYEhFVP320RmwI3Jj+JtALuDoi7ig2pDbVA7/PHtvMAT5XcDxtypLuAcAXi46lLRHxiKTrgZmkbQgeo/pLbkyVNAB4Dzi1IycsdOtprmZm1rpu/YjJzMxa5wRhZmYVOUGYmVlFThBmZlaRE4SZmVXkBGE1R1JI+nnZ569L+kEH3fsKSZ/siHu18z1HZ1VYGyoc20bSbZKelzRT0hRJG0raZ2Wr4ko6PSvFYLbcnCCsFr0LHCVp/aIDKZcVd1teJwEnR8ToFvdYA/gjqYzGkKz0xwXAwFUM73RS4bnllq23sW7MCcJq0VLS4qWvtDzQsgcg6a3s5z6S7pN0s6Q5ks6RdFy2v8YTkrYqu83+khol/TWrKVUqkvgzSdMlzZb0xbL7PiDpFiqsZpZ0bHb/JyX9NGv7PrAnMEnSz1pc8mngoYi4tdQQEfdGRLNV3ZJ+IOnrZZ+flDQ4WxH+R6U9Q56U9ClJXybVFWoo9VgkjZH0UNZDuS6rR1baE+OnkmYCR7fz/wfr4rr7SmqrXecDsyWduwLX7EQqj7yQtPr40ogYqbRRVD3pX9kAg0l1mLYi/VHdGvgMqbLnrpJWB/4sqVTlcwSwfUS8WP5lkjYmFVLbhVQj5y5JR0bE2ZL2Bb4eES03JtqetC/JyjoIeCUiDs1iWDci3lTal2N0RLye9by+B+wfEf8n6ZvAV4Gzs3u8kfVcrJtzD8JqUlZ190rgyytw2fRsD5B3gb+RKmACPEFKCiVTImJZRDxPSiTbkmozfSYryfIIMAAYkp3/aMvkkNkVuDcr/LYU+D1pH4c8PQEckPUC9oqINyuc8xFgGCnJzQJOBDYvO35tzjFajXAPwmrZr0g1cy4va1tK9g8fST2A1cqOvVv2flnZ52U0/2+hZf2ZAATUR8Sd5Qck7UMqt91RngL2Xo7z/vt7ZtYAiIi/ShpB2hfgx5KmRcTZLa4VaS+RY1u5d0f+PlbD3IOwmhURC4EppAHfkpdIj3Qg7T3QeyVufbSkHtm4xJbAc8CdwClZefjSTKP2Nul5FNhb0vrZgO+xpB2/2nI18FFJh5YaJI3SB/f9eImsxHeWELbI3m8MvB0RVwE/o6kM+L+Bvtn7h4E9skdnpUq227QTl3VDThBW634OlM9muoT0R/lxYHdW7l/DL5P+uN9O2t/3P6RtU58GZkp6EriYdnrgEfEqaSOfBuBxYEZE3NzONe8AhwH12TTXp4EvkXaQKzcVWE/SU8AEmvYA2AF4NHt0dBbw46x9InCHpIaIWAB8FpgsaTbwEOkxmlkzruZqZmYVuQdhZmYVOUGYmVlFThBmZlaRE4SZmVXkBGFmZhU5QZiZWUVOEGZmVtH/Byhd5hy8QmmGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PaQfXiy00IC"
      },
      "source": [
        "#Kmeans:\n",
        "kmeans=KMeans(n_clusters=num_of_clusters,init='random',max_iter=500).fit(colon_majority_df)\n",
        "clustering_result['Kmeans']=kmeans.labels_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVhKXIDJKZAA"
      },
      "source": [
        "#import seaborn as sns\n",
        "#sns.lmplot(x='Top10perc',y='S.F.Ratio',data = colon_majority_df,hue='labels',fit_reg=False)\n",
        "#sns.pairplot(colon_majority_df[:0],hue='labels')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "NgNNkrxMLn8Q",
        "outputId": "221abb30-99b6-4f07-acfe-a05e381c5691"
      },
      "source": [
        "partition1 = colon_majority_df\n",
        "partition1=partition1.reset_index(drop=True)\n",
        "partition1['cluster_label'] = clustering_result['Kmeans']\n",
        "partition1 #with cluster labels "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              "0              1       85             66  ...   31        0              2\n",
              "1              1       89             66  ...   21        0              3\n",
              "2              5      116             74  ...   30        0              4\n",
              "3             10      115              0  ...   29        0              5\n",
              "4              4      110             92  ...   30        0              2\n",
              "..           ...      ...            ...  ...  ...      ...            ...\n",
              "495            9       89             62  ...   33        0              2\n",
              "496           10      101             76  ...   63        0              1\n",
              "497            2      122             70  ...   27        0              4\n",
              "498            5      121             72  ...   30        0              1\n",
              "499            1       93             70  ...   23        0              2\n",
              "\n",
              "[500 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1414
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOpcquEM8hJ8"
      },
      "source": [
        "# **CLUSTER VISUALIZATION USING PCA FOR REDUCING DIMENSIONS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xz9DBR5xmzQ"
      },
      "source": [
        "import plotly as py \n",
        "import plotly.graph_objs as go \n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BoJarSW2Frx"
      },
      "source": [
        "PCA is an algorithm that is used for dimensionality reduction - meaning, informally, that it can take in a DataFrame with many columns and return a DataFrame with a reduced number of columns that still retains much of the information from the columns of the original DataFrame. The columns of the DataFrame produced from the PCA procedure are called Principal Components. We will use these principal components to help us visualize our clusters in 2-D, and 3-D space, since we cannot easily visualize the data we have in higher dimensions. For example, we can use two principal components to visualize the clusters in 2-D space, or three principal components to visualize the clusters in 3-D space.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GE8D46Ne1Bjj"
      },
      "source": [
        "from sklearn.decomposition import PCA #Principal Component Analysis\n",
        "\n",
        "#PCA with two principal components\n",
        "pca_2d = PCA(n_components=2)\n",
        "\n",
        "#PCA with three principal components\n",
        "pca_3d = PCA(n_components=3)\n",
        "\n",
        "#This DataFrame contains the two principal components that will be used\n",
        "#for the 2-D visualization mentioned above\n",
        "PCs_2d = pd.DataFrame(pca_2d.fit_transform(partition1.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#And this DataFrame contains three principal components that will aid us\n",
        "#in visualizing our clusters in 3-D\n",
        "PCs_3d = pd.DataFrame(pca_3d.fit_transform(partition1.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#\"PC1_2d\" means: 'The first principal component of the components created for 2-D visualization, by PCA.'\n",
        "#And \"PC2_2d\" means: 'The second principal component of the components created for 2-D visualization, by PCA.'\n",
        "PCs_2d.columns = [\"PC1_2d\", \"PC2_2d\"]\n",
        "\n",
        "PCs_3d.columns = [\"PC1_3d\", \"PC2_3d\", \"PC3_3d\"]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IerigAV2g93"
      },
      "source": [
        "partition1_plot= pd.concat([partition1,PCs_2d,PCs_3d], axis=1, join='inner')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O733h3D59BRl"
      },
      "source": [
        "### **CLUSTER VISUALIZATION IN TWO DIMENSIONAL SPACE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "HGE88cYEyxlK",
        "outputId": "780c019f-8823-43bd-8392-1f4bce705deb"
      },
      "source": [
        "data=[]\n",
        "for i in partition1_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition1_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter(x = cluster_i[\"PC1_2d\"],y = cluster_i[\"PC2_2d\"],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Two Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Visualizing Clusters in Two Dimensions Using PCA\"}, \"xaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"PC1\"}, \"zeroline\": false}, \"yaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"PC2\"}, \"zeroline\": false}},\n",
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFM1VD3r9Njy"
      },
      "source": [
        "### **CLUSTER VISUALIZATION IN THREE DIMENSIONAL SPACE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "qTljPyTg7SpV",
        "outputId": "9ff02bf8-8bc7-467b-9653-5577f9991ef8"
      },
      "source": [
        "data=[]\n",
        "for i in partition1_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition1_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter3d(x = cluster_i[\"PC1_3d\"],y = cluster_i[\"PC2_3d\"],z=cluster_i['PC3_3d'],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Three Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Visualizing Clusters in Three Dimensions Using PCA\"}, \"xaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"PC1\"}, \"zeroline\": false}, \"yaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"PC2\"}, \"zeroline\": false}},\n",
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          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LzE-ccvYvP0"
      },
      "source": [
        "## **KMEANS++ CLUSTERING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEjsUVMeYuku"
      },
      "source": [
        "#Kmeans++:\n",
        "kmeans=KMeans(n_clusters=num_of_clusters,init='k-means++',max_iter=500).fit(colon_majority_df)\n",
        "clustering_result['Kmeans++']=kmeans.labels_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "cdq5gnTkZUSA",
        "outputId": "6df4a239-c494-4adf-92e9-78d23d51665c"
      },
      "source": [
        "partition2 = colon_majority_df\n",
        "partition2=partition2.reset_index(drop=True)\n",
        "partition2['cluster_label'] = clustering_result['Kmeans++']\n",
        "partition2 #with cluster labels "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
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              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
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              "      <td>0</td>\n",
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              "      <td>29</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>9</td>\n",
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              "      <td>22.5</td>\n",
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              "      <td>33</td>\n",
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              "    <tr>\n",
              "      <th>496</th>\n",
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              "      <td>76</td>\n",
              "      <td>48</td>\n",
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              "      <td>0.171</td>\n",
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              "     Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              "0              1       85             66  ...   31        0              1\n",
              "1              1       89             66  ...   21        0              5\n",
              "2              5      116             74  ...   30        0              4\n",
              "3             10      115              0  ...   29        0              1\n",
              "4              4      110             92  ...   30        0              4\n",
              "..           ...      ...            ...  ...  ...      ...            ...\n",
              "495            9       89             62  ...   33        0              1\n",
              "496           10      101             76  ...   63        0              0\n",
              "497            2      122             70  ...   27        0              4\n",
              "498            5      121             72  ...   30        0              3\n",
              "499            1       93             70  ...   23        0              1\n",
              "\n",
              "[500 rows x 10 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 1421
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNJVgTDfZ4c0"
      },
      "source": [
        "#PCA with two principal components\n",
        "pca_2d = PCA(n_components=2)\n",
        "\n",
        "#PCA with three principal components\n",
        "pca_3d = PCA(n_components=3)\n",
        "\n",
        "#This DataFrame contains the two principal components that will be used\n",
        "#for the 2-D visualization mentioned above\n",
        "PCs_2d = pd.DataFrame(pca_2d.fit_transform(partition2.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#And this DataFrame contains three principal components that will aid us\n",
        "#in visualizing our clusters in 3-D\n",
        "PCs_3d = pd.DataFrame(pca_3d.fit_transform(partition2.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#\"PC1_2d\" means: 'The first principal component of the components created for 2-D visualization, by PCA.'\n",
        "#And \"PC2_2d\" means: 'The second principal component of the components created for 2-D visualization, by PCA.'\n",
        "PCs_2d.columns = [\"PC1_2d\", \"PC2_2d\"]\n",
        "\n",
        "PCs_3d.columns = [\"PC1_3d\", \"PC2_3d\", \"PC3_3d\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVVy7u8DaEXh"
      },
      "source": [
        "partition2_plot= pd.concat([partition2,PCs_2d,PCs_3d], axis=1, join='inner')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "GiLgYRMHaTc3",
        "outputId": "c62f6f3b-351c-468c-9326-40082a185b9d"
      },
      "source": [
        "data=[]\n",
        "for i in partition2_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition1_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter(x = cluster_i[\"PC1_2d\"],y = cluster_i[\"PC2_2d\"],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Two Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)\n"
      ],
      "execution_count": null,
      "outputs": [
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              "                    Plotly.newPlot(\n",
              "                        '5a20049c-89de-4638-84d5-16ba0278babc',\n",
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9ffu4DLjap03",
        "outputId": "3978c93c-a409-49d2-a05f-c326e42989a4"
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      "source": [
        "data=[]\n",
        "for i in partition2_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition2_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter3d(x = cluster_i[\"PC1_3d\"],y = cluster_i[\"PC2_3d\"],z=cluster_i['PC3_3d'],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Three Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "                        [{\"mode\": \"markers\", \"name\": \"Cluster 1\", \"type\": \"scatter3d\", \"x\": [-70.25698206044837, -69.94590499863835, -71.0948947126222, -71.52068506907095, -71.54243686358978, -47.67308754228528, -72.76932542004705, -73.53525406903313, -76.29154057183477, -68.87036754067496, -70.38734480513108, -79.32858883867532, -74.2212641559344, -68.65852084992747, -67.96012426006578, -68.77931176345056, -74.53473224636909, -68.6470504667912, -66.62207037565666, -68.57480746316465, -72.71017544293083, -69.47734092038223, -71.76034074581545, -52.33153707708017, -72.77693547068687, -72.80143905621689, -69.37132011015868, -66.85575252539944, -70.03653046224716, -69.1886799861474, -72.37196721706873, -70.2939787622693, -68.96135593806699, -69.48607418134151, -71.28746619393776, -72.00279581428711, -71.76839034518257, -56.026302799773035, -73.28455289686441, -69.49648264216299, -71.24364734784298, -70.104197411434, -68.52872726116603, -67.67708218765272, 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        "outputId": "e5121214-3847-45e1-9adb-cfd0d05fba6a"
      },
      "source": [
        "!pip install kmodes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kmodes in /usr/local/lib/python3.7/dist-packages (0.11.0)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.7/dist-packages (from kmodes) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.19.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Etg8swfMDU3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a82d830d-b92a-42b8-fe36-697fa021bbd4"
      },
      "source": [
        "from kmodes.kmodes import KModes\n",
        "\n",
        "\n",
        "km = KModes(n_clusters=num_of_clusters, init='Huang', n_init=5, verbose=1)\n",
        "\n",
        "clusters = km.fit_predict(colon_majority_df)\n",
        "\n",
        "df_0_centroids=km.cluster_centroids_\n",
        "clusters"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 1, iteration: 1/100, moves: 83, cost: 3033.0\n",
            "Run 1, iteration: 2/100, moves: 8, cost: 3032.0\n",
            "Run 1, iteration: 3/100, moves: 0, cost: 3032.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 2, iteration: 1/100, moves: 91, cost: 3022.0\n",
            "Run 2, iteration: 2/100, moves: 2, cost: 3022.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 3, iteration: 1/100, moves: 200, cost: 3027.0\n",
            "Run 3, iteration: 2/100, moves: 25, cost: 3026.0\n",
            "Run 3, iteration: 3/100, moves: 0, cost: 3026.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 4, iteration: 1/100, moves: 60, cost: 3075.0\n",
            "Run 4, iteration: 2/100, moves: 1, cost: 3075.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 5, iteration: 1/100, moves: 106, cost: 3042.0\n",
            "Run 5, iteration: 2/100, moves: 1, cost: 3042.0\n",
            "Best run was number 2\n"
          ],
          "name": "stdout"
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        "outputId": "69bb20b0-beac-486a-f84e-b35e3436a545"
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      "source": [
        "partition3 = colon_majority_df\n",
        "partition3=partition3.reset_index(drop=True)\n",
        "partition3['cluster_label'] = clusters\n",
        "partition3 #with cluster labels "
      ],
      "execution_count": null,
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              "0              1       85             66  ...   31        0              0\n",
              "1              1       89             66  ...   21        0              0\n",
              "2              5      116             74  ...   30        0              3\n",
              "3             10      115              0  ...   29        0              4\n",
              "4              4      110             92  ...   30        0              0\n",
              "..           ...      ...            ...  ...  ...      ...            ...\n",
              "495            9       89             62  ...   33        0              0\n",
              "496           10      101             76  ...   63        0              5\n",
              "497            2      122             70  ...   27        0              2\n",
              "498            5      121             72  ...   30        0              0\n",
              "499            1       93             70  ...   23        0              6\n",
              "\n",
              "[500 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1428
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvIFwTLn-c7I"
      },
      "source": [
        "## **K-prototypes Clustering**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZ-AfOlZ-kq8",
        "outputId": "e52212ef-93be-4ac0-9560-e3cc92fde60d"
      },
      "source": [
        "from kmodes.kprototypes import KPrototypes\n",
        "\n",
        "\n",
        "kproto = KPrototypes(n_clusters=num_of_clusters, init='Cao', verbose=2)\n",
        "clusters = kproto.fit_predict(colon_majority_df, categorical=[1])\n",
        "\n",
        "\n",
        "# Print cluster centroids of the trained model.\n",
        "clusters\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 1, iteration: 1/100, moves: 131, ncost: 706563.3286852823\n",
            "Run: 1, iteration: 2/100, moves: 71, ncost: 595595.0391543658\n",
            "Run: 1, iteration: 3/100, moves: 30, ncost: 548546.2078965778\n",
            "Run: 1, iteration: 4/100, moves: 18, ncost: 538679.8886348833\n",
            "Run: 1, iteration: 5/100, moves: 8, ncost: 537848.5052034097\n",
            "Run: 1, iteration: 6/100, moves: 16, ncost: 535041.3053692122\n",
            "Run: 1, iteration: 7/100, moves: 16, ncost: 531142.9555560171\n",
            "Run: 1, iteration: 8/100, moves: 21, ncost: 517075.04227268364\n",
            "Run: 1, iteration: 9/100, moves: 19, ncost: 504912.86775089026\n",
            "Run: 1, iteration: 10/100, moves: 21, ncost: 493277.73839160416\n",
            "Run: 1, iteration: 11/100, moves: 16, ncost: 489506.0380129793\n",
            "Run: 1, iteration: 12/100, moves: 11, ncost: 486995.98049768957\n",
            "Run: 1, iteration: 13/100, moves: 10, ncost: 485131.63393373304\n",
            "Run: 1, iteration: 14/100, moves: 1, ncost: 485085.3191514442\n",
            "Run: 1, iteration: 15/100, moves: 0, ncost: 485085.3191514442\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 2, iteration: 1/100, moves: 67, ncost: 580824.8810543307\n",
            "Run: 2, iteration: 2/100, moves: 38, ncost: 533410.6700228717\n",
            "Run: 2, iteration: 3/100, moves: 21, ncost: 516318.310021438\n",
            "Run: 2, iteration: 4/100, moves: 13, ncost: 513723.4328741932\n",
            "Run: 2, iteration: 5/100, moves: 9, ncost: 512696.17582774645\n",
            "Run: 2, iteration: 6/100, moves: 2, ncost: 512635.3507059357\n",
            "Run: 2, iteration: 7/100, moves: 1, ncost: 512621.7168089087\n",
            "Run: 2, iteration: 8/100, moves: 0, ncost: 512621.7168089087\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 3, iteration: 1/100, moves: 161, ncost: 1145772.876509232\n",
            "Run: 3, iteration: 2/100, moves: 91, ncost: 907078.603355475\n",
            "Run: 3, iteration: 3/100, moves: 55, ncost: 757083.1552052902\n",
            "Run: 3, iteration: 4/100, moves: 71, ncost: 666709.437527606\n",
            "Run: 3, iteration: 5/100, moves: 42, ncost: 588941.8919624651\n",
            "Run: 3, iteration: 6/100, moves: 19, ncost: 578590.3375535595\n",
            "Run: 3, iteration: 7/100, moves: 14, ncost: 546023.9015006586\n",
            "Run: 3, iteration: 8/100, moves: 17, ncost: 534388.0166187589\n",
            "Run: 3, iteration: 9/100, moves: 9, ncost: 531807.6385681961\n",
            "Run: 3, iteration: 10/100, moves: 6, ncost: 530114.510201643\n",
            "Run: 3, iteration: 11/100, moves: 9, ncost: 525802.6880141819\n",
            "Run: 3, iteration: 12/100, moves: 13, ncost: 512677.1599502987\n",
            "Run: 3, iteration: 13/100, moves: 12, ncost: 501191.20754320885\n",
            "Run: 3, iteration: 14/100, moves: 14, ncost: 490046.43157036975\n",
            "Run: 3, iteration: 15/100, moves: 6, ncost: 488313.36446377204\n",
            "Run: 3, iteration: 16/100, moves: 8, ncost: 485676.1813039689\n",
            "Run: 3, iteration: 17/100, moves: 4, ncost: 485081.8965515425\n",
            "Run: 3, iteration: 18/100, moves: 0, ncost: 485081.8965515425\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 4, iteration: 1/100, moves: 110, ncost: 665231.1400762754\n",
            "Run: 4, iteration: 2/100, moves: 43, ncost: 571053.2521199869\n",
            "Run: 4, iteration: 3/100, moves: 19, ncost: 537290.1524099463\n",
            "Run: 4, iteration: 4/100, moves: 11, ncost: 514486.99269522994\n",
            "Run: 4, iteration: 5/100, moves: 12, ncost: 494688.327889984\n",
            "Run: 4, iteration: 6/100, moves: 12, ncost: 490643.6481675273\n",
            "Run: 4, iteration: 7/100, moves: 8, ncost: 489424.5986011688\n",
            "Run: 4, iteration: 8/100, moves: 2, ncost: 489309.513113797\n",
            "Run: 4, iteration: 9/100, moves: 2, ncost: 489256.83820957487\n",
            "Run: 4, iteration: 10/100, moves: 0, ncost: 489256.83820957487\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 5, iteration: 1/100, moves: 98, ncost: 862126.1987347646\n",
            "Run: 5, iteration: 2/100, moves: 40, ncost: 720464.8322559302\n",
            "Run: 5, iteration: 3/100, moves: 33, ncost: 644223.1639347249\n",
            "Run: 5, iteration: 4/100, moves: 32, ncost: 609680.0935922185\n",
            "Run: 5, iteration: 5/100, moves: 32, ncost: 564492.9665457282\n",
            "Run: 5, iteration: 6/100, moves: 44, ncost: 533906.2587729739\n",
            "Run: 5, iteration: 7/100, moves: 38, ncost: 501066.4714138075\n",
            "Run: 5, iteration: 8/100, moves: 32, ncost: 470385.7369635715\n",
            "Run: 5, iteration: 9/100, moves: 21, ncost: 449146.6334579015\n",
            "Run: 5, iteration: 10/100, moves: 14, ncost: 443714.45454062807\n",
            "Run: 5, iteration: 11/100, moves: 7, ncost: 442377.74558387476\n",
            "Run: 5, iteration: 12/100, moves: 5, ncost: 441853.72645028715\n",
            "Run: 5, iteration: 13/100, moves: 2, ncost: 441747.63032172434\n",
            "Run: 5, iteration: 14/100, moves: 1, ncost: 441730.4213571542\n",
            "Run: 5, iteration: 15/100, moves: 0, ncost: 441730.4213571542\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 6, iteration: 1/100, moves: 79, ncost: 958785.8290384699\n",
            "Run: 6, iteration: 2/100, moves: 55, ncost: 814149.4259149459\n",
            "Run: 6, iteration: 3/100, moves: 56, ncost: 714222.1867820746\n",
            "Run: 6, iteration: 4/100, moves: 56, ncost: 633980.917044038\n",
            "Run: 6, iteration: 5/100, moves: 28, ncost: 604052.2864727668\n",
            "Run: 6, iteration: 6/100, moves: 25, ncost: 578552.0162053229\n",
            "Run: 6, iteration: 7/100, moves: 24, ncost: 552385.2686243757\n",
            "Run: 6, iteration: 8/100, moves: 18, ncost: 529060.0425477345\n",
            "Run: 6, iteration: 9/100, moves: 18, ncost: 523731.8730996956\n",
            "Run: 6, iteration: 10/100, moves: 24, ncost: 518156.275587359\n",
            "Run: 6, iteration: 11/100, moves: 6, ncost: 517453.27064992976\n",
            "Run: 6, iteration: 12/100, moves: 10, ncost: 516524.1313908757\n",
            "Run: 6, iteration: 13/100, moves: 9, ncost: 515769.41411330266\n",
            "Run: 6, iteration: 14/100, moves: 9, ncost: 514716.2426466333\n",
            "Run: 6, iteration: 15/100, moves: 8, ncost: 513748.9256264637\n",
            "Run: 6, iteration: 16/100, moves: 9, ncost: 513037.70666368556\n",
            "Run: 6, iteration: 17/100, moves: 1, ncost: 513023.9625109161\n",
            "Run: 6, iteration: 18/100, moves: 0, ncost: 513023.9625109161\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 7, iteration: 1/100, moves: 142, ncost: 820837.2464071916\n",
            "Run: 7, iteration: 2/100, moves: 87, ncost: 754866.1319234121\n",
            "Run: 7, iteration: 3/100, moves: 41, ncost: 723177.2817877847\n",
            "Run: 7, iteration: 4/100, moves: 15, ncost: 700064.8022431388\n",
            "Run: 7, iteration: 5/100, moves: 13, ncost: 683519.9026574263\n",
            "Run: 7, iteration: 6/100, moves: 9, ncost: 681176.4733947455\n",
            "Run: 7, iteration: 7/100, moves: 11, ncost: 675886.788278823\n",
            "Run: 7, iteration: 8/100, moves: 6, ncost: 674932.0827934847\n",
            "Run: 7, iteration: 9/100, moves: 9, ncost: 673784.2359899981\n",
            "Run: 7, iteration: 10/100, moves: 5, ncost: 673472.6535967005\n",
            "Run: 7, iteration: 11/100, moves: 5, ncost: 673167.4987368267\n",
            "Run: 7, iteration: 12/100, moves: 8, ncost: 672500.808818588\n",
            "Run: 7, iteration: 13/100, moves: 13, ncost: 670927.6627933917\n",
            "Run: 7, iteration: 14/100, moves: 29, ncost: 664152.0374228844\n",
            "Run: 7, iteration: 15/100, moves: 55, ncost: 626888.5777740981\n",
            "Run: 7, iteration: 16/100, moves: 60, ncost: 593686.6552490519\n",
            "Run: 7, iteration: 17/100, moves: 30, ncost: 555489.5143722498\n",
            "Run: 7, iteration: 18/100, moves: 14, ncost: 548181.8603645694\n",
            "Run: 7, iteration: 19/100, moves: 27, ncost: 539073.9592343394\n",
            "Run: 7, iteration: 20/100, moves: 15, ncost: 537227.2138789793\n",
            "Run: 7, iteration: 21/100, moves: 14, ncost: 534931.4294370584\n",
            "Run: 7, iteration: 22/100, moves: 16, ncost: 530269.0418877816\n",
            "Run: 7, iteration: 23/100, moves: 21, ncost: 516310.8970650871\n",
            "Run: 7, iteration: 24/100, moves: 25, ncost: 502113.0987690419\n",
            "Run: 7, iteration: 25/100, moves: 21, ncost: 490071.6856130582\n",
            "Run: 7, iteration: 26/100, moves: 8, ncost: 488313.36446377204\n",
            "Run: 7, iteration: 27/100, moves: 8, ncost: 485676.1813039689\n",
            "Run: 7, iteration: 28/100, moves: 4, ncost: 485081.8965515425\n",
            "Run: 7, iteration: 29/100, moves: 0, ncost: 485081.8965515425\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 8, iteration: 1/100, moves: 161, ncost: 750321.6722780609\n",
            "Run: 8, iteration: 2/100, moves: 69, ncost: 667256.758224828\n",
            "Run: 8, iteration: 3/100, moves: 46, ncost: 588959.4049401794\n",
            "Run: 8, iteration: 4/100, moves: 20, ncost: 578590.3375535595\n",
            "Run: 8, iteration: 5/100, moves: 14, ncost: 546023.9015006585\n",
            "Run: 8, iteration: 6/100, moves: 17, ncost: 534388.0166187589\n",
            "Run: 8, iteration: 7/100, moves: 9, ncost: 531807.638568196\n",
            "Run: 8, iteration: 8/100, moves: 6, ncost: 530114.5102016429\n",
            "Run: 8, iteration: 9/100, moves: 9, ncost: 525802.6880141819\n",
            "Run: 8, iteration: 10/100, moves: 13, ncost: 512677.15995029866\n",
            "Run: 8, iteration: 11/100, moves: 12, ncost: 501191.20754320896\n",
            "Run: 8, iteration: 12/100, moves: 14, ncost: 490046.43157036975\n",
            "Run: 8, iteration: 13/100, moves: 6, ncost: 488313.364463772\n",
            "Run: 8, iteration: 14/100, moves: 8, ncost: 485676.18130396877\n",
            "Run: 8, iteration: 15/100, moves: 4, ncost: 485081.8965515425\n",
            "Run: 8, iteration: 16/100, moves: 0, ncost: 485081.8965515425\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 9, iteration: 1/100, moves: 70, ncost: 624321.5451689106\n",
            "Run: 9, iteration: 2/100, moves: 35, ncost: 571875.3436527215\n",
            "Run: 9, iteration: 3/100, moves: 25, ncost: 535846.2993058955\n",
            "Run: 9, iteration: 4/100, moves: 14, ncost: 516051.7015742135\n",
            "Run: 9, iteration: 5/100, moves: 10, ncost: 514756.6474953007\n",
            "Run: 9, iteration: 6/100, moves: 6, ncost: 514186.1521528246\n",
            "Run: 9, iteration: 7/100, moves: 6, ncost: 513342.28929595544\n",
            "Run: 9, iteration: 8/100, moves: 4, ncost: 513023.962510916\n",
            "Run: 9, iteration: 9/100, moves: 0, ncost: 513023.962510916\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 10, iteration: 1/100, moves: 153, ncost: 991425.8978275978\n",
            "Run: 10, iteration: 2/100, moves: 89, ncost: 792229.9471797415\n",
            "Run: 10, iteration: 3/100, moves: 50, ncost: 728528.1466958054\n",
            "Run: 10, iteration: 4/100, moves: 44, ncost: 652824.7794126362\n",
            "Run: 10, iteration: 5/100, moves: 51, ncost: 602283.9786936461\n",
            "Run: 10, iteration: 6/100, moves: 70, ncost: 562763.7636722918\n",
            "Run: 10, iteration: 7/100, moves: 66, ncost: 518756.570563351\n",
            "Run: 10, iteration: 8/100, moves: 53, ncost: 473690.3794242836\n",
            "Run: 10, iteration: 9/100, moves: 27, ncost: 449146.6334579015\n",
            "Run: 10, iteration: 10/100, moves: 14, ncost: 443714.45454062807\n",
            "Run: 10, iteration: 11/100, moves: 7, ncost: 442377.74558387476\n",
            "Run: 10, iteration: 12/100, moves: 5, ncost: 441853.72645028715\n",
            "Run: 10, iteration: 13/100, moves: 2, ncost: 441747.63032172434\n",
            "Run: 10, iteration: 14/100, moves: 1, ncost: 441730.4213571542\n",
            "Run: 10, iteration: 15/100, moves: 0, ncost: 441730.4213571542\n",
            "Best run was number 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 5, 6, 2, 6, 6, 4, 1, 6, 5, 5, 6, 6, 4, 6, 6, 1, 6, 4, 6, 6, 6,\n",
              "       6, 6, 2, 4, 4, 6, 3, 6, 5, 6, 5, 2, 6, 5, 6, 6, 4, 5, 5, 3, 6, 6,\n",
              "       6, 6, 6, 6, 2, 4, 6, 5, 6, 4, 6, 6, 1, 4, 4, 1, 6, 4, 4, 6, 6, 4,\n",
              "       6, 1, 6, 5, 6, 4, 6, 6, 6, 4, 6, 5, 6, 5, 5, 6, 4, 5, 4, 5, 6, 3,\n",
              "       6, 6, 4, 3, 6, 6, 5, 6, 6, 1, 6, 0, 5, 5, 4, 6, 5, 3, 6, 6, 6, 6,\n",
              "       4, 2, 4, 4, 6, 6, 6, 5, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 4, 1, 6, 4,\n",
              "       6, 6, 6, 5, 2, 1, 4, 4, 6, 0, 4, 4, 6, 4, 6, 6, 4, 1, 6, 0, 3, 6,\n",
              "       6, 6, 4, 6, 6, 6, 3, 5, 6, 6, 4, 6, 6, 4, 6, 4, 6, 4, 5, 6, 3, 5,\n",
              "       1, 5, 0, 4, 4, 4, 6, 5, 1, 6, 4, 6, 5, 5, 6, 5, 4, 5, 4, 5, 1, 6,\n",
              "       1, 6, 4, 6, 4, 6, 4, 3, 2, 5, 4, 6, 6, 6, 5, 4, 2, 4, 6, 6, 6, 4,\n",
              "       6, 4, 6, 6, 3, 4, 6, 4, 4, 4, 5, 1, 4, 4, 4, 4, 6, 1, 4, 5, 4, 4,\n",
              "       1, 3, 4, 3, 5, 6, 6, 6, 1, 6, 6, 1, 3, 4, 6, 6, 1, 4, 4, 6, 2, 5,\n",
              "       2, 4, 4, 6, 6, 6, 6, 6, 6, 4, 5, 4, 5, 4, 5, 1, 2, 5, 6, 4, 4, 4,\n",
              "       6, 4, 6, 6, 5, 4, 5, 5, 6, 6, 6, 6, 6, 6, 5, 4, 6, 6, 4, 5, 0, 3,\n",
              "       6, 6, 4, 6, 6, 2, 6, 6, 4, 1, 4, 6, 4, 6, 6, 1, 4, 6, 1, 6, 6, 4,\n",
              "       6, 6, 3, 4, 5, 2, 6, 6, 4, 5, 1, 6, 5, 6, 4, 2, 4, 6, 6, 1, 4, 4,\n",
              "       1, 4, 6, 6, 5, 6, 4, 5, 1, 6, 6, 6, 6, 4, 4, 6, 4, 6, 5, 5, 6, 6,\n",
              "       4, 5, 3, 4, 5, 6, 6, 6, 6, 6, 6, 2, 5, 5, 1, 6, 2, 5, 6, 2, 6, 6,\n",
              "       4, 3, 1, 1, 6, 6, 6, 6, 1, 6, 6, 5, 6, 4, 6, 6, 6, 6, 5, 6, 1, 6,\n",
              "       6, 4, 4, 5, 6, 2, 1, 0, 6, 5, 5, 4, 6, 5, 5, 1, 6, 6, 5, 1, 5, 1,\n",
              "       6, 4, 1, 6, 6, 3, 4, 5, 6, 1, 6, 6, 1, 6, 5, 6, 2, 1, 6, 1, 2, 5,\n",
              "       6, 3, 3, 6, 3, 6, 6, 1, 6, 1, 5, 6, 6, 1, 6, 6, 6, 1, 6, 6, 5, 6,\n",
              "       1, 5, 5, 5, 5, 4, 4, 6, 6, 6, 6, 6, 1, 6, 5, 6], dtype=uint16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1429
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "3lJMacKE--xg",
        "outputId": "205fdf44-4b32-4552-fd92-7e8d6f1105f9"
      },
      "source": [
        "partition4 = colon_majority_df\n",
        "partition4=partition4.reset_index(drop=True)\n",
        "partition4['cluster_label'] = clusters\n",
        "partition4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              "0              1       85             66  ...   31        0              6\n",
              "1              1       89             66  ...   21        0              5\n",
              "2              5      116             74  ...   30        0              6\n",
              "3             10      115              0  ...   29        0              2\n",
              "4              4      110             92  ...   30        0              6\n",
              "..           ...      ...            ...  ...  ...      ...            ...\n",
              "495            9       89             62  ...   33        0              6\n",
              "496           10      101             76  ...   63        0              1\n",
              "497            2      122             70  ...   27        0              6\n",
              "498            5      121             72  ...   30        0              5\n",
              "499            1       93             70  ...   23        0              6\n",
              "\n",
              "[500 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1430
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLyTvUUr6-kB"
      },
      "source": [
        "## **MINIBATCH KMEANS CLUSTERING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_q3dmFr6qZJ"
      },
      "source": [
        "from sklearn.cluster import MiniBatchKMeans\n",
        "mbk = MiniBatchKMeans(init='k-means++', n_clusters=num_of_clusters, batch_size=40, n_init=10, max_no_improvement=10, verbose=0)\n",
        "mbk.fit(colon_majority_df)\n",
        "clustering_result['Minibatch']=mbk.labels_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLcRAduR88mx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f55c26f8-d9f9-4808-87cd-d42927e2c7e5"
      },
      "source": [
        "clustering_result['Minibatch'].values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 1, 1, 1, 1, 4, 6, 1, 0, 0, 1, 1, 4, 1, 1, 5, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 4, 4, 1, 2, 1, 0, 1, 0, 1, 1, 0, 1, 1, 4, 0, 0, 6, 1, 4,\n",
              "       1, 1, 1, 1, 1, 4, 1, 0, 1, 4, 1, 1, 5, 4, 4, 6, 1, 4, 4, 1, 1, 4,\n",
              "       1, 5, 1, 0, 1, 4, 1, 1, 1, 4, 1, 0, 1, 0, 0, 1, 4, 0, 4, 4, 1, 2,\n",
              "       1, 1, 4, 6, 1, 1, 0, 1, 1, 5, 1, 2, 0, 0, 4, 1, 0, 6, 1, 1, 1, 1,\n",
              "       4, 1, 4, 4, 1, 1, 1, 0, 4, 1, 1, 1, 0, 1, 1, 1, 1, 1, 4, 5, 1, 4,\n",
              "       1, 1, 1, 0, 1, 5, 4, 4, 1, 3, 4, 4, 1, 4, 1, 1, 4, 5, 1, 3, 2, 1,\n",
              "       1, 1, 4, 1, 1, 1, 2, 0, 1, 1, 4, 1, 1, 4, 1, 4, 1, 4, 0, 1, 2, 0,\n",
              "       5, 0, 3, 4, 4, 4, 1, 0, 5, 1, 4, 1, 0, 0, 1, 0, 0, 0, 4, 0, 5, 1,\n",
              "       5, 1, 4, 1, 4, 1, 4, 6, 1, 0, 4, 4, 1, 1, 0, 0, 1, 4, 1, 1, 1, 4,\n",
              "       1, 4, 1, 1, 2, 4, 1, 4, 0, 4, 0, 5, 4, 4, 4, 4, 1, 5, 4, 0, 4, 4,\n",
              "       5, 2, 0, 6, 0, 1, 1, 1, 5, 1, 1, 5, 2, 4, 1, 1, 5, 4, 4, 1, 1, 0,\n",
              "       1, 4, 4, 1, 1, 1, 1, 1, 1, 4, 0, 4, 0, 4, 4, 5, 1, 0, 1, 4, 4, 4,\n",
              "       1, 4, 1, 1, 0, 4, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 4, 0, 2, 6,\n",
              "       1, 1, 4, 1, 1, 1, 1, 1, 4, 5, 4, 1, 4, 1, 1, 5, 4, 1, 5, 1, 1, 4,\n",
              "       1, 1, 2, 4, 0, 1, 1, 1, 4, 0, 5, 1, 0, 1, 4, 1, 4, 1, 1, 5, 4, 4,\n",
              "       5, 0, 1, 1, 0, 1, 4, 0, 5, 1, 1, 1, 1, 4, 4, 1, 4, 1, 0, 0, 1, 1,\n",
              "       4, 0, 2, 4, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 6, 1, 4, 0, 1, 1, 1, 1,\n",
              "       4, 6, 5, 5, 1, 1, 1, 1, 5, 1, 1, 0, 1, 4, 1, 1, 1, 1, 0, 1, 5, 1,\n",
              "       1, 4, 4, 0, 1, 1, 0, 2, 1, 0, 0, 0, 1, 0, 0, 5, 1, 1, 0, 5, 0, 5,\n",
              "       1, 4, 6, 1, 1, 6, 4, 0, 1, 5, 1, 1, 5, 1, 0, 1, 1, 5, 1, 5, 1, 0,\n",
              "       1, 2, 2, 1, 2, 1, 1, 5, 1, 5, 0, 1, 1, 5, 1, 1, 1, 5, 1, 1, 0, 1,\n",
              "       5, 0, 0, 0, 0, 4, 4, 1, 1, 1, 1, 1, 5, 1, 0, 1], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1432
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "HddUke1y_66Y",
        "outputId": "7f23041b-4d86-4736-c999-7001ac62e247"
      },
      "source": [
        "partition5 = colon_majority_df\n",
        "partition5=partition5.reset_index(drop=True)\n",
        "partition5['cluster_label'] = clustering_result['Minibatch']\n",
        "partition5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              "0              1       85             66  ...   31        0              1\n",
              "1              1       89             66  ...   21        0              4\n",
              "2              5      116             74  ...   30        0              1\n",
              "3             10      115              0  ...   29        0              1\n",
              "4              4      110             92  ...   30        0              1\n",
              "..           ...      ...            ...  ...  ...      ...            ...\n",
              "495            9       89             62  ...   33        0              1\n",
              "496           10      101             76  ...   63        0              5\n",
              "497            2      122             70  ...   27        0              1\n",
              "498            5      121             72  ...   30        0              0\n",
              "499            1       93             70  ...   23        0              1\n",
              "\n",
              "[500 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1433
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPk4WYTGsW1N"
      },
      "source": [
        "## **Fuzzy K-Means Clustering**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQTbGraNsf6E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fbc9d31-c0c8-4448-aba0-535fc9084fdf"
      },
      "source": [
        "!pip install fuzzy-c-means\n",
        "import numpy as np\n",
        "from fcmeans import FCM\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: fuzzy-c-means in /usr/local/lib/python3.7/dist-packages (1.2.4)\n",
            "Requirement already satisfied: jax<0.3.0,>=0.2.7 in /usr/local/lib/python3.7/dist-packages (from fuzzy-c-means) (0.2.13)\n",
            "Requirement already satisfied: jaxlib<0.2.0,>=0.1.57 in /usr/local/lib/python3.7/dist-packages (from fuzzy-c-means) (0.1.66+cuda110)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax<0.3.0,>=0.2.7->fuzzy-c-means) (3.3.0)\n",
            "Requirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.7/dist-packages (from jax<0.3.0,>=0.2.7->fuzzy-c-means) (1.19.5)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax<0.3.0,>=0.2.7->fuzzy-c-means) (0.12.0)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from jaxlib<0.2.0,>=0.1.57->fuzzy-c-means) (1.12)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from jaxlib<0.2.0,>=0.1.57->fuzzy-c-means) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py->jax<0.3.0,>=0.2.7->fuzzy-c-means) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ym4EWq-a1FjT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bb76626-6f4d-4e8d-e7b0-9d3702dde896"
      },
      "source": [
        "fcm = FCM(n_clusters=num_of_clusters)\n",
        "fcm.fit(colon_majority_df.values)\n",
        "# outputs\n",
        "fcm_centers = fcm.centers\n",
        "fcm_labels = fcm.predict(colon_majority_df.values)\n",
        "fcm_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeviceArray([5, 3, 1, 5, 1, 1, 3, 4, 1, 3, 3, 1, 1, 0, 5, 1, 4, 1, 3, 1,\n",
              "             1, 1, 1, 5, 5, 0, 0, 5, 6, 5, 3, 1, 3, 5, 5, 3, 5, 1, 0, 3,\n",
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              "             3, 5, 0, 3, 0, 3, 1, 6, 1, 5, 0, 6, 5, 5, 3, 1, 5, 4, 1, 6,\n",
              "             3, 3, 0, 1, 3, 6, 5, 1, 1, 1, 0, 5, 0, 0, 5, 1, 5, 3, 5, 5,\n",
              "             1, 5, 3, 5, 5, 1, 1, 5, 0, 4, 5, 3, 5, 1, 1, 3, 5, 4, 0, 0,\n",
              "             5, 2, 0, 0, 1, 0, 5, 5, 3, 4, 1, 2, 6, 1, 5, 1, 0, 5, 5, 5,\n",
              "             6, 3, 5, 1, 0, 1, 5, 0, 1, 0, 1, 0, 3, 1, 6, 3, 4, 3, 2, 0,\n",
              "             0, 0, 1, 3, 4, 1, 0, 1, 3, 4, 5, 3, 3, 3, 0, 3, 4, 5, 4, 1,\n",
              "             0, 1, 0, 1, 0, 6, 5, 3, 0, 5, 1, 5, 3, 3, 5, 0, 5, 1, 5, 0,\n",
              "             5, 0, 1, 1, 6, 0, 5, 0, 3, 0, 3, 4, 0, 0, 0, 3, 5, 4, 0, 3,\n",
              "             0, 0, 4, 6, 3, 6, 3, 5, 1, 5, 4, 5, 5, 4, 6, 0, 5, 5, 4, 0,\n",
              "             0, 5, 5, 4, 5, 0, 0, 1, 5, 1, 1, 5, 1, 0, 3, 0, 3, 0, 3, 4,\n",
              "             1, 3, 1, 0, 0, 0, 5, 0, 5, 1, 3, 0, 3, 3, 1, 1, 5, 1, 1, 1,\n",
              "             3, 3, 1, 1, 0, 3, 6, 6, 5, 1, 0, 5, 5, 5, 1, 1, 0, 4, 0, 5,\n",
              "             0, 5, 5, 4, 0, 1, 4, 5, 5, 3, 1, 5, 6, 0, 3, 5, 1, 5, 0, 3,\n",
              "             4, 1, 3, 1, 0, 5, 0, 1, 5, 4, 0, 0, 4, 3, 1, 5, 3, 1, 0, 3,\n",
              "             4, 5, 1, 5, 5, 0, 0, 5, 3, 5, 3, 3, 5, 1, 0, 3, 6, 0, 3, 1,\n",
              "             5, 1, 5, 5, 5, 5, 3, 3, 4, 5, 5, 3, 5, 5, 1, 1, 0, 6, 4, 4,\n",
              "             5, 5, 1, 5, 4, 5, 1, 3, 5, 0, 1, 1, 1, 5, 3, 5, 4, 5, 1, 0,\n",
              "             0, 3, 1, 5, 4, 6, 5, 3, 3, 3, 1, 3, 3, 4, 1, 1, 3, 4, 3, 4,\n",
              "             5, 0, 4, 5, 5, 6, 0, 3, 1, 4, 1, 5, 4, 1, 3, 5, 5, 4, 1, 4,\n",
              "             1, 3, 5, 6, 6, 1, 6, 5, 5, 4, 5, 4, 3, 1, 5, 4, 1, 1, 5, 4,\n",
              "             1, 5, 3, 5, 4, 3, 3, 3, 3, 0, 0, 5, 1, 5, 5, 5, 4, 1, 3, 5],            dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1435
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUVGk8T-2jSC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "25a5b789-9dca-4e5a-eada-7a8bb948e1b2"
      },
      "source": [
        "partition6 = colon_majority_df\n",
        "partition6=partition6.reset_index(drop=True)\n",
        "partition6['cluster_label'] = fcm_labels\n",
        "partition6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              "0              1       85             66  ...   31        0              5\n",
              "1              1       89             66  ...   21        0              3\n",
              "2              5      116             74  ...   30        0              1\n",
              "3             10      115              0  ...   29        0              5\n",
              "4              4      110             92  ...   30        0              1\n",
              "..           ...      ...            ...  ...  ...      ...            ...\n",
              "495            9       89             62  ...   33        0              5\n",
              "496           10      101             76  ...   63        0              4\n",
              "497            2      122             70  ...   27        0              1\n",
              "498            5      121             72  ...   30        0              3\n",
              "499            1       93             70  ...   23        0              5\n",
              "\n",
              "[500 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1436
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1eKJ4_2x0jW",
        "outputId": "cf6960ed-b7b6-4bd4-b0a2-32e922a8ecb7"
      },
      "source": [
        "partition1, partition2, partition3, partition4,partition5,partition6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(     Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              " 0              1       85             66  ...   31        0              2\n",
              " 1              1       89             66  ...   21        0              3\n",
              " 2              5      116             74  ...   30        0              4\n",
              " 3             10      115              0  ...   29        0              5\n",
              " 4              4      110             92  ...   30        0              2\n",
              " ..           ...      ...            ...  ...  ...      ...            ...\n",
              " 495            9       89             62  ...   33        0              2\n",
              " 496           10      101             76  ...   63        0              1\n",
              " 497            2      122             70  ...   27        0              4\n",
              " 498            5      121             72  ...   30        0              1\n",
              " 499            1       93             70  ...   23        0              2\n",
              " \n",
              " [500 rows x 10 columns],\n",
              "      Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              " 0              1       85             66  ...   31        0              1\n",
              " 1              1       89             66  ...   21        0              5\n",
              " 2              5      116             74  ...   30        0              4\n",
              " 3             10      115              0  ...   29        0              1\n",
              " 4              4      110             92  ...   30        0              4\n",
              " ..           ...      ...            ...  ...  ...      ...            ...\n",
              " 495            9       89             62  ...   33        0              1\n",
              " 496           10      101             76  ...   63        0              0\n",
              " 497            2      122             70  ...   27        0              4\n",
              " 498            5      121             72  ...   30        0              3\n",
              " 499            1       93             70  ...   23        0              1\n",
              " \n",
              " [500 rows x 10 columns],\n",
              "      Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              " 0              1       85             66  ...   31        0              0\n",
              " 1              1       89             66  ...   21        0              0\n",
              " 2              5      116             74  ...   30        0              3\n",
              " 3             10      115              0  ...   29        0              4\n",
              " 4              4      110             92  ...   30        0              0\n",
              " ..           ...      ...            ...  ...  ...      ...            ...\n",
              " 495            9       89             62  ...   33        0              0\n",
              " 496           10      101             76  ...   63        0              5\n",
              " 497            2      122             70  ...   27        0              2\n",
              " 498            5      121             72  ...   30        0              0\n",
              " 499            1       93             70  ...   23        0              6\n",
              " \n",
              " [500 rows x 10 columns],\n",
              "      Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              " 0              1       85             66  ...   31        0              6\n",
              " 1              1       89             66  ...   21        0              5\n",
              " 2              5      116             74  ...   30        0              6\n",
              " 3             10      115              0  ...   29        0              2\n",
              " 4              4      110             92  ...   30        0              6\n",
              " ..           ...      ...            ...  ...  ...      ...            ...\n",
              " 495            9       89             62  ...   33        0              6\n",
              " 496           10      101             76  ...   63        0              1\n",
              " 497            2      122             70  ...   27        0              6\n",
              " 498            5      121             72  ...   30        0              5\n",
              " 499            1       93             70  ...   23        0              6\n",
              " \n",
              " [500 rows x 10 columns],\n",
              "      Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              " 0              1       85             66  ...   31        0              1\n",
              " 1              1       89             66  ...   21        0              4\n",
              " 2              5      116             74  ...   30        0              1\n",
              " 3             10      115              0  ...   29        0              1\n",
              " 4              4      110             92  ...   30        0              1\n",
              " ..           ...      ...            ...  ...  ...      ...            ...\n",
              " 495            9       89             62  ...   33        0              1\n",
              " 496           10      101             76  ...   63        0              5\n",
              " 497            2      122             70  ...   27        0              1\n",
              " 498            5      121             72  ...   30        0              0\n",
              " 499            1       93             70  ...   23        0              1\n",
              " \n",
              " [500 rows x 10 columns],\n",
              "      Pregnancies  Glucose  BloodPressure  ...  Age  Outcome  cluster_label\n",
              " 0              1       85             66  ...   31        0              5\n",
              " 1              1       89             66  ...   21        0              3\n",
              " 2              5      116             74  ...   30        0              1\n",
              " 3             10      115              0  ...   29        0              5\n",
              " 4              4      110             92  ...   30        0              1\n",
              " ..           ...      ...            ...  ...  ...      ...            ...\n",
              " 495            9       89             62  ...   33        0              5\n",
              " 496           10      101             76  ...   63        0              4\n",
              " 497            2      122             70  ...   27        0              1\n",
              " 498            5      121             72  ...   30        0              3\n",
              " 499            1       93             70  ...   23        0              5\n",
              " \n",
              " [500 rows x 10 columns])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1437
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uywOF-D49Sd",
        "outputId": "32fa6f0f-3c60-4652-ed62-5f3e23604843"
      },
      "source": [
        "c = 0\n",
        "for i in range(len(partition1.cluster_label)):\n",
        "  print(partition1.cluster_label[i], partition2.cluster_label[i], partition3.cluster_label[i],partition4.cluster_label[i],partition5.cluster_label[i],partition6.cluster_label[i])\n",
        "  #if partition1.cluster_label[i]==partition2.cluster_label[i]:\n",
        "    #c+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2 1 0 6 1 5\n",
            "3 5 0 5 4 3\n",
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            "3 5 0 4 4 3\n",
            "6 0 0 1 6 4\n",
            "2 4 0 6 1 1\n",
            "1 3 0 5 0 3\n",
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            "6 6 0 3 2 6\n",
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            "4 4 3 6 1 1\n",
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            "2 1 2 6 1 5\n",
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            "4 4 0 6 1 1\n",
            "2 1 0 6 1 5\n",
            "6 6 0 3 2 6\n",
            "3 5 2 4 4 0\n",
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            "5 1 4 2 1 5\n",
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            "2 1 1 6 1 5\n",
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            "2 1 4 6 1 1\n",
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            "3 5 0 4 4 0\n",
            "5 1 4 2 1 5\n",
            "3 5 0 4 4 0\n",
            "2 4 4 6 1 1\n",
            "2 1 4 6 1 5\n",
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            "3 5 0 4 4 0\n",
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            "1 0 3 1 5 4\n",
            "3 3 0 4 0 3\n",
            "4 4 6 6 1 1\n",
            "2 1 5 6 1 5\n",
            "3 3 0 5 0 3\n",
            "4 4 0 6 1 1\n",
            "3 5 6 4 4 0\n",
            "3 3 0 5 0 3\n",
            "6 0 5 1 5 4\n",
            "2 1 6 6 1 5\n",
            "4 4 0 6 1 1\n",
            "2 1 0 6 1 5\n",
            "2 1 3 6 1 5\n",
            "3 5 0 4 4 0\n",
            "3 5 0 4 4 0\n",
            "2 1 4 6 1 5\n",
            "3 5 2 4 4 3\n",
            "2 1 0 6 1 5\n",
            "1 3 0 5 0 3\n",
            "1 3 0 5 0 3\n",
            "2 1 5 6 1 5\n",
            "4 4 2 6 1 1\n",
            "3 5 5 4 4 0\n",
            "1 3 1 5 0 3\n",
            "6 6 6 3 2 6\n",
            "3 5 0 4 4 0\n",
            "1 3 2 5 0 3\n",
            "4 4 0 6 1 1\n",
            "2 1 0 6 1 5\n",
            "4 4 1 6 1 1\n",
            "2 1 3 6 1 5\n",
            "2 1 6 6 1 5\n",
            "2 1 0 6 1 5\n",
            "5 1 4 2 1 5\n",
            "1 3 0 5 0 3\n",
            "3 3 2 5 0 3\n",
            "6 0 0 1 6 4\n",
            "2 1 4 6 1 5\n",
            "5 1 0 2 4 5\n",
            "1 3 0 5 0 3\n",
            "2 4 0 6 1 5\n",
            "5 1 3 2 1 5\n",
            "4 4 0 6 1 1\n",
            "4 4 0 6 1 1\n",
            "3 5 0 4 4 0\n",
            "6 6 1 3 6 6\n",
            "1 0 6 1 5 4\n",
            "1 0 0 1 5 4\n",
            "2 1 5 6 1 5\n",
            "2 1 0 6 1 5\n",
            "4 4 0 6 1 1\n",
            "2 1 2 6 1 5\n",
            "1 0 2 1 5 4\n",
            "2 1 2 6 1 5\n",
            "4 4 0 6 1 1\n",
            "1 3 1 5 0 3\n",
            "2 1 2 6 1 5\n",
            "3 5 0 4 4 0\n",
            "4 4 1 6 1 1\n",
            "4 4 1 6 1 1\n",
            "4 4 3 6 1 1\n",
            "2 1 2 6 1 5\n",
            "3 5 1 5 0 3\n",
            "2 1 2 6 1 5\n",
            "1 0 0 1 5 4\n",
            "2 1 3 6 1 5\n",
            "2 4 3 6 1 1\n",
            "3 5 2 4 4 0\n",
            "3 5 3 4 4 0\n",
            "3 3 1 5 0 3\n",
            "4 4 0 6 1 1\n",
            "5 1 4 2 1 5\n",
            "1 0 0 1 0 4\n",
            "0 6 2 0 2 6\n",
            "2 1 4 6 1 5\n",
            "3 3 6 5 0 3\n",
            "1 3 0 5 0 3\n",
            "3 5 3 4 0 3\n",
            "4 1 2 6 1 1\n",
            "1 3 5 5 0 3\n",
            "3 5 5 5 0 3\n",
            "1 0 0 1 5 4\n",
            "4 4 3 6 1 1\n",
            "4 4 0 6 1 1\n",
            "1 3 0 5 0 3\n",
            "1 0 0 1 5 4\n",
            "1 3 0 5 0 3\n",
            "1 0 0 1 5 4\n",
            "2 1 0 6 1 5\n",
            "3 5 0 4 4 0\n",
            "6 0 5 1 6 4\n",
            "2 1 0 6 1 5\n",
            "2 1 4 6 1 5\n",
            "6 6 1 3 6 6\n",
            "3 5 5 4 4 0\n",
            "3 3 1 5 0 3\n",
            "4 4 3 6 1 1\n",
            "6 0 2 1 5 4\n",
            "4 4 2 6 1 1\n",
            "2 1 0 6 1 5\n",
            "1 0 6 1 5 4\n",
            "2 4 4 6 1 1\n",
            "3 3 6 5 0 3\n",
            "2 1 2 6 1 5\n",
            "5 1 4 2 1 5\n",
            "1 0 3 1 5 4\n",
            "4 4 0 6 1 1\n",
            "1 0 2 1 5 4\n",
            "5 1 2 2 1 1\n",
            "3 3 0 5 0 3\n",
            "2 1 3 6 1 5\n",
            "6 6 2 3 2 6\n",
            "0 6 0 3 2 6\n",
            "4 4 3 6 1 1\n",
            "6 6 1 3 2 6\n",
            "2 1 3 6 1 5\n",
            "2 1 0 6 1 5\n",
            "1 0 0 1 5 4\n",
            "2 1 0 6 1 5\n",
            "1 0 0 1 5 4\n",
            "1 3 3 5 0 3\n",
            "4 4 0 6 1 1\n",
            "2 4 0 6 1 5\n",
            "1 0 0 1 5 4\n",
            "4 4 5 6 1 1\n",
            "4 4 2 6 1 1\n",
            "2 1 2 6 1 5\n",
            "1 0 2 1 5 4\n",
            "2 4 2 6 1 1\n",
            "2 1 3 6 1 5\n",
            "1 3 1 5 0 3\n",
            "2 1 0 6 1 5\n",
            "1 0 1 1 5 4\n",
            "3 3 1 5 0 3\n",
            "1 3 0 5 0 3\n",
            "1 3 0 5 0 3\n",
            "3 3 0 5 0 3\n",
            "3 5 0 4 4 0\n",
            "3 5 0 4 4 0\n",
            "2 1 2 6 1 5\n",
            "4 4 0 6 1 1\n",
            "2 1 0 6 1 5\n",
            "2 1 2 6 1 5\n",
            "2 1 0 6 1 5\n",
            "1 0 5 1 5 4\n",
            "4 4 2 6 1 1\n",
            "1 3 0 5 0 3\n",
            "2 1 6 6 1 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wLZB1OwYA1m"
      },
      "source": [
        "## **Finding the 3 Best algorithms for our model:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6h5hXJ-lYAMN",
        "outputId": "63ff5a4d-4cff-4673-a51f-7fe338515507"
      },
      "source": [
        "from sklearn.metrics import davies_bouldin_score\n",
        "print(\"Kmeans (partition 1): \",davies_bouldin_score(colon_majority_df, partition1.cluster_label))\n",
        "print(\"Kmeans++ (partition 2): \",davies_bouldin_score(colon_majority_df, partition2.cluster_label))\n",
        "print(\"KModes (partition 3): \",davies_bouldin_score(colon_majority_df, partition3.cluster_label))\n",
        "print(\"K-prototypes(partition 4): \",davies_bouldin_score(colon_majority_df, partition4.cluster_label))\n",
        "print(\"Minibatch Kmeans (partition 5): \",davies_bouldin_score(colon_majority_df, partition5.cluster_label))\n",
        "print(\"Fuzzy K-means(partition 6): \",davies_bouldin_score(colon_majority_df, partition6.cluster_label))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Kmeans (partition 1):  0.9053928820550031\n",
            "Kmeans++ (partition 2):  0.9990704965175017\n",
            "KModes (partition 3):  11.218636750442439\n",
            "K-prototypes(partition 4):  0.8565564328935757\n",
            "Minibatch Kmeans (partition 5):  0.9044098636676213\n",
            "Fuzzy K-means(partition 6):  0.9904746219927499\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dm4k1nylAqjz"
      },
      "source": [
        "partitionA, partitionB, partitionC = partition1, partition2, partition5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "id": "hpmQ6K2eBRBG",
        "outputId": "a1188db4-d810-4d4d-a1f5-c37355b4d435"
      },
      "source": [
        "partitionA, partitionB, partitionC = partition1, partition2, partition5\n",
        "colon_majority = colon_majority_df\n",
        "colon_majority['C1'] = partitionA.cluster_label.values\n",
        "colon_majority['C2'] = partitionB.cluster_label.values\n",
        "colon_majority['C3'] = partitionC.cluster_label.values\n",
        "colon_majority"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>762</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  ...  Outcome  C1  C2  C3\n",
              "1              1       85             66             29  ...        0   2   1   1\n",
              "3              1       89             66             23  ...        0   3   5   4\n",
              "5              5      116             74              0  ...        0   4   4   1\n",
              "7             10      115              0              0  ...        0   5   1   1\n",
              "10             4      110             92              0  ...        0   2   4   1\n",
              "..           ...      ...            ...            ...  ...      ...  ..  ..  ..\n",
              "762            9       89             62              0  ...        0   2   1   1\n",
              "763           10      101             76             48  ...        0   1   0   5\n",
              "764            2      122             70             27  ...        0   4   4   1\n",
              "765            5      121             72             23  ...        0   1   3   0\n",
              "767            1       93             70             31  ...        0   2   1   1\n",
              "\n",
              "[500 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1441
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zZ-R8RNB8e_"
      },
      "source": [
        "#colon_majority.to_csv('colon_majority.csv', index = False)\n",
        "#from google.colab import files\n",
        "#files.download(\"colon_majority.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "AY5Nl2BWDWyh",
        "outputId": "f99d3c45-c45e-4c1b-d0d4-5363ee4e3cdf"
      },
      "source": [
        "colon_majority['index'] = range(0, len(colon_majority))\n",
        "\n",
        "colon_majority=colon_majority.reset_index(drop=False)\n",
        "colon_majority.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\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>level_0</th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   level_0  Pregnancies  Glucose  BloodPressure  ...  C1  C2  C3  index\n",
              "0        1            1       85             66  ...   2   1   1      0\n",
              "1        3            1       89             66  ...   3   5   4      1\n",
              "2        5            5      116             74  ...   4   4   1      2\n",
              "3        7           10      115              0  ...   5   1   1      3\n",
              "4       10            4      110             92  ...   2   4   1      4\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1447
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "dR4qn2OxJ3xJ",
        "outputId": "7a66ceb0-5881-451a-8ee4-013fa3888094"
      },
      "source": [
        "colon_majority=colon_majority.drop(['level_0'], axis=1)\n",
        "colon_majority.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
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              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  ...  C1  C2  C3  index\n",
              "0            1       85             66             29  ...   2   1   1      0\n",
              "1            1       89             66             23  ...   3   5   4      1\n",
              "2            5      116             74              0  ...   4   4   1      2\n",
              "3           10      115              0              0  ...   5   1   1      3\n",
              "4            4      110             92              0  ...   2   4   1      4\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1448
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "nndsadktKCrx",
        "outputId": "d29fed75-0019-42db-fd01-0649bc50e39e"
      },
      "source": [
        "clustering_result=colon_majority[['index','C1','C2','C3']]\n",
        "clustering_result.rename(columns={'index': 'Datapoint(x)'}, inplace=True)\n",
        "clustering_result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4308: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Datapoint(x)</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</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>495</th>\n",
              "      <td>495</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>496</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>497</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>498</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>499</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C1  C2  C3\n",
              "0               0   2   1   1\n",
              "1               1   3   5   4\n",
              "2               2   4   4   1\n",
              "3               3   5   1   1\n",
              "4               4   2   4   1\n",
              "..            ...  ..  ..  ..\n",
              "495           495   2   1   1\n",
              "496           496   1   0   5\n",
              "497           497   4   4   1\n",
              "498           498   1   3   0\n",
              "499           499   2   1   1\n",
              "\n",
              "[500 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1449
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "NQ2doQN2KGUC",
        "outputId": "f2a74037-0fe8-499e-be93-b4a5098037de"
      },
      "source": [
        "\n",
        "num_of_clusters=7\n",
        "C1_datapoints={}\n",
        "C2_datapoints={}\n",
        "C3_datapoints={}\n",
        "def clusters(dictionary,clustername):\n",
        "  for i in clustering_result.index:\n",
        "    if clustering_result.loc[i,clustername] not in dictionary.keys():\n",
        "      dictionary[clustering_result.loc[i,clustername]]=[clustering_result.loc[i,'Datapoint(x)']]\n",
        "    else:\n",
        "      dictionary[clustering_result.loc[i,clustername]].append(clustering_result.loc[i,'Datapoint(x)'])\n",
        "  return dictionary\n",
        "\n",
        "C1_datapoints=clusters(C1_datapoints,'C1')\n",
        "C2_datapoints=clusters(C2_datapoints,'C2')\n",
        "C3_datapoints=clusters(C3_datapoints,'C3')\n",
        "print(\"C1 clusters :\",C1_datapoints)\n",
        "print(\"C3 clusters :\",C3_datapoints)\n",
        "\n",
        "\n",
        "def common_member(list1,list2):\n",
        "  set1=set(list1)\n",
        "  set2=set(list2)\n",
        "  return len(set1.intersection(set2))\n",
        "\n",
        "# def union(list1, list2):\n",
        "#   l = list1 + list2\n",
        "#   return len(set(l))\n",
        "\n",
        "\n",
        "contingency_matrix1=[]\n",
        "for i in range(7):\n",
        "  common=[]\n",
        "  for j in range(7):\n",
        "    common_element=common_member(C3_datapoints[i],C1_datapoints[j])\n",
        "    common.append(common_element)\n",
        "  contingency_matrix1.append(common)\n",
        "contingency_df1=pd.DataFrame(contingency_matrix1,index=['C30','C31','C32','C33','C34','C35','C36'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "contingency_df1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C1 clusters : {2: [0, 4, 8, 12, 14, 20, 23, 27, 29, 34, 36, 42, 43, 44, 45, 46, 47, 50, 52, 54, 55, 60, 66, 68, 70, 72, 73, 74, 76, 81, 89, 92, 93, 96, 98, 106, 109, 114, 116, 118, 119, 121, 123, 124, 125, 127, 130, 132, 139, 140, 146, 147, 153, 154, 157, 158, 159, 162, 166, 170, 190, 197, 203, 209, 211, 216, 218, 220, 223, 226, 236, 247, 249, 251, 252, 256, 257, 261, 268, 271, 272, 286, 288, 291, 296, 298, 308, 311, 312, 315, 319, 321, 322, 327, 328, 331, 337, 341, 343, 347, 348, 355, 361, 363, 364, 367, 369, 372, 380, 382, 383, 384, 389, 392, 400, 401, 403, 405, 408, 413, 415, 417, 418, 426, 440, 443, 444, 451, 453, 455, 462, 467, 468, 470, 474, 478, 480, 481, 483, 491, 493, 494, 495, 499], 3: [1, 6, 13, 18, 25, 26, 38, 49, 53, 57, 58, 61, 62, 65, 71, 75, 77, 80, 82, 84, 85, 90, 100, 102, 104, 110, 112, 113, 117, 122, 128, 131, 138, 142, 143, 145, 148, 156, 164, 167, 169, 171, 179, 180, 181, 186, 192, 193, 194, 200, 202, 204, 208, 213, 215, 219, 221, 225, 227, 228, 229, 230, 232, 233, 234, 235, 238, 240, 241, 244, 255, 259, 260, 265, 266, 273, 275, 276, 277, 278, 281, 283, 284, 285, 287, 292, 301, 304, 310, 316, 318, 320, 324, 329, 333, 338, 339, 344, 346, 350, 351, 353, 356, 358, 359, 365, 366, 368, 374, 377, 387, 396, 409, 414, 419, 420, 421, 427, 429, 432, 441, 446, 447, 454, 461, 485, 488, 489, 490], 4: [2, 5, 11, 15, 17, 19, 21, 22, 31, 37, 63, 64, 78, 86, 88, 95, 103, 107, 108, 115, 120, 126, 133, 134, 144, 150, 155, 163, 165, 168, 173, 182, 185, 187, 199, 201, 210, 217, 222, 248, 267, 269, 270, 282, 289, 294, 295, 297, 299, 302, 303, 309, 314, 325, 330, 336, 354, 357, 362, 373, 379, 381, 394, 395, 402, 406, 410, 411, 412, 422, 430, 434, 435, 448, 450, 458, 465, 473, 476, 477, 492, 497], 5: [3, 24, 33, 48, 111, 136, 206, 214, 262, 264, 280, 313, 335, 345, 385, 390, 393, 423, 456, 460], 6: [7, 28, 41, 59, 87, 91, 97, 105, 174, 184, 196, 205, 224, 245, 254, 279, 307, 326, 332, 349, 360, 376, 388, 397, 442, 445, 449, 463, 466], 1: [9, 10, 16, 30, 32, 35, 39, 40, 51, 56, 67, 69, 79, 83, 94, 101, 129, 135, 137, 149, 161, 172, 175, 176, 177, 183, 188, 189, 191, 195, 198, 207, 212, 231, 237, 239, 242, 246, 250, 253, 258, 263, 274, 290, 293, 300, 305, 317, 323, 334, 340, 342, 352, 370, 371, 375, 378, 386, 391, 398, 399, 404, 407, 416, 424, 428, 431, 433, 436, 437, 438, 439, 452, 457, 459, 469, 471, 472, 475, 479, 482, 484, 486, 487, 496, 498], 0: [99, 141, 151, 152, 160, 178, 243, 306, 425, 464]}\n",
            "C3 clusters : {1: [0, 2, 3, 4, 5, 8, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 27, 29, 31, 33, 34, 36, 37, 42, 44, 45, 46, 47, 48, 50, 52, 54, 55, 60, 63, 64, 66, 68, 70, 72, 73, 74, 76, 78, 81, 86, 88, 89, 92, 93, 95, 96, 98, 103, 106, 107, 108, 109, 111, 114, 115, 116, 119, 120, 121, 123, 124, 125, 126, 127, 130, 132, 133, 134, 136, 140, 144, 146, 147, 150, 153, 154, 155, 157, 158, 159, 162, 163, 165, 166, 168, 170, 173, 182, 185, 187, 190, 197, 199, 201, 203, 206, 210, 211, 214, 216, 217, 218, 220, 222, 223, 226, 236, 247, 248, 249, 251, 252, 256, 257, 261, 262, 264, 267, 268, 269, 270, 271, 272, 280, 282, 286, 288, 289, 294, 295, 296, 297, 298, 299, 302, 303, 308, 309, 311, 312, 313, 314, 315, 319, 321, 322, 325, 327, 328, 330, 331, 335, 336, 337, 341, 343, 345, 347, 348, 354, 355, 357, 361, 362, 363, 364, 367, 369, 372, 373, 379, 380, 381, 382, 383, 384, 385, 389, 392, 393, 394, 395, 400, 401, 402, 403, 405, 406, 408, 410, 411, 412, 413, 415, 417, 418, 422, 423, 426, 430, 434, 435, 440, 443, 444, 448, 450, 451, 453, 455, 456, 458, 460, 462, 465, 467, 468, 470, 473, 474, 476, 477, 478, 480, 481, 483, 491, 492, 493, 494, 495, 497, 499], 4: [1, 6, 13, 25, 26, 38, 43, 49, 53, 57, 58, 61, 62, 65, 71, 75, 82, 84, 85, 90, 102, 110, 112, 113, 118, 128, 131, 138, 139, 142, 143, 145, 148, 156, 164, 167, 169, 171, 179, 180, 181, 186, 194, 200, 202, 204, 208, 209, 215, 219, 221, 225, 227, 229, 232, 233, 234, 235, 238, 240, 241, 255, 259, 260, 265, 266, 273, 275, 277, 278, 283, 284, 285, 287, 291, 304, 310, 316, 318, 320, 324, 329, 333, 338, 344, 346, 350, 351, 358, 365, 366, 368, 374, 377, 390, 396, 409, 419, 420, 441, 446, 489, 490], 6: [7, 41, 59, 91, 105, 205, 245, 307, 388, 397, 442, 445], 0: [9, 10, 18, 30, 32, 35, 39, 40, 51, 69, 77, 79, 80, 83, 94, 100, 101, 104, 117, 122, 135, 161, 172, 175, 177, 183, 188, 189, 191, 192, 193, 195, 207, 212, 213, 228, 230, 239, 244, 246, 263, 274, 276, 281, 290, 292, 293, 300, 301, 305, 334, 339, 342, 353, 356, 359, 370, 371, 375, 378, 386, 387, 391, 407, 414, 421, 424, 427, 428, 429, 431, 432, 436, 438, 447, 454, 461, 472, 482, 485, 486, 487, 488, 498], 5: [16, 56, 67, 97, 129, 137, 149, 176, 184, 196, 198, 231, 237, 242, 250, 253, 258, 279, 317, 323, 326, 340, 349, 352, 360, 398, 399, 404, 416, 433, 437, 439, 449, 452, 457, 459, 469, 471, 475, 479, 484, 496], 2: [28, 87, 99, 152, 160, 174, 224, 243, 254, 306, 332, 376, 425, 463, 464, 466], 3: [141, 151, 178]}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>139</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C10  C11  C12  C13  C14  C15  C16\n",
              "C30    0   52    0   32    0    0    0\n",
              "C31    0    0  139    0   82   19    0\n",
              "C32    7    0    0    0    0    0    9\n",
              "C33    3    0    0    0    0    0    0\n",
              "C34    0    0    5   97    0    1    0\n",
              "C35    0   34    0    0    0    0    8\n",
              "C36    0    0    0    0    0    0   12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1451
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "qRP1T6MoKI7s",
        "outputId": "026cbc72-af74-462f-a0e1-26647d35fff5"
      },
      "source": [
        "print(\"C1 clusters :\",C1_datapoints)\n",
        "print(\"C2 clusters :\",C2_datapoints)\n",
        "\n",
        "contingency_matrix2=[]\n",
        "for i in range(7):\n",
        "  common=[]\n",
        "  for j in range(7):\n",
        "    common_element=common_member(C2_datapoints[i],C1_datapoints[j])\n",
        "    common.append(common_element)\n",
        "  contingency_matrix2.append(common)\n",
        "contingency_df2=pd.DataFrame(contingency_matrix2,index=['C20','C21','C22','C23','C24','C25','C26'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "contingency_df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C1 clusters : {2: [0, 4, 8, 12, 14, 20, 23, 27, 29, 34, 36, 42, 43, 44, 45, 46, 47, 50, 52, 54, 55, 60, 66, 68, 70, 72, 73, 74, 76, 81, 89, 92, 93, 96, 98, 106, 109, 114, 116, 118, 119, 121, 123, 124, 125, 127, 130, 132, 139, 140, 146, 147, 153, 154, 157, 158, 159, 162, 166, 170, 190, 197, 203, 209, 211, 216, 218, 220, 223, 226, 236, 247, 249, 251, 252, 256, 257, 261, 268, 271, 272, 286, 288, 291, 296, 298, 308, 311, 312, 315, 319, 321, 322, 327, 328, 331, 337, 341, 343, 347, 348, 355, 361, 363, 364, 367, 369, 372, 380, 382, 383, 384, 389, 392, 400, 401, 403, 405, 408, 413, 415, 417, 418, 426, 440, 443, 444, 451, 453, 455, 462, 467, 468, 470, 474, 478, 480, 481, 483, 491, 493, 494, 495, 499], 3: [1, 6, 13, 18, 25, 26, 38, 49, 53, 57, 58, 61, 62, 65, 71, 75, 77, 80, 82, 84, 85, 90, 100, 102, 104, 110, 112, 113, 117, 122, 128, 131, 138, 142, 143, 145, 148, 156, 164, 167, 169, 171, 179, 180, 181, 186, 192, 193, 194, 200, 202, 204, 208, 213, 215, 219, 221, 225, 227, 228, 229, 230, 232, 233, 234, 235, 238, 240, 241, 244, 255, 259, 260, 265, 266, 273, 275, 276, 277, 278, 281, 283, 284, 285, 287, 292, 301, 304, 310, 316, 318, 320, 324, 329, 333, 338, 339, 344, 346, 350, 351, 353, 356, 358, 359, 365, 366, 368, 374, 377, 387, 396, 409, 414, 419, 420, 421, 427, 429, 432, 441, 446, 447, 454, 461, 485, 488, 489, 490], 4: [2, 5, 11, 15, 17, 19, 21, 22, 31, 37, 63, 64, 78, 86, 88, 95, 103, 107, 108, 115, 120, 126, 133, 134, 144, 150, 155, 163, 165, 168, 173, 182, 185, 187, 199, 201, 210, 217, 222, 248, 267, 269, 270, 282, 289, 294, 295, 297, 299, 302, 303, 309, 314, 325, 330, 336, 354, 357, 362, 373, 379, 381, 394, 395, 402, 406, 410, 411, 412, 422, 430, 434, 435, 448, 450, 458, 465, 473, 476, 477, 492, 497], 5: [3, 24, 33, 48, 111, 136, 206, 214, 262, 264, 280, 313, 335, 345, 385, 390, 393, 423, 456, 460], 6: [7, 28, 41, 59, 87, 91, 97, 105, 174, 184, 196, 205, 224, 245, 254, 279, 307, 326, 332, 349, 360, 376, 388, 397, 442, 445, 449, 463, 466], 1: [9, 10, 16, 30, 32, 35, 39, 40, 51, 56, 67, 69, 79, 83, 94, 101, 129, 135, 137, 149, 161, 172, 175, 176, 177, 183, 188, 189, 191, 195, 198, 207, 212, 231, 237, 239, 242, 246, 250, 253, 258, 263, 274, 290, 293, 300, 305, 317, 323, 334, 340, 342, 352, 370, 371, 375, 378, 386, 391, 398, 399, 404, 407, 416, 424, 428, 431, 433, 436, 437, 438, 439, 452, 457, 459, 469, 471, 472, 475, 479, 482, 484, 486, 487, 496, 498], 0: [99, 141, 151, 152, 160, 178, 243, 306, 425, 464]}\n",
            "C2 clusters : {1: [0, 3, 14, 23, 24, 27, 29, 33, 34, 36, 42, 43, 44, 45, 46, 47, 48, 50, 52, 54, 55, 60, 66, 70, 72, 73, 74, 76, 81, 92, 93, 96, 106, 109, 111, 114, 116, 118, 119, 121, 123, 124, 127, 130, 132, 136, 140, 146, 147, 154, 157, 158, 159, 162, 166, 190, 197, 206, 209, 211, 214, 216, 218, 220, 226, 236, 247, 249, 251, 252, 256, 257, 261, 262, 264, 268, 271, 280, 286, 288, 291, 298, 308, 311, 312, 313, 315, 319, 321, 322, 327, 328, 331, 335, 337, 341, 343, 345, 348, 355, 361, 363, 364, 367, 369, 372, 380, 382, 383, 384, 385, 389, 390, 393, 400, 401, 403, 405, 408, 413, 415, 417, 423, 426, 430, 440, 443, 444, 451, 455, 456, 460, 462, 467, 468, 470, 478, 481, 483, 491, 493, 494, 495, 499], 5: [1, 6, 13, 25, 26, 38, 49, 53, 57, 58, 61, 62, 65, 71, 75, 82, 84, 85, 90, 100, 102, 110, 112, 113, 128, 131, 138, 139, 142, 143, 145, 148, 156, 164, 167, 169, 171, 179, 180, 181, 186, 192, 194, 200, 202, 204, 208, 215, 219, 221, 225, 227, 229, 232, 233, 234, 235, 238, 240, 241, 255, 259, 260, 265, 266, 273, 275, 276, 277, 278, 283, 284, 285, 287, 301, 304, 310, 316, 318, 320, 324, 329, 333, 338, 344, 346, 350, 351, 358, 365, 366, 368, 374, 377, 396, 409, 414, 419, 420, 429, 432, 441, 446, 489, 490], 4: [2, 4, 5, 8, 11, 12, 15, 17, 19, 20, 21, 22, 31, 37, 63, 64, 68, 78, 86, 88, 89, 95, 98, 103, 107, 108, 115, 120, 125, 126, 133, 134, 144, 150, 153, 155, 163, 165, 168, 170, 173, 182, 185, 187, 199, 201, 203, 210, 217, 222, 223, 248, 267, 269, 270, 272, 282, 289, 294, 295, 296, 297, 299, 302, 303, 309, 314, 325, 330, 336, 347, 354, 357, 362, 373, 379, 381, 392, 394, 395, 402, 406, 410, 411, 412, 418, 422, 434, 435, 448, 450, 453, 458, 465, 473, 474, 476, 477, 480, 492, 497], 0: [7, 16, 56, 59, 67, 97, 129, 137, 149, 176, 184, 196, 198, 231, 237, 242, 250, 253, 258, 279, 317, 323, 326, 340, 349, 352, 360, 388, 398, 399, 404, 416, 424, 433, 437, 439, 442, 449, 452, 457, 459, 469, 471, 475, 479, 484, 496], 3: [9, 10, 18, 30, 32, 35, 39, 40, 51, 69, 77, 79, 80, 83, 94, 101, 104, 117, 122, 135, 161, 172, 175, 177, 183, 188, 189, 191, 193, 195, 207, 212, 213, 228, 230, 239, 244, 246, 263, 274, 281, 290, 292, 293, 300, 305, 334, 339, 342, 353, 356, 359, 370, 371, 375, 378, 386, 387, 391, 407, 421, 427, 428, 431, 436, 438, 447, 454, 461, 472, 482, 485, 486, 487, 488, 498], 6: [28, 41, 87, 91, 105, 152, 160, 174, 205, 224, 243, 245, 254, 307, 332, 376, 397, 425, 445, 463, 464, 466], 2: [99, 141, 151, 178, 306]}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>104</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C10  C11  C12  C13  C14  C15  C16\n",
              "C20    0   35    0    0    0    0   12\n",
              "C21    0    0  123    0    1   20    0\n",
              "C22    5    0    0    0    0    0    0\n",
              "C23    0   51    0   25    0    0    0\n",
              "C24    0    0   20    0   81    0    0\n",
              "C25    0    0    1  104    0    0    0\n",
              "C26    5    0    0    0    0    0   17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1452
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "Wsoo6oreKLDk",
        "outputId": "9ef1770b-2523-45ca-b410-b7a75561be66"
      },
      "source": [
        "contigency_mat=pd.concat([contingency_df2, contingency_df1])\n",
        "print(\"-----------Contrigency matrix----------\")\n",
        "contigency_mat"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-----------Contrigency matrix----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>104</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>139</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C10  C11  C12  C13  C14  C15  C16\n",
              "C20    0   35    0    0    0    0   12\n",
              "C21    0    0  123    0    1   20    0\n",
              "C22    5    0    0    0    0    0    0\n",
              "C23    0   51    0   25    0    0    0\n",
              "C24    0    0   20    0   81    0    0\n",
              "C25    0    0    1  104    0    0    0\n",
              "C26    5    0    0    0    0    0   17\n",
              "C30    0   52    0   32    0    0    0\n",
              "C31    0    0  139    0   82   19    0\n",
              "C32    7    0    0    0    0    0    9\n",
              "C33    3    0    0    0    0    0    0\n",
              "C34    0    0    5   97    0    1    0\n",
              "C35    0   34    0    0    0    0    8\n",
              "C36    0    0    0    0    0    0   12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1453
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "uf5157BcKNah",
        "outputId": "db5569f4-b39b-4bee-aaa6-13baada547e1"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "C1_datapoints={}\n",
        "C2_datapoints={}\n",
        "C3_datapoints={}\n",
        "\n",
        "def clusters(dictionary,clustername):\n",
        "  for i in clustering_result.index:\n",
        "    if clustering_result.loc[i,clustername] not in dictionary.keys():\n",
        "      dictionary[clustering_result.loc[i,clustername]]=[clustering_result.loc[i,'Datapoint(x)']]\n",
        "    else:\n",
        "      dictionary[clustering_result.loc[i,clustername]].append(clustering_result.loc[i,'Datapoint(x)'])\n",
        "  return dictionary\n",
        "\n",
        "C1_datapoints=clusters(C1_datapoints,'C1')\n",
        "C2_datapoints=clusters(C2_datapoints,'C2')\n",
        "C3_datapoints=clusters(C3_datapoints,'C3')\n",
        "\n",
        "\n",
        "\n",
        "def union(list1, list2):\n",
        "  l = list1 + list2\n",
        "  return len(set(l))\n",
        "\n",
        "def common_member(list1,list2):\n",
        "  set1=set(list1)\n",
        "  set2=set(list2)\n",
        "  return len(set1.intersection(set2))\n",
        "\n",
        "\n",
        "union_df1 = []\n",
        "common_df1 = []\n",
        "\n",
        "for i in range(7):\n",
        "  uni = []\n",
        "  common = []\n",
        "  for j in range(7):\n",
        "    union_element = union(C3_datapoints[i],C1_datapoints[j])\n",
        "    common_element = common_member(C3_datapoints[i],C1_datapoints[j])\n",
        "    uni.append(union_element)\n",
        "    common.append(common_element)\n",
        "   \n",
        "  union_df1.append(uni)\n",
        "  common_df1.append(common)\n",
        "\n",
        "confusion_mat1 = np.divide(common_df1, union_df1)\n",
        "confusion_mat1=pd.DataFrame(confusion_mat1,index=['C30','C31','C32','C33','C34','C35','C36'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "common_df1, union_df1\n",
        "confusion_mat1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.440678</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.176796</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.567347</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.341667</td>\n",
              "      <td>0.078838</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020661</td>\n",
              "      <td>0.718519</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008197</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.361702</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.126984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.413793</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11       C12       C13       C14       C15       C16\n",
              "C30  0.000000  0.440678  0.000000  0.176796  0.000000  0.000000  0.000000\n",
              "C31  0.000000  0.000000  0.567347  0.000000  0.341667  0.078838  0.000000\n",
              "C32  0.368421  0.000000  0.000000  0.000000  0.000000  0.000000  0.250000\n",
              "C33  0.300000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n",
              "C34  0.000000  0.000000  0.020661  0.718519  0.000000  0.008197  0.000000\n",
              "C35  0.000000  0.361702  0.000000  0.000000  0.000000  0.000000  0.126984\n",
              "C36  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.413793"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1454
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "oIDbQ2vdKPyi",
        "outputId": "845a2658-5ff1-4073-f4c5-6f2cc7a92dfd"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "C1_datapoints={}\n",
        "C2_datapoints={}\n",
        "C3_datapoints={}\n",
        "\n",
        "def clusters(dictionary,clustername):\n",
        "  for i in clustering_result.index:\n",
        "    if clustering_result.loc[i,clustername] not in dictionary.keys():\n",
        "      dictionary[clustering_result.loc[i,clustername]]=[clustering_result.loc[i,'Datapoint(x)']]\n",
        "    else:\n",
        "      dictionary[clustering_result.loc[i,clustername]].append(clustering_result.loc[i,'Datapoint(x)'])\n",
        "  return dictionary\n",
        "\n",
        "C1_datapoints=clusters(C1_datapoints,'C1')\n",
        "C2_datapoints=clusters(C2_datapoints,'C2')\n",
        "C3_datapoints=clusters(C3_datapoints,'C3')\n",
        "\n",
        "\n",
        "union_df2 = []\n",
        "common_df2 = []\n",
        "\n",
        "for i in range(7):\n",
        "  uni = []\n",
        "  common = []\n",
        "  for j in range(7):\n",
        "    union_element = union(C2_datapoints[i],C1_datapoints[j])\n",
        "    common_element = common_member(C2_datapoints[i],C1_datapoints[j])\n",
        "    uni.append(union_element)\n",
        "    common.append(common_element)\n",
        "   \n",
        "  union_df2.append(uni)\n",
        "  common_df2.append(common)\n",
        "\n",
        "confusion_mat2 = np.divide(common_df2, union_df2)\n",
        "\n",
        "confusion_mat2=pd.DataFrame(confusion_mat2,index=['C20','C21','C22','C23','C24','C25','C26'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "common_df2, union_df2\n",
        "confusion_mat2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.745455</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.459459</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.088889</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.794118</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004032</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>0.185185</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11       C12       C13       C14       C15     C16\n",
              "C20  0.000000  0.357143  0.000000  0.000000  0.000000  0.000000  0.1875\n",
              "C21  0.000000  0.000000  0.745455  0.000000  0.004444  0.138889  0.0000\n",
              "C22  0.500000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0000\n",
              "C23  0.000000  0.459459  0.000000  0.138889  0.000000  0.000000  0.0000\n",
              "C24  0.000000  0.000000  0.088889  0.000000  0.794118  0.000000  0.0000\n",
              "C25  0.000000  0.000000  0.004032  0.800000  0.000000  0.000000  0.0000\n",
              "C26  0.185185  0.000000  0.000000  0.000000  0.000000  0.000000  0.5000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1455
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "gY0bKAGuKSE0",
        "outputId": "b7839749-857c-4199-d5f0-00b67ee46bf5"
      },
      "source": [
        "confusion_mat=pd.concat([confusion_mat2, confusion_mat1])\n",
        "print(\"-----------Confusion matrix----------\")\n",
        "confusion_mat"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-----------Confusion matrix----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.187500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.745455</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.459459</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.088889</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.794118</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004032</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>0.185185</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.440678</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.176796</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.567347</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.341667</td>\n",
              "      <td>0.078838</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020661</td>\n",
              "      <td>0.718519</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008197</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.361702</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.126984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.413793</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11       C12       C13       C14       C15       C16\n",
              "C20  0.000000  0.357143  0.000000  0.000000  0.000000  0.000000  0.187500\n",
              "C21  0.000000  0.000000  0.745455  0.000000  0.004444  0.138889  0.000000\n",
              "C22  0.500000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n",
              "C23  0.000000  0.459459  0.000000  0.138889  0.000000  0.000000  0.000000\n",
              "C24  0.000000  0.000000  0.088889  0.000000  0.794118  0.000000  0.000000\n",
              "C25  0.000000  0.000000  0.004032  0.800000  0.000000  0.000000  0.000000\n",
              "C26  0.185185  0.000000  0.000000  0.000000  0.000000  0.000000  0.500000\n",
              "C30  0.000000  0.440678  0.000000  0.176796  0.000000  0.000000  0.000000\n",
              "C31  0.000000  0.000000  0.567347  0.000000  0.341667  0.078838  0.000000\n",
              "C32  0.368421  0.000000  0.000000  0.000000  0.000000  0.000000  0.250000\n",
              "C33  0.300000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n",
              "C34  0.000000  0.000000  0.020661  0.718519  0.000000  0.008197  0.000000\n",
              "C35  0.000000  0.361702  0.000000  0.000000  0.000000  0.000000  0.126984\n",
              "C36  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.413793"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1456
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gm_E9FMeKUR5",
        "outputId": "99c2f3c8-6087-4502-d05a-9642fbb9c544"
      },
      "source": [
        "CCluster = []\n",
        "CLabel = ['C10', 'C11',\t'C12', 'C13',\t'C14', 'C15',\t'C16']\n",
        "\n",
        "for i in range(len(confusion_mat)):\n",
        "  l =confusion_mat.iloc[i]\n",
        "  l = list(l)\n",
        "  ind = l.index(max(l))\n",
        "  CCluster.append(CLabel[ind])\n",
        "CCluster"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['C11',\n",
              " 'C12',\n",
              " 'C10',\n",
              " 'C11',\n",
              " 'C14',\n",
              " 'C13',\n",
              " 'C16',\n",
              " 'C11',\n",
              " 'C12',\n",
              " 'C10',\n",
              " 'C10',\n",
              " 'C13',\n",
              " 'C11',\n",
              " 'C16']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1457
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "fr35OlX0KWSD",
        "outputId": "9c00c852-2a6b-4880-be37-1dec4782d492"
      },
      "source": [
        "confusion_matrix = confusion_mat\n",
        "confusion_matrix['CCluster'] = CCluster\n",
        "confusion_matrix.tail(8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "      <th>CCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>0.185185</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>C16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.440678</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.176796</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.567347</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.341667</td>\n",
              "      <td>0.078838</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>C10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020661</td>\n",
              "      <td>0.718519</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008197</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.361702</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.126984</td>\n",
              "      <td>C11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.413793</td>\n",
              "      <td>C16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11       C12  ...       C15       C16  CCluster\n",
              "C26  0.185185  0.000000  0.000000  ...  0.000000  0.500000       C16\n",
              "C30  0.000000  0.440678  0.000000  ...  0.000000  0.000000       C11\n",
              "C31  0.000000  0.000000  0.567347  ...  0.078838  0.000000       C12\n",
              "C32  0.368421  0.000000  0.000000  ...  0.000000  0.250000       C10\n",
              "C33  0.300000  0.000000  0.000000  ...  0.000000  0.000000       C10\n",
              "C34  0.000000  0.000000  0.020661  ...  0.008197  0.000000       C13\n",
              "C35  0.000000  0.361702  0.000000  ...  0.000000  0.126984       C11\n",
              "C36  0.000000  0.000000  0.000000  ...  0.000000  0.413793       C16\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1458
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BPBNTagKZV6",
        "outputId": "803365ef-43c0-4be3-e236-cfcab858cc9a"
      },
      "source": [
        "clustering_result['C1'].values, clustering_result['C2'].values, clustering_result['C3'].values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([2, 3, 4, 5, 2, 4, 3, 6, 2, 1, 1, 4, 2, 3, 2, 4, 1, 4, 3, 4, 2, 4,\n",
              "        4, 2, 5, 3, 3, 2, 6, 2, 1, 4, 1, 5, 2, 1, 2, 4, 3, 1, 1, 6, 2, 2,\n",
              "        2, 2, 2, 2, 5, 3, 2, 1, 2, 3, 2, 2, 1, 3, 3, 6, 2, 3, 3, 4, 4, 3,\n",
              "        2, 1, 2, 1, 2, 3, 2, 2, 2, 3, 2, 3, 4, 1, 3, 2, 3, 1, 3, 3, 4, 6,\n",
              "        4, 2, 3, 6, 2, 2, 1, 4, 2, 6, 2, 0, 3, 1, 3, 4, 3, 6, 2, 4, 4, 2,\n",
              "        3, 5, 3, 3, 2, 4, 2, 3, 2, 2, 4, 2, 3, 2, 2, 2, 4, 2, 3, 1, 2, 3,\n",
              "        2, 4, 4, 1, 5, 1, 3, 2, 2, 0, 3, 3, 4, 3, 2, 2, 3, 1, 4, 0, 0, 2,\n",
              "        2, 4, 3, 2, 2, 2, 0, 1, 2, 4, 3, 4, 2, 3, 4, 3, 2, 3, 1, 4, 6, 1,\n",
              "        1, 1, 0, 3, 3, 3, 4, 1, 6, 4, 3, 4, 1, 1, 2, 1, 3, 3, 3, 1, 6, 2,\n",
              "        1, 4, 3, 4, 3, 2, 3, 6, 5, 1, 3, 2, 4, 2, 1, 3, 5, 3, 2, 4, 2, 3,\n",
              "        2, 3, 4, 2, 6, 3, 2, 3, 3, 3, 3, 1, 3, 3, 3, 3, 2, 1, 3, 1, 3, 3,\n",
              "        1, 0, 3, 6, 1, 2, 4, 2, 1, 2, 2, 1, 6, 3, 2, 2, 1, 3, 3, 2, 5, 1,\n",
              "        5, 3, 3, 4, 2, 4, 4, 2, 2, 3, 1, 3, 3, 3, 3, 6, 5, 3, 4, 3, 3, 3,\n",
              "        2, 3, 2, 4, 1, 2, 3, 1, 4, 4, 2, 4, 2, 4, 1, 3, 4, 4, 3, 1, 0, 6,\n",
              "        2, 4, 3, 2, 2, 5, 4, 2, 3, 1, 3, 2, 3, 2, 2, 1, 3, 4, 6, 2, 2, 3,\n",
              "        4, 2, 6, 3, 1, 5, 4, 2, 3, 3, 1, 2, 1, 2, 3, 5, 3, 2, 2, 6, 3, 3,\n",
              "        1, 3, 4, 2, 3, 4, 3, 3, 6, 2, 4, 2, 2, 3, 3, 2, 3, 2, 1, 1, 2, 4,\n",
              "        3, 1, 6, 3, 1, 4, 2, 4, 2, 2, 2, 5, 1, 3, 6, 2, 5, 1, 2, 5, 4, 4,\n",
              "        3, 6, 1, 1, 2, 2, 4, 2, 1, 2, 4, 1, 2, 3, 4, 4, 4, 2, 3, 2, 1, 2,\n",
              "        2, 3, 3, 3, 4, 5, 1, 0, 2, 3, 1, 3, 4, 1, 3, 1, 4, 4, 1, 1, 1, 1,\n",
              "        2, 3, 6, 2, 2, 6, 3, 3, 4, 6, 4, 2, 1, 2, 3, 2, 5, 1, 4, 1, 5, 3,\n",
              "        2, 6, 0, 4, 6, 2, 2, 1, 2, 1, 1, 4, 2, 1, 4, 4, 2, 1, 2, 2, 1, 2,\n",
              "        1, 3, 1, 1, 3, 3, 3, 2, 4, 2, 2, 2, 1, 4, 1, 2], dtype=int32),\n",
              " array([1, 5, 4, 1, 4, 4, 5, 0, 4, 3, 3, 4, 4, 5, 1, 4, 0, 4, 3, 4, 4, 4,\n",
              "        4, 1, 1, 5, 5, 1, 6, 1, 3, 4, 3, 1, 1, 3, 1, 4, 5, 3, 3, 6, 1, 1,\n",
              "        1, 1, 1, 1, 1, 5, 1, 3, 1, 5, 1, 1, 0, 5, 5, 0, 1, 5, 5, 4, 4, 5,\n",
              "        1, 0, 4, 3, 1, 5, 1, 1, 1, 5, 1, 3, 4, 3, 3, 1, 5, 3, 5, 5, 4, 6,\n",
              "        4, 4, 5, 6, 1, 1, 3, 4, 1, 0, 4, 2, 5, 3, 5, 4, 3, 6, 1, 4, 4, 1,\n",
              "        5, 1, 5, 5, 1, 4, 1, 3, 1, 1, 4, 1, 3, 1, 1, 4, 4, 1, 5, 0, 1, 5,\n",
              "        1, 4, 4, 3, 1, 0, 5, 5, 1, 2, 5, 5, 4, 5, 1, 1, 5, 0, 4, 2, 6, 4,\n",
              "        1, 4, 5, 1, 1, 1, 6, 3, 1, 4, 5, 4, 1, 5, 4, 5, 4, 5, 3, 4, 6, 3,\n",
              "        0, 3, 2, 5, 5, 5, 4, 3, 0, 4, 5, 4, 3, 3, 1, 3, 5, 3, 5, 3, 0, 1,\n",
              "        0, 4, 5, 4, 5, 4, 5, 6, 1, 3, 5, 1, 4, 1, 3, 3, 1, 5, 1, 4, 1, 5,\n",
              "        1, 5, 4, 4, 6, 5, 1, 5, 3, 5, 3, 0, 5, 5, 5, 5, 1, 0, 5, 3, 5, 5,\n",
              "        0, 6, 3, 6, 3, 1, 4, 1, 0, 1, 1, 0, 6, 5, 1, 1, 0, 5, 5, 1, 1, 3,\n",
              "        1, 5, 5, 4, 1, 4, 4, 1, 4, 5, 3, 5, 5, 5, 5, 0, 1, 3, 4, 5, 5, 5,\n",
              "        1, 5, 1, 4, 3, 1, 3, 3, 4, 4, 4, 4, 1, 4, 3, 5, 4, 4, 5, 3, 2, 6,\n",
              "        1, 4, 5, 1, 1, 1, 4, 1, 5, 0, 5, 1, 5, 1, 1, 0, 5, 4, 0, 1, 1, 5,\n",
              "        4, 1, 6, 5, 3, 1, 4, 1, 5, 3, 0, 1, 3, 1, 5, 1, 5, 4, 1, 0, 5, 5,\n",
              "        0, 3, 4, 1, 3, 4, 5, 3, 0, 1, 4, 1, 1, 5, 5, 1, 5, 1, 3, 3, 1, 4,\n",
              "        5, 3, 6, 5, 3, 4, 1, 4, 1, 1, 1, 1, 3, 3, 0, 1, 1, 3, 4, 1, 4, 4,\n",
              "        5, 6, 0, 0, 1, 1, 4, 1, 0, 1, 4, 3, 1, 5, 4, 4, 4, 1, 5, 1, 0, 1,\n",
              "        4, 5, 5, 3, 4, 1, 0, 6, 1, 3, 3, 5, 1, 3, 5, 0, 4, 4, 3, 0, 3, 0,\n",
              "        1, 5, 0, 1, 1, 6, 5, 3, 4, 0, 4, 1, 0, 4, 3, 1, 1, 0, 4, 0, 1, 3,\n",
              "        1, 6, 6, 4, 6, 1, 1, 0, 1, 0, 3, 4, 4, 0, 4, 4, 1, 0, 4, 1, 3, 1,\n",
              "        0, 3, 3, 3, 3, 5, 5, 1, 4, 1, 1, 1, 0, 4, 3, 1], dtype=int32),\n",
              " array([1, 4, 1, 1, 1, 1, 4, 6, 1, 0, 0, 1, 1, 4, 1, 1, 5, 1, 0, 1, 1, 1,\n",
              "        1, 1, 1, 4, 4, 1, 2, 1, 0, 1, 0, 1, 1, 0, 1, 1, 4, 0, 0, 6, 1, 4,\n",
              "        1, 1, 1, 1, 1, 4, 1, 0, 1, 4, 1, 1, 5, 4, 4, 6, 1, 4, 4, 1, 1, 4,\n",
              "        1, 5, 1, 0, 1, 4, 1, 1, 1, 4, 1, 0, 1, 0, 0, 1, 4, 0, 4, 4, 1, 2,\n",
              "        1, 1, 4, 6, 1, 1, 0, 1, 1, 5, 1, 2, 0, 0, 4, 1, 0, 6, 1, 1, 1, 1,\n",
              "        4, 1, 4, 4, 1, 1, 1, 0, 4, 1, 1, 1, 0, 1, 1, 1, 1, 1, 4, 5, 1, 4,\n",
              "        1, 1, 1, 0, 1, 5, 4, 4, 1, 3, 4, 4, 1, 4, 1, 1, 4, 5, 1, 3, 2, 1,\n",
              "        1, 1, 4, 1, 1, 1, 2, 0, 1, 1, 4, 1, 1, 4, 1, 4, 1, 4, 0, 1, 2, 0,\n",
              "        5, 0, 3, 4, 4, 4, 1, 0, 5, 1, 4, 1, 0, 0, 1, 0, 0, 0, 4, 0, 5, 1,\n",
              "        5, 1, 4, 1, 4, 1, 4, 6, 1, 0, 4, 4, 1, 1, 0, 0, 1, 4, 1, 1, 1, 4,\n",
              "        1, 4, 1, 1, 2, 4, 1, 4, 0, 4, 0, 5, 4, 4, 4, 4, 1, 5, 4, 0, 4, 4,\n",
              "        5, 2, 0, 6, 0, 1, 1, 1, 5, 1, 1, 5, 2, 4, 1, 1, 5, 4, 4, 1, 1, 0,\n",
              "        1, 4, 4, 1, 1, 1, 1, 1, 1, 4, 0, 4, 0, 4, 4, 5, 1, 0, 1, 4, 4, 4,\n",
              "        1, 4, 1, 1, 0, 4, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 4, 0, 2, 6,\n",
              "        1, 1, 4, 1, 1, 1, 1, 1, 4, 5, 4, 1, 4, 1, 1, 5, 4, 1, 5, 1, 1, 4,\n",
              "        1, 1, 2, 4, 0, 1, 1, 1, 4, 0, 5, 1, 0, 1, 4, 1, 4, 1, 1, 5, 4, 4,\n",
              "        5, 0, 1, 1, 0, 1, 4, 0, 5, 1, 1, 1, 1, 4, 4, 1, 4, 1, 0, 0, 1, 1,\n",
              "        4, 0, 2, 4, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 6, 1, 4, 0, 1, 1, 1, 1,\n",
              "        4, 6, 5, 5, 1, 1, 1, 1, 5, 1, 1, 0, 1, 4, 1, 1, 1, 1, 0, 1, 5, 1,\n",
              "        1, 4, 4, 0, 1, 1, 0, 2, 1, 0, 0, 0, 1, 0, 0, 5, 1, 1, 0, 5, 0, 5,\n",
              "        1, 4, 6, 1, 1, 6, 4, 0, 1, 5, 1, 1, 5, 1, 0, 1, 1, 5, 1, 5, 1, 0,\n",
              "        1, 2, 2, 1, 2, 1, 1, 5, 1, 5, 0, 1, 1, 5, 1, 1, 1, 5, 1, 1, 0, 1,\n",
              "        5, 0, 0, 0, 0, 4, 4, 1, 1, 1, 1, 1, 5, 1, 0, 1], dtype=int32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1459
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTI7d914KbJv"
      },
      "source": [
        "# C2 = [5, 1, 5, 5, 4, 6, 5, 4, 5, 5, 4, 5, 4, 5, 5, 6, 6, 6, 6, 5, 5, 4, 5, 5, 4, 5, 4, 5, 2, 6, 2, 2, 5, 2, 4, 5, 1, 5, 4, 5]\n",
        "# C3 = [5, 1, 5, 5, 4, 6, 5, 4, 5, 5, 4, 5, 4, 5, 5, 6, 6, 6, 6, 5, 5, 4, 5, 5, 4, 5, 4, 5, 2, 6, 2, 2, 5, 2, 4, 5, 1, 5, 4, 5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698
        },
        "id": "z2Hc2fVAKfIR",
        "outputId": "ea12c102-0cbe-477e-a439-5dc3225cf8fd"
      },
      "source": [
        "#Relabeling\n",
        "new_clustering_result = clustering_result\n",
        "new_clustering_result['C2'] = clustering_result['C2'].values\n",
        "new_clustering_result['C3'] = clustering_result['C3'].values\n",
        "new_clustering_result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Datapoint(x)</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</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>495</th>\n",
              "      <td>495</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>496</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>497</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>498</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>499</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C1  C2  C3\n",
              "0               0   2   1   1\n",
              "1               1   3   5   4\n",
              "2               2   4   4   1\n",
              "3               3   5   1   1\n",
              "4               4   2   4   1\n",
              "..            ...  ..  ..  ..\n",
              "495           495   2   1   1\n",
              "496           496   1   0   5\n",
              "497           497   4   4   1\n",
              "498           498   1   3   0\n",
              "499           499   2   1   1\n",
              "\n",
              "[500 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1461
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "dnw7lIRpMU92",
        "outputId": "e889f7a9-6326-48c4-c413-1ed202ae56bd"
      },
      "source": [
        "#Majority Voting\n",
        "\n",
        "C = []\n",
        "for i in range(len(new_clustering_result)):\n",
        "  l = new_clustering_result.iloc[i]\n",
        "  l = list(l[1:])\n",
        "  majority_label = max(set(l), key = l.count)\n",
        "  C.append(majority_label)\n",
        "\n",
        "new_clustering_result['C'] = C\n",
        "new_clustering_result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Datapoint(x)</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>495</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>496</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>497</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>498</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>499</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C1  C2  C3  C\n",
              "0               0   2   1   1  1\n",
              "1               1   3   5   4  3\n",
              "2               2   4   4   1  4\n",
              "3               3   5   1   1  1\n",
              "4               4   2   4   1  1\n",
              "..            ...  ..  ..  .. ..\n",
              "495           495   2   1   1  1\n",
              "496           496   1   0   5  0\n",
              "497           497   4   4   1  4\n",
              "498           498   1   3   0  0\n",
              "499           499   2   1   1  1\n",
              "\n",
              "[500 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1462
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "Qz4N10kPMdhy",
        "outputId": "1ef8b5a5-bc60-47c7-f27b-ad663b60b0e0"
      },
      "source": [
        "consensus_clustering_result=new_clustering_result.drop(['C1', 'C2', 'C3'], axis = 1)\n",
        "consensus_clustering_result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Datapoint(x)</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>495</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>496</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>497</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>498</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>499</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C\n",
              "0               0  1\n",
              "1               1  3\n",
              "2               2  4\n",
              "3               3  1\n",
              "4               4  1\n",
              "..            ... ..\n",
              "495           495  1\n",
              "496           496  0\n",
              "497           497  4\n",
              "498           498  0\n",
              "499           499  1\n",
              "\n",
              "[500 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1463
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "S_hT9r3NMozr",
        "outputId": "8e06599c-c92a-4e49-e646-afdf5dc91fd6"
      },
      "source": [
        "colon_majority.rename(columns={'index': 'Datapoint(x)'}, inplace=True)\n",
        "colon_majority=colon_majority.merge(consensus_clustering_result,on='Datapoint(x)')\n",
        "colon_majority=colon_majority.drop(columns=['C1','C2','C3'])\n",
        "colon_majority"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>Datapoint(x)</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>4</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>495</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>496</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>497</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>498</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>499</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  ...  Outcome  Datapoint(x)  C\n",
              "0              1       85             66  ...        0             0  1\n",
              "1              1       89             66  ...        0             1  3\n",
              "2              5      116             74  ...        0             2  4\n",
              "3             10      115              0  ...        0             3  1\n",
              "4              4      110             92  ...        0             4  1\n",
              "..           ...      ...            ...  ...      ...           ... ..\n",
              "495            9       89             62  ...        0           495  1\n",
              "496           10      101             76  ...        0           496  0\n",
              "497            2      122             70  ...        0           497  4\n",
              "498            5      121             72  ...        0           498  0\n",
              "499            1       93             70  ...        0           499  1\n",
              "\n",
              "[500 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1464
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJVCphT0M0bk",
        "outputId": "4e9d7dfe-a11f-4f49-c0cb-19a888aa6234"
      },
      "source": [
        "cluster_0 = colon_majority[colon_majority['C']==0]\n",
        "cluster_0.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(109, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1465
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVXsopE1M23Q",
        "outputId": "cb6f69d2-99dc-47e5-9ecc-26fa68be1ef2"
      },
      "source": [
        "cluster_1 = colon_majority[colon_majority['C']==1]\n",
        "cluster_1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(164, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1466
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cau-797TM5wY",
        "outputId": "9de4db4e-3377-488c-d8cf-7ba649cdfe10"
      },
      "source": [
        "cluster_2 = colon_majority[colon_majority['C']==2]\n",
        "cluster_2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1467
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6o5u2K8M-CS",
        "outputId": "eaf0d4e0-d8d8-4926-a963-d6328760f351"
      },
      "source": [
        "cluster_3 = colon_majority[colon_majority['C']==3]\n",
        "cluster_3.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(122, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1468
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKw98d1oNAac",
        "outputId": "af430ddb-8223-4c24-89d1-189801c16c5b"
      },
      "source": [
        "cluster_4 = colon_majority[colon_majority['C']==4]\n",
        "cluster_4.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(81, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1469
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVIo0Dg-NC0h",
        "outputId": "071f4fec-e1e2-4063-d13c-f8de78308c23"
      },
      "source": [
        "cluster_5 = colon_majority[colon_majority['C']==5]\n",
        "cluster_5.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1470
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7UlgdSnNFgI",
        "outputId": "b5da1df5-7a1a-4fa8-d6dd-004b3e98a4d3"
      },
      "source": [
        "cluster_6 = colon_majority[colon_majority['C']==6]\n",
        "cluster_6.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1471
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kieCRESJfL70",
        "outputId": "e854cb9f-c5d7-4241-e4a0-81c61f318466"
      },
      "source": [
        "len(cluster_0.values[0][1:-2])\n",
        "centroid_3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.5245901639344264,\n",
              " 97.55737704918033,\n",
              " 68.17213114754098,\n",
              " 25.688524590163933,\n",
              " 69.63934426229508,\n",
              " 30.29098360655739,\n",
              " 0.47222131147540974,\n",
              " 27.368852459016395]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1515
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WT5MO3Q0Us4g",
        "outputId": "c43a7507-71af-4e9e-83bd-bec0150b2a68"
      },
      "source": [
        "cluster_0.shape, cluster_1.shape, cluster_2.shape, cluster_3.shape, cluster_4.shape, cluster_5.shape, cluster_6.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((109, 11), (164, 11), (3, 11), (122, 11), (81, 11), (0, 11), (21, 11))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1472
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "vi9lE9RdNHmI",
        "outputId": "97aeda17-12a8-4b43-c242-a3365553caf3"
      },
      "source": [
        "#centroid of different clusters\n",
        "\n",
        "from sklearn.neighbors import NearestCentroid\n",
        "colon_majority\n",
        "X=colon_majority.drop(columns=['Datapoint(x)',target,'C'])\n",
        "y=colon_majority.C\n",
        "clf=NearestCentroid()\n",
        "clf.fit(X,y)\n",
        "data_centroids=clf.centroids_\n",
        "pd.DataFrame(data_centroids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.981651</td>\n",
              "      <td>123.853211</td>\n",
              "      <td>69.495413</td>\n",
              "      <td>28.871560</td>\n",
              "      <td>172.229358</td>\n",
              "      <td>32.363303</td>\n",
              "      <td>0.466000</td>\n",
              "      <td>29.486239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.585366</td>\n",
              "      <td>93.762195</td>\n",
              "      <td>61.426829</td>\n",
              "      <td>13.097561</td>\n",
              "      <td>1.060976</td>\n",
              "      <td>28.102439</td>\n",
              "      <td>0.357171</td>\n",
              "      <td>30.695122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>126.333333</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>36.666667</td>\n",
              "      <td>332.333333</td>\n",
              "      <td>38.633333</td>\n",
              "      <td>0.441333</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.524590</td>\n",
              "      <td>97.557377</td>\n",
              "      <td>68.172131</td>\n",
              "      <td>25.688525</td>\n",
              "      <td>69.639344</td>\n",
              "      <td>30.290984</td>\n",
              "      <td>0.472221</td>\n",
              "      <td>27.368852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.530864</td>\n",
              "      <td>135.987654</td>\n",
              "      <td>78.444444</td>\n",
              "      <td>8.617284</td>\n",
              "      <td>0.271605</td>\n",
              "      <td>30.316049</td>\n",
              "      <td>0.422617</td>\n",
              "      <td>40.950617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.761905</td>\n",
              "      <td>134.142857</td>\n",
              "      <td>73.809524</td>\n",
              "      <td>28.333333</td>\n",
              "      <td>282.571429</td>\n",
              "      <td>35.652381</td>\n",
              "      <td>0.587143</td>\n",
              "      <td>29.761905</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0           1          2  ...          5         6          7\n",
              "0  2.981651  123.853211  69.495413  ...  32.363303  0.466000  29.486239\n",
              "1  3.585366   93.762195  61.426829  ...  28.102439  0.357171  30.695122\n",
              "2  1.000000  126.333333  74.000000  ...  38.633333  0.441333  22.000000\n",
              "3  2.524590   97.557377  68.172131  ...  30.290984  0.472221  27.368852\n",
              "4  4.530864  135.987654  78.444444  ...  30.316049  0.422617  40.950617\n",
              "5  2.761905  134.142857  73.809524  ...  35.652381  0.587143  29.761905\n",
              "\n",
              "[6 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1498
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIMU4IhQNSwx",
        "outputId": "0d851f42-499b-4e13-95ca-86aa635edccd"
      },
      "source": [
        "datapoints5 = cluster_6\n",
        "datapoints5.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1545
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2Zqx9d8NU9T",
        "outputId": "9d611f48-460f-4cf7-fc86-aeb92486d341"
      },
      "source": [
        "#Finding 61 datapoints in cluster 4\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_3 = list(data_centroids[2])\n",
        "\n",
        "\n",
        "l=list(cluster_3.values[0])\n",
        "l=l[1:-3]\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_3)):\n",
        "  l=list(cluster_3.values[i])\n",
        "  l=l[1:-3]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_3[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_3_with_distance = cluster_3\n",
        "cluster_3_with_distance['Distance from centroid'] = distances\n",
        "cluster_3_with_distance = cluster_3_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_3_with_distance\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_O5Rf8gYP_N4",
        "outputId": "3ac1dd34-ae9c-4bb8-f5e9-5ab8bf2f21e1"
      },
      "source": [
        "datapoints3 = cluster_4_with_distance[:61]\n",
        "datapoints3.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1533
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CozfEEh1QB82",
        "outputId": "f993f111-1034-454f-f033-8e669ed95fd7"
      },
      "source": [
        "#Finding 61 datapoints in cluster 2\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_1 = list(data_centroids[1])\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_1)):\n",
        "  l=list(cluster_1.values[i])\n",
        "  l=l[1:-3]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_1[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_1_with_distance = cluster_1\n",
        "cluster_1_with_distance['Distance from centroid'] = distances\n",
        "cluster_1_with_distance = cluster_1_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_1_with_distance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNuzp05vQnyb",
        "outputId": "e8283202-5760-45bd-deff-1008edc83891"
      },
      "source": [
        "datapoints2 = cluster_1_with_distance[:61]\n",
        "datapoints2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1538
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T41-e0mGRsNf",
        "outputId": "ce53273a-8af4-4fee-e468-3ff37dbf1877"
      },
      "source": [
        "#Finding 61 datapoints in cluster 1\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_0 = list(data_centroids[0])\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_0)):\n",
        "  l=list(cluster_0.values[i])\n",
        "  l=l[1:-3]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_0[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_0_with_distance = cluster_0\n",
        "cluster_0_with_distance['Distance from centroid'] = distances\n",
        "cluster_0_with_distance = cluster_0_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_0_with_distance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjiAHvGJR6Zy",
        "outputId": "b90ca3e3-bb81-4eb0-fb21-fd059d35f56e"
      },
      "source": [
        "datapoints1 = cluster_0_with_distance[:61]\n",
        "datapoints1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1540
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIAxPhEER80o",
        "outputId": "3916d1f6-7926-4035-c89e-1ee36290a253"
      },
      "source": [
        "#Finding 61 datapoints in cluster 5\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_4 = list(data_centroids[4])\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_4)):\n",
        "  l=list(cluster_4.values[i])\n",
        "  l=l[1:-3]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_4[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_4_with_distance = cluster_4\n",
        "cluster_4_with_distance['Distance from centroid'] = distances\n",
        "cluster_4_with_distance = cluster_4_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_4_with_distance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfYsbmL8R89O",
        "outputId": "7b538657-2e7f-4d65-cf03-0be8501f22b6"
      },
      "source": [
        "datapoints4 = cluster_4_with_distance[:61]\n",
        "datapoints4.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1541
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXhsXhvejzFj",
        "outputId": "8f42283c-a1c0-4340-bb45-cebd413652bc"
      },
      "source": [
        "datapoints1.shape, datapoints2.shape, datapoints3.shape, datapoints4.shape, datapoints5.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((61, 12), (61, 12), (61, 12), (61, 12), (21, 11))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1551
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "YV3a7jqvYSdO",
        "outputId": "c353348e-4217-412b-bb47-47a6bccb9f6b"
      },
      "source": [
        "balanced_dataset_with_class0=pd.concat([datapoints1, datapoints2, datapoints3, datapoints4, datapoints5])\n",
        "balanced_dataset_with_class0 = balanced_dataset_with_class0.drop(['C','Distance from centroid', 'Datapoint(x)'], axis=1)\n",
        "#balanced_dataset_with_class0=balanced_dataset_with_class0.drop([target], axis=1)\n",
        "#balanced_dataset_with_class0['diagnosis'] =  [0]*len(balanced_dataset_with_class0)\n",
        "balanced_dataset_with_class0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>88</td>\n",
              "      <td>60</td>\n",
              "      <td>110</td>\n",
              "      <td>46.8</td>\n",
              "      <td>0.962</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>0</td>\n",
              "      <td>95</td>\n",
              "      <td>80</td>\n",
              "      <td>45</td>\n",
              "      <td>92</td>\n",
              "      <td>36.5</td>\n",
              "      <td>0.330</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>0</td>\n",
              "      <td>102</td>\n",
              "      <td>78</td>\n",
              "      <td>40</td>\n",
              "      <td>90</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0.238</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>407</th>\n",
              "      <td>0</td>\n",
              "      <td>94</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>115</td>\n",
              "      <td>43.5</td>\n",
              "      <td>0.347</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>0</td>\n",
              "      <td>84</td>\n",
              "      <td>82</td>\n",
              "      <td>31</td>\n",
              "      <td>125</td>\n",
              "      <td>38.2</td>\n",
              "      <td>0.233</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>397</th>\n",
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              "      <td>41.5</td>\n",
              "      <td>0.270</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>442</th>\n",
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              "      <td>123</td>\n",
              "      <td>100</td>\n",
              "      <td>35</td>\n",
              "      <td>240</td>\n",
              "      <td>57.3</td>\n",
              "      <td>0.880</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>445</th>\n",
              "      <td>2</td>\n",
              "      <td>101</td>\n",
              "      <td>58</td>\n",
              "      <td>17</td>\n",
              "      <td>265</td>\n",
              "      <td>24.2</td>\n",
              "      <td>0.614</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>2</td>\n",
              "      <td>127</td>\n",
              "      <td>46</td>\n",
              "      <td>21</td>\n",
              "      <td>335</td>\n",
              "      <td>34.4</td>\n",
              "      <td>0.176</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466</th>\n",
              "      <td>0</td>\n",
              "      <td>134</td>\n",
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              "      <td>20</td>\n",
              "      <td>291</td>\n",
              "      <td>26.4</td>\n",
              "      <td>0.352</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>265 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  ...  Age  Outcome\n",
              "30             0      100  ...   31        0\n",
              "276            0       95  ...   26        0\n",
              "414            0      102  ...   24        0\n",
              "407            0       94  ...   21        0\n",
              "305            0       84  ...   23        0\n",
              "..           ...      ...  ...  ...      ...\n",
              "397            0      152  ...   27        0\n",
              "442            3      123  ...   22        0\n",
              "445            2      101  ...   23        0\n",
              "463            2      127  ...   22        0\n",
              "466            0      134  ...   21        0\n",
              "\n",
              "[265 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1563
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "zbm6os9aaVB7",
        "outputId": "94528e6f-da53-4592-9adf-ea79f1688c67"
      },
      "source": [
        "og_dataset = pd.read_csv(\"https://raw.githubusercontent.com/arghac14/UndErNsembled/master/Datasets/pima-diabetes.csv\")\n",
        "minority_dataset = og_dataset[og_dataset[target]==1]\n",
        "minority_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<p>268 rows × 9 columns</p>\n",
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            "text/plain": [
              "     Pregnancies  Glucose  ...  Age  Outcome\n",
              "0              6      148  ...   50        1\n",
              "2              8      183  ...   32        1\n",
              "4              0      137  ...   33        1\n",
              "6              3       78  ...   26        1\n",
              "8              2      197  ...   53        1\n",
              "..           ...      ...  ...  ...      ...\n",
              "755            1      128  ...   37        1\n",
              "757            0      123  ...   52        1\n",
              "759            6      190  ...   66        1\n",
              "761            9      170  ...   43        1\n",
              "766            1      126  ...   47        1\n",
              "\n",
              "[268 rows x 9 columns]"
            ]
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          "metadata": {
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          "execution_count": 1564
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "L-EF76WxandF",
        "outputId": "36f29a0f-2942-4314-8a1a-09875233c9da"
      },
      "source": [
        "balanced_dataset = pd.concat([balanced_dataset_with_class0, minority_dataset])\n",
        "balanced_dataset = balanced_dataset.sample(frac=1)\n",
        "balanced_dataset = balanced_dataset.reset_index(drop=True)\n",
        "balanced_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0.212</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>13</td>\n",
              "      <td>129</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>39.9</td>\n",
              "      <td>0.569</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>7</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.484</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531</th>\n",
              "      <td>4</td>\n",
              "      <td>95</td>\n",
              "      <td>60</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>35.4</td>\n",
              "      <td>0.284</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>532</th>\n",
              "      <td>3</td>\n",
              "      <td>103</td>\n",
              "      <td>72</td>\n",
              "      <td>30</td>\n",
              "      <td>152</td>\n",
              "      <td>27.6</td>\n",
              "      <td>0.730</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>533 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  ...  Age  Outcome\n",
              "0              4      127  ...   28        0\n",
              "1              0       84  ...   23        0\n",
              "2              0      125  ...   21        0\n",
              "3              4      129  ...   23        0\n",
              "4              3      129  ...   32        1\n",
              "..           ...      ...  ...  ...      ...\n",
              "528           14      175  ...   38        1\n",
              "529           13      129  ...   44        1\n",
              "530            7      100  ...   32        1\n",
              "531            4       95  ...   28        0\n",
              "532            3      103  ...   27        0\n",
              "\n",
              "[533 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1565
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "LNhq5VNqaxaX",
        "outputId": "72cdf478-881e-4f51-9c05-1a2b24a1d812"
      },
      "source": [
        "balanced_dataset.to_csv('balanced_dataset_diabetes.csv', index = False)\n",
        "from google.colab import files\n",
        "files.download(\"balanced_dataset_diabetes.csv\")"
      ],
      "execution_count": 1570,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_c47102a7-a4df-4c32-b30e-d98c60e8c1d1\", \"balanced_dataset_diabetes.csv\", 17889)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}