K_Means_Clustering_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "K-Means Clustering Project.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Sk5d1F3FtwSm2sWB0OftMjADatCoxl_h",
      "authorship_tag": "ABX9TyP2oRHjeBbyLQ3HItFWfY+J",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/krish-hayagriva/Machine-Learning-Projects/blob/main/K_Means_Clustering_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This sheet contains the program code for performing Clsutering on WWine Data using K-Means algorithim"
      ],
      "metadata": {
        "id": "MOKgBsC1OYZh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing Libraries"
      ],
      "metadata": {
        "id": "oRj98Zo8OoCj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P9OWqOHmyqds"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy as sp\n",
        "import sklearn as scikit"
      ],
      "metadata": {
        "id": "G-tJEvuB0FxH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install -U scikit-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzqqP9qg06DO",
        "outputId": "786081b9-5233-4c6a-8c20-32e4d20ceb7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.21.5)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing and reading the dataset"
      ],
      "metadata": {
        "id": "bdESkARnOgv2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJRKr52EPt27",
        "outputId": "f06a8cc0-1d92-4f76-b5cf-1ba0e17d636d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "wine = pd.read_csv(\"/content/drive/My Drive/wine.csv\")\n"
      ],
      "metadata": {
        "id": "F-KoIVFhQsr0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading the rice dataframe"
      ],
      "metadata": {
        "id": "sVx0u499RaBZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wine\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YAXCeJmRa22q",
        "outputId": "d6db0d65-97b1-4750-86b6-1fb1cd9ae81a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     alcohol  malic   ash  alcanity  magn  phenols  flavnoids  nonfph  procy  \\\n",
              "0      14.23   1.71  2.43      15.6   127     2.80       3.06    0.28   2.29   \n",
              "1      13.20   1.78  2.14      11.2   100     2.65       2.76    0.26   1.28   \n",
              "2      13.16   2.36  2.67      18.6   101     2.80       3.24    0.30   2.81   \n",
              "3      14.37   1.95  2.50      16.8   113     3.85       3.49    0.24   2.18   \n",
              "4      13.24   2.59  2.87      21.0   118     2.80       2.69    0.39   1.82   \n",
              "..       ...    ...   ...       ...   ...      ...        ...     ...    ...   \n",
              "173    13.71   5.65  2.45      20.5    95     1.68       0.61    0.52   1.06   \n",
              "174    13.40   3.91  2.48      23.0   102     1.80       0.75    0.43   1.41   \n",
              "175    13.27   4.28  2.26      20.0   120     1.59       0.69    0.43   1.35   \n",
              "176    13.17   2.59  2.37      20.0   120     1.65       0.68    0.53   1.46   \n",
              "177    14.13   4.10  2.74      24.5    96     2.05       0.76    0.56   1.35   \n",
              "\n",
              "     cintensity   hue  od280  proline  \n",
              "0          5.64  1.04   3.92     1065  \n",
              "1          4.38  1.05   3.40     1050  \n",
              "2          5.68  1.03   3.17     1185  \n",
              "3          7.80  0.86   3.45     1480  \n",
              "4          4.32  1.04   2.93      735  \n",
              "..          ...   ...    ...      ...  \n",
              "173        7.70  0.64   1.74      740  \n",
              "174        7.30  0.70   1.56      750  \n",
              "175       10.20  0.59   1.56      835  \n",
              "176        9.30  0.60   1.62      840  \n",
              "177        9.20  0.61   1.60      560  \n",
              "\n",
              "[178 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f0ed345-55d1-4992-a0de-16f5e6ea231a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcanity</th>\n",
              "      <th>magn</th>\n",
              "      <th>phenols</th>\n",
              "      <th>flavnoids</th>\n",
              "      <th>nonfph</th>\n",
              "      <th>procy</th>\n",
              "      <th>cintensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.56</td>\n",
              "      <td>835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.62</td>\n",
              "      <td>840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>96</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f0ed345-55d1-4992-a0de-16f5e6ea231a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f0ed345-55d1-4992-a0de-16f5e6ea231a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4f0ed345-55d1-4992-a0de-16f5e6ea231a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.isnull().sum()"
      ],
      "metadata": {
        "id": "kiPEj030YZM6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26f2488a-ab31-4595-8ccb-20e9080d110b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alcohol       0\n",
              "malic         0\n",
              "ash           0\n",
              "alcanity      0\n",
              "magn          0\n",
              "phenols       0\n",
              "flavnoids     0\n",
              "nonfph        0\n",
              "procy         0\n",
              "cintensity    0\n",
              "hue           0\n",
              "od280         0\n",
              "proline       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.info()"
      ],
      "metadata": {
        "id": "Wj0MfoIxq7J4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34f63d96-961c-4f58-eb22-eb323a206b23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 178 entries, 0 to 177\n",
            "Data columns (total 13 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   alcohol     178 non-null    float64\n",
            " 1   malic       178 non-null    float64\n",
            " 2   ash         178 non-null    float64\n",
            " 3   alcanity    178 non-null    float64\n",
            " 4   magn        178 non-null    int64  \n",
            " 5   phenols     178 non-null    float64\n",
            " 6   flavnoids   178 non-null    float64\n",
            " 7   nonfph      178 non-null    float64\n",
            " 8   procy       178 non-null    float64\n",
            " 9   cintensity  178 non-null    float64\n",
            " 10  hue         178 non-null    float64\n",
            " 11  od280       178 non-null    float64\n",
            " 12  proline     178 non-null    int64  \n",
            "dtypes: float64(11), int64(2)\n",
            "memory usage: 18.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.describe()"
      ],
      "metadata": {
        "id": "cA3P9ErSrVPw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "0d03448d-2d8f-45a9-f343-c45828ce799d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          alcohol       malic         ash    alcanity        magn     phenols  \\\n",
              "count  178.000000  178.000000  178.000000  178.000000  178.000000  178.000000   \n",
              "mean    13.000618    2.336348    2.366517   19.494944   99.741573    2.295112   \n",
              "std      0.811827    1.117146    0.274344    3.339564   14.282484    0.625851   \n",
              "min     11.030000    0.740000    1.360000   10.600000   70.000000    0.980000   \n",
              "25%     12.362500    1.602500    2.210000   17.200000   88.000000    1.742500   \n",
              "50%     13.050000    1.865000    2.360000   19.500000   98.000000    2.355000   \n",
              "75%     13.677500    3.082500    2.557500   21.500000  107.000000    2.800000   \n",
              "max     14.830000    5.800000    3.230000   30.000000  162.000000    3.880000   \n",
              "\n",
              "        flavnoids      nonfph       procy  cintensity         hue       od280  \\\n",
              "count  178.000000  178.000000  178.000000  178.000000  178.000000  178.000000   \n",
              "mean     2.029270    0.361854    1.590899    5.058090    0.957449    2.611685   \n",
              "std      0.998859    0.124453    0.572359    2.318286    0.228572    0.709990   \n",
              "min      0.340000    0.130000    0.410000    1.280000    0.480000    1.270000   \n",
              "25%      1.205000    0.270000    1.250000    3.220000    0.782500    1.937500   \n",
              "50%      2.135000    0.340000    1.555000    4.690000    0.965000    2.780000   \n",
              "75%      2.875000    0.437500    1.950000    6.200000    1.120000    3.170000   \n",
              "max      5.080000    0.660000    3.580000   13.000000    1.710000    4.000000   \n",
              "\n",
              "           proline  \n",
              "count   178.000000  \n",
              "mean    746.893258  \n",
              "std     314.907474  \n",
              "min     278.000000  \n",
              "25%     500.500000  \n",
              "50%     673.500000  \n",
              "75%     985.000000  \n",
              "max    1680.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dfd87b60-cb17-48e9-a1bf-c934a277cb70\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcanity</th>\n",
              "      <th>magn</th>\n",
              "      <th>phenols</th>\n",
              "      <th>flavnoids</th>\n",
              "      <th>nonfph</th>\n",
              "      <th>procy</th>\n",
              "      <th>cintensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.000618</td>\n",
              "      <td>2.336348</td>\n",
              "      <td>2.366517</td>\n",
              "      <td>19.494944</td>\n",
              "      <td>99.741573</td>\n",
              "      <td>2.295112</td>\n",
              "      <td>2.029270</td>\n",
              "      <td>0.361854</td>\n",
              "      <td>1.590899</td>\n",
              "      <td>5.058090</td>\n",
              "      <td>0.957449</td>\n",
              "      <td>2.611685</td>\n",
              "      <td>746.893258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.811827</td>\n",
              "      <td>1.117146</td>\n",
              "      <td>0.274344</td>\n",
              "      <td>3.339564</td>\n",
              "      <td>14.282484</td>\n",
              "      <td>0.625851</td>\n",
              "      <td>0.998859</td>\n",
              "      <td>0.124453</td>\n",
              "      <td>0.572359</td>\n",
              "      <td>2.318286</td>\n",
              "      <td>0.228572</td>\n",
              "      <td>0.709990</td>\n",
              "      <td>314.907474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.030000</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>10.600000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>1.270000</td>\n",
              "      <td>278.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.362500</td>\n",
              "      <td>1.602500</td>\n",
              "      <td>2.210000</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>1.742500</td>\n",
              "      <td>1.205000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>3.220000</td>\n",
              "      <td>0.782500</td>\n",
              "      <td>1.937500</td>\n",
              "      <td>500.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.050000</td>\n",
              "      <td>1.865000</td>\n",
              "      <td>2.360000</td>\n",
              "      <td>19.500000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>2.355000</td>\n",
              "      <td>2.135000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>1.555000</td>\n",
              "      <td>4.690000</td>\n",
              "      <td>0.965000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>673.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.677500</td>\n",
              "      <td>3.082500</td>\n",
              "      <td>2.557500</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>2.875000</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>1.950000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.120000</td>\n",
              "      <td>3.170000</td>\n",
              "      <td>985.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.830000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.230000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>162.000000</td>\n",
              "      <td>3.880000</td>\n",
              "      <td>5.080000</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>3.580000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.710000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1680.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfd87b60-cb17-48e9-a1bf-c934a277cb70')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dfd87b60-cb17-48e9-a1bf-c934a277cb70 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dfd87b60-cb17-48e9-a1bf-c934a277cb70');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sclr = StandardScaler() # defining the standard scaler\n",
        "winet = pd.DataFrame(sclr.fit_transform(wine),columns =wine.columns) # transforming all features into a standardized form\n",
        "(winet).head(5)"
      ],
      "metadata": {
        "id": "emIjmTfotCN1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "0cc3d08c-0073-4e42-fe29-8fb1b0ad1434"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    alcohol     malic       ash  alcanity      magn   phenols  flavnoids  \\\n",
              "0  1.518613 -0.562250  0.232053 -1.169593  1.913905  0.808997   1.034819   \n",
              "1  0.246290 -0.499413 -0.827996 -2.490847  0.018145  0.568648   0.733629   \n",
              "2  0.196879  0.021231  1.109334 -0.268738  0.088358  0.808997   1.215533   \n",
              "3  1.691550 -0.346811  0.487926 -0.809251  0.930918  2.491446   1.466525   \n",
              "4  0.295700  0.227694  1.840403  0.451946  1.281985  0.808997   0.663351   \n",
              "\n",
              "     nonfph     procy  cintensity       hue     od280   proline  \n",
              "0 -0.659563  1.224884    0.251717  0.362177  1.847920  1.013009  \n",
              "1 -0.820719 -0.544721   -0.293321  0.406051  1.113449  0.965242  \n",
              "2 -0.498407  2.135968    0.269020  0.318304  0.788587  1.395148  \n",
              "3 -0.981875  1.032155    1.186068 -0.427544  1.184071  2.334574  \n",
              "4  0.226796  0.401404   -0.319276  0.362177  0.449601 -0.037874  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7a00d4e5-7ac4-42c2-8542-671045ac4744\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcanity</th>\n",
              "      <th>magn</th>\n",
              "      <th>phenols</th>\n",
              "      <th>flavnoids</th>\n",
              "      <th>nonfph</th>\n",
              "      <th>procy</th>\n",
              "      <th>cintensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.518613</td>\n",
              "      <td>-0.562250</td>\n",
              "      <td>0.232053</td>\n",
              "      <td>-1.169593</td>\n",
              "      <td>1.913905</td>\n",
              "      <td>0.808997</td>\n",
              "      <td>1.034819</td>\n",
              "      <td>-0.659563</td>\n",
              "      <td>1.224884</td>\n",
              "      <td>0.251717</td>\n",
              "      <td>0.362177</td>\n",
              "      <td>1.847920</td>\n",
              "      <td>1.013009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.246290</td>\n",
              "      <td>-0.499413</td>\n",
              "      <td>-0.827996</td>\n",
              "      <td>-2.490847</td>\n",
              "      <td>0.018145</td>\n",
              "      <td>0.568648</td>\n",
              "      <td>0.733629</td>\n",
              "      <td>-0.820719</td>\n",
              "      <td>-0.544721</td>\n",
              "      <td>-0.293321</td>\n",
              "      <td>0.406051</td>\n",
              "      <td>1.113449</td>\n",
              "      <td>0.965242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.196879</td>\n",
              "      <td>0.021231</td>\n",
              "      <td>1.109334</td>\n",
              "      <td>-0.268738</td>\n",
              "      <td>0.088358</td>\n",
              "      <td>0.808997</td>\n",
              "      <td>1.215533</td>\n",
              "      <td>-0.498407</td>\n",
              "      <td>2.135968</td>\n",
              "      <td>0.269020</td>\n",
              "      <td>0.318304</td>\n",
              "      <td>0.788587</td>\n",
              "      <td>1.395148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.691550</td>\n",
              "      <td>-0.346811</td>\n",
              "      <td>0.487926</td>\n",
              "      <td>-0.809251</td>\n",
              "      <td>0.930918</td>\n",
              "      <td>2.491446</td>\n",
              "      <td>1.466525</td>\n",
              "      <td>-0.981875</td>\n",
              "      <td>1.032155</td>\n",
              "      <td>1.186068</td>\n",
              "      <td>-0.427544</td>\n",
              "      <td>1.184071</td>\n",
              "      <td>2.334574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.295700</td>\n",
              "      <td>0.227694</td>\n",
              "      <td>1.840403</td>\n",
              "      <td>0.451946</td>\n",
              "      <td>1.281985</td>\n",
              "      <td>0.808997</td>\n",
              "      <td>0.663351</td>\n",
              "      <td>0.226796</td>\n",
              "      <td>0.401404</td>\n",
              "      <td>-0.319276</td>\n",
              "      <td>0.362177</td>\n",
              "      <td>0.449601</td>\n",
              "      <td>-0.037874</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a00d4e5-7ac4-42c2-8542-671045ac4744')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7a00d4e5-7ac4-42c2-8542-671045ac4744 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7a00d4e5-7ac4-42c2-8542-671045ac4744');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = winet.corr()\n",
        "print(corr_matrix)\n",
        "type(winet)"
      ],
      "metadata": {
        "id": "6aBSM4gpoolX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d94942f-07c6-4a2e-8c73-c967f88ed322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             alcohol     malic       ash  alcanity      magn   phenols  \\\n",
            "alcohol     1.000000  0.094397  0.211545 -0.310235  0.270798  0.289101   \n",
            "malic       0.094397  1.000000  0.164045  0.288500 -0.054575 -0.335167   \n",
            "ash         0.211545  0.164045  1.000000  0.443367  0.286587  0.128980   \n",
            "alcanity   -0.310235  0.288500  0.443367  1.000000 -0.083333 -0.321113   \n",
            "magn        0.270798 -0.054575  0.286587 -0.083333  1.000000  0.214401   \n",
            "phenols     0.289101 -0.335167  0.128980 -0.321113  0.214401  1.000000   \n",
            "flavnoids   0.236815 -0.411007  0.115077 -0.351370  0.195784  0.864564   \n",
            "nonfph     -0.155929  0.292977  0.186230  0.361922 -0.256294 -0.449935   \n",
            "procy       0.136698 -0.220746  0.009652 -0.197327  0.236441  0.612413   \n",
            "cintensity  0.546364  0.248985  0.258887  0.018732  0.199950 -0.055136   \n",
            "hue        -0.071747 -0.561296 -0.074667 -0.273955  0.055398  0.433681   \n",
            "od280       0.072343 -0.368710  0.003911 -0.276769  0.066004  0.699949   \n",
            "proline     0.643720 -0.192011  0.223626 -0.440597  0.393351  0.498115   \n",
            "\n",
            "            flavnoids    nonfph     procy  cintensity       hue     od280  \\\n",
            "alcohol      0.236815 -0.155929  0.136698    0.546364 -0.071747  0.072343   \n",
            "malic       -0.411007  0.292977 -0.220746    0.248985 -0.561296 -0.368710   \n",
            "ash          0.115077  0.186230  0.009652    0.258887 -0.074667  0.003911   \n",
            "alcanity    -0.351370  0.361922 -0.197327    0.018732 -0.273955 -0.276769   \n",
            "magn         0.195784 -0.256294  0.236441    0.199950  0.055398  0.066004   \n",
            "phenols      0.864564 -0.449935  0.612413   -0.055136  0.433681  0.699949   \n",
            "flavnoids    1.000000 -0.537900  0.652692   -0.172379  0.543479  0.787194   \n",
            "nonfph      -0.537900  1.000000 -0.365845    0.139057 -0.262640 -0.503270   \n",
            "procy        0.652692 -0.365845  1.000000   -0.025250  0.295544  0.519067   \n",
            "cintensity  -0.172379  0.139057 -0.025250    1.000000 -0.521813 -0.428815   \n",
            "hue          0.543479 -0.262640  0.295544   -0.521813  1.000000  0.565468   \n",
            "od280        0.787194 -0.503270  0.519067   -0.428815  0.565468  1.000000   \n",
            "proline      0.494193 -0.311385  0.330417    0.316100  0.236183  0.312761   \n",
            "\n",
            "             proline  \n",
            "alcohol     0.643720  \n",
            "malic      -0.192011  \n",
            "ash         0.223626  \n",
            "alcanity   -0.440597  \n",
            "magn        0.393351  \n",
            "phenols     0.498115  \n",
            "flavnoids   0.494193  \n",
            "nonfph     -0.311385  \n",
            "procy       0.330417  \n",
            "cintensity  0.316100  \n",
            "hue         0.236183  \n",
            "od280       0.312761  \n",
            "proline     1.000000  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n"
      ],
      "metadata": {
        "id": "e9jfL_lWbtSH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# applying the Elbow method to identfy the optimal number of clusters k\n",
        "K = range(1,8)\n",
        "ssd = []\n",
        "for k in K:\n",
        "  Kmeans = KMeans(n_clusters = k , init = 'k-means++',random_state = 0)\n",
        "  wlabel = kmeans.labels_\n",
        "  winecs = Kmeans.fit(winet)\n",
        "  ssd.append(winecs.inertia_)\n"
      ],
      "metadata": {
        "id": "BjURs8Mwb3lV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(wlabel)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTMzCdGi-WcJ",
        "outputId": "371b94d7-9117-4a6d-c0c7-8d72e3b3e1a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 1 4 1 1 4 1 1 1 4 1 1 1 4 4 4 1 4 1 4 4 4 4 4 4 4 4 1 4 1 4 4 4 4 4\n",
            " 4 5 1 1 5 1 5 1 4 1 1 1 1 1 1 1 4 1 1 1 4 1 7 7 7 7 5 7 2 5 5 7 0 7 2 2 4\n",
            " 5 7 5 7 0 2 5 5 2 3 5 5 2 2 2 2 7 2 7 5 5 0 0 5 5 5 5 7 2 5 5 2 5 2 5 2 5\n",
            " 5 2 2 2 2 5 2 7 5 5 2 2 5 5 5 5 2 2 2 7 3 3 3 7 3 3 3 3 3 3 3 3 3 6 3 3 3\n",
            " 6 6 6 6 6 6 3 3 6 3 6 6 3 3 3 3 6 3 6 6 6 6 3 3 6 3 6 6 6 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wineEL = pd.DataFrame({'clus_id':K, 'Sum of Squares distance': ssd})\n",
        "wineEL"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "ODXALhGteVU-",
        "outputId": "c8653c04-181e-4c82-f6fa-aef887afb2ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   clus_id  Sum of Squares distance\n",
              "0        1              2314.000000\n",
              "1        2              1659.007967\n",
              "2        3              1278.760776\n",
              "3        4              1175.705193\n",
              "4        5              1113.020882\n",
              "5        6              1044.078049\n",
              "6        7               999.549369"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17409827-6637-4187-9d3f-6c1d1be0967a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clus_id</th>\n",
              "      <th>Sum of Squares distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2314.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1659.007967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1278.760776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1175.705193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1113.020882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>1044.078049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>999.549369</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17409827-6637-4187-9d3f-6c1d1be0967a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-17409827-6637-4187-9d3f-6c1d1be0967a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-17409827-6637-4187-9d3f-6c1d1be0967a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.metrics import pairwise_distances\n",
        "shil_Averagescore = metrics.silhouette_score(winet, labels = wlabel, metric = 'euclidean')\n",
        "print(shil_Averagescore)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwaPwCl7SEsj",
        "outputId": "5c0a9ab2-f234-4608-cee9-bfd1210db0e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.13581656516941268\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A Silhoutte score lies between -1 and +1 . Towards +1 , the larger the value, higher is the density of clustering and towards zero or -1 indicating incorrect clustering. \n",
        "\n",
        "In the extant case, our Silhouttee score is positive and 013 indicating relatively correct clustering and reasonable density."
      ],
      "metadata": {
        "id": "KEMuQqBcXO2V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will also now use the elbow diagram to understand the result of clustering through a visual graphical representation"
      ],
      "metadata": {
        "id": "227rOYuYYMkW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,8),ssd)\n",
        "plt.title('elbow_method')\n",
        "plt.xlabel('no of clusters')\n",
        "plt.ylabel('ssd')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "QdXnSTSkZprU",
        "outputId": "3dacf2e9-f271-4eb0-abf6-723d0a3b1b60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score"
      ],
      "metadata": {
        "id": "SwhgyQnS2JGv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shavg = silhouette_score(winet,wlabel)\n",
        "shavg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66ZrcO6A2P5f",
        "outputId": "aa5d7c99-e226-45da-e03b-314bec09c394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13581656516941268"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shcore_values = pd.DataFrame(silhouette_samples(winet,wlabel))"
      ],
      "metadata": {
        "id": "F5ZF_WHMOOJ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To generate the Silhouttee score for each cluster separately to validate and choose the optimum clusters from elbow method.\n",
        "\n",
        "cls = 8\n",
        "means_lst = []\n",
        "for label in range(cls):\n",
        "    means_lst.append(shcore_values[wlabel== label].mean())\n",
        "print(means_lst)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bplRJv_cTHJ4",
        "outputId": "7338067f-3e62-4e1c-ca86-0ef14cfc78c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0    0.260015\n",
            "dtype: float64, 0    0.170075\n",
            "dtype: float64, 0    0.088499\n",
            "dtype: float64, 0    0.171612\n",
            "dtype: float64, 0    0.121705\n",
            "dtype: float64, 0    0.121521\n",
            "dtype: float64, 0    0.146734\n",
            "dtype: float64, 0    0.082621\n",
            "dtype: float64]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see from the above, after the first three clusters, the distances between the clusters and within clusters does not improve . Hence best is to segment the wine produce into three clusters"
      ],
      "metadata": {
        "id": "I-ZmExC0Vvhh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# appending the cluster ID into the data frame to be a readable data\n",
        "wine['Cluster-ID'] = wlabel\n",
        "wine"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "Z3ruIIKRVFRy",
        "outputId": "182fbdf2-787e-4c36-b185-16683b0e28b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     alcohol  malic   ash  alcanity  magn  phenols  flavnoids  nonfph  procy  \\\n",
              "0      14.23   1.71  2.43      15.6   127     2.80       3.06    0.28   2.29   \n",
              "1      13.20   1.78  2.14      11.2   100     2.65       2.76    0.26   1.28   \n",
              "2      13.16   2.36  2.67      18.6   101     2.80       3.24    0.30   2.81   \n",
              "3      14.37   1.95  2.50      16.8   113     3.85       3.49    0.24   2.18   \n",
              "4      13.24   2.59  2.87      21.0   118     2.80       2.69    0.39   1.82   \n",
              "..       ...    ...   ...       ...   ...      ...        ...     ...    ...   \n",
              "173    13.71   5.65  2.45      20.5    95     1.68       0.61    0.52   1.06   \n",
              "174    13.40   3.91  2.48      23.0   102     1.80       0.75    0.43   1.41   \n",
              "175    13.27   4.28  2.26      20.0   120     1.59       0.69    0.43   1.35   \n",
              "176    13.17   2.59  2.37      20.0   120     1.65       0.68    0.53   1.46   \n",
              "177    14.13   4.10  2.74      24.5    96     2.05       0.76    0.56   1.35   \n",
              "\n",
              "     cintensity   hue  od280  proline  Cluster-ID  \n",
              "0          5.64  1.04   3.92     1065           1  \n",
              "1          4.38  1.05   3.40     1050           1  \n",
              "2          5.68  1.03   3.17     1185           1  \n",
              "3          7.80  0.86   3.45     1480           1  \n",
              "4          4.32  1.04   2.93      735           4  \n",
              "..          ...   ...    ...      ...         ...  \n",
              "173        7.70  0.64   1.74      740           3  \n",
              "174        7.30  0.70   1.56      750           6  \n",
              "175       10.20  0.59   1.56      835           6  \n",
              "176        9.30  0.60   1.62      840           6  \n",
              "177        9.20  0.61   1.60      560           6  \n",
              "\n",
              "[178 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7931eae2-41fe-4ca3-8597-a26a3776f765\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcanity</th>\n",
              "      <th>magn</th>\n",
              "      <th>phenols</th>\n",
              "      <th>flavnoids</th>\n",
              "      <th>nonfph</th>\n",
              "      <th>procy</th>\n",
              "      <th>cintensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280</th>\n",
              "      <th>proline</th>\n",
              "      <th>Cluster-ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
              "      <td>4</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.56</td>\n",
              "      <td>835</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.62</td>\n",
              "      <td>840</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>96</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7931eae2-41fe-4ca3-8597-a26a3776f765')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7931eae2-41fe-4ca3-8597-a26a3776f765 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7931eae2-41fe-4ca3-8597-a26a3776f765');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "DSK9osCyVSWn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}