[Project]_Clustering_Analysis_with_Python.ipynb

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      "name": "[Project] Clustering Analysis with Python",
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      "cell_type": "markdown",
      "metadata": {
        "id": "FRotGY1k8iIp"
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      "source": [
        "## 1. Defining the Question"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMdePzNZ8iIq"
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      "source": [
        "### a) The Data Analysis Question\n",
        "\n",
        "Perform wine segmentation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4S43KyW8iIr"
      },
      "source": [
        "### b) Metric for Success\n",
        "\n",
        "The project will be a success when we are able to create clusters with clear boundaries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YE0Zisl-8iIs"
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      "source": [
        "### c) The Context \n",
        "\n",
        "Champagne Company is a leading wine company that specialises in the importation and\n",
        "distribution of premium and super-premium wines, champagnes and spirits within the\n",
        "East African region. The main goal of the company is to make the most exclusive brands\n",
        "of wine more accessible at an affordable price. The company wants to satisfy the\n",
        "growing local demand for specialised products and the ever-increasing tourism industry\n",
        "where guests don ́t want to settle for less."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5WzpVnI8iIt"
      },
      "source": [
        "### d) Solution Steps\n",
        "\n",
        "- Import, Explore and Clean Data\n",
        "- Data Analysis\n",
        "- Data Modeling and Evaluation\n",
        "- Summarise Findings\n",
        "- Provide Recommendations\n",
        "- Challenge the Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tICJILU8iIu"
      },
      "source": [
        "### e) Data Relevance\n",
        "\n",
        "The dataset is appropriate to answering the research question."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhIVwJVk8iIv"
      },
      "source": [
        "## 2. Import, Explore and Clean Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39w8ezh68iIw"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "pd.set_option('display.max.columns', None)"
      ],
      "execution_count": 1,
      "outputs": []
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        "outputId": "119f03dd-5cb0-4cfe-fb65-e2121a5f97bd"
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      "source": [
        "df = pd.read_csv('https://bit.ly/2UBJERh')\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Alcohol  Malic_Acid   Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
              "0    14.23        1.71  2.43          15.6        127           2.80   \n",
              "1    13.20        1.78  2.14          11.2        100           2.65   \n",
              "2    13.16        2.36  2.67          18.6        101           2.80   \n",
              "3    14.37        1.95  2.50          16.8        113           3.85   \n",
              "4    13.24        2.59  2.87          21.0        118           2.80   \n",
              "\n",
              "   Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity   Hue  \\\n",
              "0        3.06                  0.28             2.29             5.64  1.04   \n",
              "1        2.76                  0.26             1.28             4.38  1.05   \n",
              "2        3.24                  0.30             2.81             5.68  1.03   \n",
              "3        3.49                  0.24             2.18             7.80  0.86   \n",
              "4        2.69                  0.39             1.82             4.32  1.04   \n",
              "\n",
              "   OD280  Proline  \n",
              "0   3.92     1065  \n",
              "1   3.40     1050  \n",
              "2   3.17     1185  \n",
              "3   3.45     1480  \n",
              "4   2.93      735  "
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              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
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              "      <td>2.67</td>\n",
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              "      <td>14.37</td>\n",
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              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
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              "      <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",
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        "outputId": "c9224f54-5056-4847-d4dd-9c8239486a6f"
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      "source": [
        "# check dataset shape\n",
        "df.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(178, 13)"
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          "execution_count": 3
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      "source": [
        "# chekc data types\n",
        "df.dtypes"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Alcohol                 float64\n",
              "Malic_Acid              float64\n",
              "Ash                     float64\n",
              "Ash_Alcanity            float64\n",
              "Magnesium                 int64\n",
              "Total_Phenols           float64\n",
              "Flavanoids              float64\n",
              "Nonflavanoid_Phenols    float64\n",
              "Proanthocyanins         float64\n",
              "Color_Intensity         float64\n",
              "Hue                     float64\n",
              "OD280                   float64\n",
              "Proline                   int64\n",
              "dtype: object"
            ]
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          "metadata": {},
          "execution_count": 4
        }
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8039567b-224a-4744-b0bd-eb4cdcc3677a"
      },
      "source": [
        "# dropping duplicates, if any\n",
        "df.drop_duplicates(inplace = True)\n",
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "(178, 13)"
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        "outputId": "6720a7e9-1ada-41d1-a9a1-8e5a33fc9625"
      },
      "source": [
        "# check for missing data\n",
        "df.isna().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Alcohol                 0\n",
              "Malic_Acid              0\n",
              "Ash                     0\n",
              "Ash_Alcanity            0\n",
              "Magnesium               0\n",
              "Total_Phenols           0\n",
              "Flavanoids              0\n",
              "Nonflavanoid_Phenols    0\n",
              "Proanthocyanins         0\n",
              "Color_Intensity         0\n",
              "Hue                     0\n",
              "OD280                   0\n",
              "Proline                 0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 6
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      "source": [
        "## 3. Data Analysis"
      ]
    },
    {
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        "outputId": "2dda0ca9-71d9-48fb-8c06-ad9bab5a162f"
      },
      "source": [
        "# get descriptive statistics\n",
        "df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Alcohol  Malic_Acid         Ash  Ash_Alcanity   Magnesium  \\\n",
              "count  178.000000  178.000000  178.000000    178.000000  178.000000   \n",
              "mean    13.000618    2.336348    2.366517     19.494944   99.741573   \n",
              "std      0.811827    1.117146    0.274344      3.339564   14.282484   \n",
              "min     11.030000    0.740000    1.360000     10.600000   70.000000   \n",
              "25%     12.362500    1.602500    2.210000     17.200000   88.000000   \n",
              "50%     13.050000    1.865000    2.360000     19.500000   98.000000   \n",
              "75%     13.677500    3.082500    2.557500     21.500000  107.000000   \n",
              "max     14.830000    5.800000    3.230000     30.000000  162.000000   \n",
              "\n",
              "       Total_Phenols  Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  \\\n",
              "count     178.000000  178.000000            178.000000       178.000000   \n",
              "mean        2.295112    2.029270              0.361854         1.590899   \n",
              "std         0.625851    0.998859              0.124453         0.572359   \n",
              "min         0.980000    0.340000              0.130000         0.410000   \n",
              "25%         1.742500    1.205000              0.270000         1.250000   \n",
              "50%         2.355000    2.135000              0.340000         1.555000   \n",
              "75%         2.800000    2.875000              0.437500         1.950000   \n",
              "max         3.880000    5.080000              0.660000         3.580000   \n",
              "\n",
              "       Color_Intensity         Hue       OD280      Proline  \n",
              "count       178.000000  178.000000  178.000000   178.000000  \n",
              "mean          5.058090    0.957449    2.611685   746.893258  \n",
              "std           2.318286    0.228572    0.709990   314.907474  \n",
              "min           1.280000    0.480000    1.270000   278.000000  \n",
              "25%           3.220000    0.782500    1.937500   500.500000  \n",
              "50%           4.690000    0.965000    2.780000   673.500000  \n",
              "75%           6.200000    1.120000    3.170000   985.000000  \n",
              "max          13.000000    1.710000    4.000000  1680.000000  "
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              "      <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-1df792ab-5938-4ab2-9ca8-9c82c42fe060')\"\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-1df792ab-5938-4ab2-9ca8-9c82c42fe060 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-1df792ab-5938-4ab2-9ca8-9c82c42fe060');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnEYg1_38iKE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6508a1f9-e9be-43a3-f05b-44f3602d9fad"
      },
      "source": [
        "# previewing the histograms of each variable\n",
        "fig, axes = plt.subplots(nrows = 7, ncols = 2, figsize = (10, 20))\n",
        "\n",
        "cols = df.columns.to_list()\n",
        "colors = ['#00FF7F', '#8B0000', '#0000FF', '#DB7093', '#FFFF00', '#FF4500', '#7B68EE', '#FF00FF',\n",
        "          '#ADFF2F', '#FFD700', '#A52A2A', '#C71585', '#8B008B']\n",
        "\n",
        "for col, color, ax in zip(cols, colors, axes.flatten()):\n",
        "    sns.distplot(df[col], ax = ax, color = color, hist_kws = dict(alpha = 0.6))\n",
        "    plt.title(col)\n",
        "    \n",
        "plt.tight_layout()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1440 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Malic Acid and Magnesium variables appear to be skewed to the right"
      ],
      "metadata": {
        "id": "KUA6SFaBdwiC"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "NZCVHfIg8iKI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7431d46c-cd14-4de7-ad3b-6d6961e1e48f"
      },
      "source": [
        "# previewing the boxplots of each variable\n",
        "fig, axes = plt.subplots(nrows = 7, ncols = 2, figsize = (10, 20))\n",
        "\n",
        "cols = df.columns.to_list()\n",
        "colors = ['#00FF7F', '#8B0000', '#0000FF', '#DB7093', '#FFFF00', '#FF4500', '#7B68EE', '#FF00FF',\n",
        "          '#ADFF2F', '#FFD700', '#A52A2A', '#C71585', '#8B008B']\n",
        "\n",
        "for col, color, ax in zip(cols, colors, axes.flatten()):\n",
        "    sns.boxplot(df[col], ax = ax, color = color)\n",
        "    plt.title(col)\n",
        "    \n",
        "plt.tight_layout()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1440 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some variables do have outliers while others don't"
      ],
      "metadata": {
        "id": "UteOS2JBeAIQ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXYwMvkG8iKO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "4c3d9de4-ff8c-4e47-e449-ca42e6462d7b"
      },
      "source": [
        "# previewing the correlations between the variables\n",
        "corr = df.corr()\n",
        "\n",
        "plt.figure(figsize = (10, 10))\n",
        "sns.heatmap(corr, annot = True, cmap = 'coolwarm')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fgqwj_rl8iKc"
      },
      "source": [
        "Strong correlations noted are:\n",
        "* Alochol & Proline\n",
        "* Total Phenols & Flavanoids\n",
        "* Total Phenols & OD280\n",
        "* Flavanoids & OD280\n",
        "* Proanthocyanins & Phenols\n",
        "* Proanthocyanins & Flavanoids"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYg5V60h8iKg"
      },
      "source": [
        "## 4. Data Modeling and Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8Mmw2k08iKg"
      },
      "source": [
        "# set all the features into X\n",
        "X = df.iloc[:,].values"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Egh-LmB38iK2"
      },
      "source": [
        "# scale our features\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit and transform our features\n",
        "X  = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "6boW05SURrLE"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuixkvLB8iK5"
      },
      "source": [
        "### 4.1 KMeans Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiJDAoPB8iK6"
      },
      "source": [
        "# fitting KMeans with 5 clusters\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(5, random_state=0).fit(X)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the cluster labels\n",
        "kmeans.labels_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2-XGYNBSsrT",
        "outputId": "5fd59dbf-967a-4312-a31f-7a00077de64b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n",
              "       2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 3,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 3, 1, 3,\n",
              "       3, 4, 4, 4, 4, 3, 4, 2, 3, 4, 4, 4, 4, 3, 4, 3, 1, 0, 3, 4, 1, 1,\n",
              "       1, 1, 1, 1, 1, 3, 3, 2, 4, 4, 3, 3, 4, 4, 3, 4, 4, 1, 4, 1, 4, 3,\n",
              "       3, 3, 1, 1, 1, 1, 4, 4, 4, 4, 3, 1, 1, 3, 3, 3, 3, 1, 1, 1, 4, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvLSks1o8iK9"
      },
      "source": [
        "# getting the KMeans scatter plot\n",
        "kmeans_pred = kmeans.labels_\n",
        "kmeans_clust = np.unique(kmeans_pred)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8, 6))\n",
        "for cluster in kmeans_clust:\n",
        "    row_ix = np.where(kmeans_pred == cluster)\n",
        "    plt.scatter(X[row_ix, 0], X[row_ix, 1], label = cluster)\n",
        "    \n",
        "plt.title(\"Clustering by K-Means Clustering (k = 5)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "Bm5o3UeQS7Z6",
        "outputId": "b11759a2-3b64-485f-d734-634bae015e36"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Obervation**: There doesn't seem to be clear boundaries between clusters"
      ],
      "metadata": {
        "id": "9RKf-9Y7jFwZ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgUoM6288iLA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "a002a3b8-7456-482f-93ec-affdffcdfeac"
      },
      "source": [
        "# getting the optimum value of k for our KMeans model using the Elbow Method and visualise\n",
        "Error = []\n",
        "\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters = i).fit(X)\n",
        "    Error.append(kmeans.inertia_)\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(range(1, 11), Error, 'bx-')\n",
        "plt.title('Elbow method')\n",
        "plt.xlabel('No of clusters')\n",
        "plt.ylabel('Error')\n",
        "plt.grid(True);"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**: The optimal value of k is 3"
      ],
      "metadata": {
        "id": "1qMvNSgejigb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the optimum value of k for our KMeans model using the silhouette coefficient\n",
        "from sklearn.metrics import silhouette_score\n",
        "silhouette = [] # list of separation distance between the resulting clusters for each value of k \n",
        "\n",
        "K = range(2, 11)\n",
        "for k in K:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=0)\n",
        "    kmeans.fit(X)\n",
        "    silhouette.append(silhouette_score(X, kmeans.labels_))\n",
        "\n",
        "plt.plot(range(2, 11), silhouette, 'bx-')\n",
        "plt.title('Silhouette score vs No. of clusters (k)')\n",
        "plt.xlabel('No of clusters (k)')\n",
        "plt.ylabel('Silhouette coefficient')\n",
        "plt.grid(True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f-2KVyHyThIX",
        "outputId": "32d5414b-f56b-4b44-e1c3-d7cd908f82d6"
      },
      "execution_count": 19,
      "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": "markdown",
      "metadata": {
        "id": "HsKukR3Y8iLL"
      },
      "source": [
        "**Observation**: The optimal value of k is 3"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Improving our solution by using k = 3"
      ],
      "metadata": {
        "id": "S8UJZIo9m5Ed"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJbeFXPS8iLM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "1085d587-798a-4816-8c7d-42ec4c305f0c"
      },
      "source": [
        "kmeans_improved = KMeans(3, random_state=0).fit(X)\n",
        "\n",
        "kmeans_new_pred = kmeans_improved.labels_\n",
        "kmeans_new_clust = np.unique(kmeans_new_pred)\n",
        "\n",
        "plt.figure(figsize = (8, 6))\n",
        "for cluster in kmeans_new_clust:\n",
        "    row_ix = np.where(kmeans_new_pred == cluster)\n",
        "    plt.scatter(X[row_ix, 0], X[row_ix, 1], label = cluster)\n",
        "    \n",
        "plt.title(\"Clustering by K-Means Clustering (k = 3)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PwrV_-K8iLP"
      },
      "source": [
        "Visualization of the model with the new value of k looks better than the original one. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmRdjgoO8iLP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "a951177c-ab01-4b46-c66a-916dfa06652e"
      },
      "source": [
        "# add these cluster labels to our dataset\n",
        "df['kmeans_cluster_group'] = kmeans_improved.predict(X)\n",
        "\n",
        "# previewing a sample of our dataset\n",
        "df.sample(10)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Alcohol  Malic_Acid   Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
              "54     13.74        1.67  2.25          16.4        118           2.60   \n",
              "94     11.62        1.99  2.28          18.0         98           3.02   \n",
              "27     13.30        1.72  2.14          17.0         94           2.40   \n",
              "106    12.25        1.73  2.12          19.0         80           1.65   \n",
              "92     12.69        1.53  2.26          20.7         80           1.38   \n",
              "74     11.96        1.09  2.30          21.0        101           3.38   \n",
              "132    12.81        2.31  2.40          24.0         98           1.15   \n",
              "80     12.00        0.92  2.00          19.0         86           2.42   \n",
              "15     13.63        1.81  2.70          17.2        112           2.85   \n",
              "59     12.37        0.94  1.36          10.6         88           1.98   \n",
              "\n",
              "     Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity   Hue  \\\n",
              "54         2.90                  0.21             1.62             5.85  0.92   \n",
              "94         2.26                  0.17             1.35             3.25  1.16   \n",
              "27         2.19                  0.27             1.35             3.95  1.02   \n",
              "106        2.03                  0.37             1.63             3.40  1.00   \n",
              "92         1.46                  0.58             1.62             3.05  0.96   \n",
              "74         2.14                  0.13             1.65             3.21  0.99   \n",
              "132        1.09                  0.27             0.83             5.70  0.66   \n",
              "80         2.26                  0.30             1.43             2.50  1.38   \n",
              "15         2.91                  0.30             1.46             7.30  1.28   \n",
              "59         0.57                  0.28             0.42             1.95  1.05   \n",
              "\n",
              "     OD280  Proline  kmeans_cluster_group  \n",
              "54    3.20     1060                     2  \n",
              "94    2.96      345                     1  \n",
              "27    2.77     1285                     2  \n",
              "106   3.17      510                     1  \n",
              "92    2.06      495                     1  \n",
              "74    3.13      886                     1  \n",
              "132   1.36      560                     0  \n",
              "80    3.12      278                     1  \n",
              "15    2.88     1310                     2  \n",
              "59    1.82      520                     1  "
            ],
            "text/html": [
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              "</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_Acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Ash_Alcanity</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total_Phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid_Phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color_Intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280</th>\n",
              "      <th>Proline</th>\n",
              "      <th>kmeans_cluster_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>13.74</td>\n",
              "      <td>1.67</td>\n",
              "      <td>2.25</td>\n",
              "      <td>16.4</td>\n",
              "      <td>118</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.62</td>\n",
              "      <td>5.85</td>\n",
              "      <td>0.92</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1060</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>11.62</td>\n",
              "      <td>1.99</td>\n",
              "      <td>2.28</td>\n",
              "      <td>18.0</td>\n",
              "      <td>98</td>\n",
              "      <td>3.02</td>\n",
              "      <td>2.26</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.35</td>\n",
              "      <td>3.25</td>\n",
              "      <td>1.16</td>\n",
              "      <td>2.96</td>\n",
              "      <td>345</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>13.30</td>\n",
              "      <td>1.72</td>\n",
              "      <td>2.14</td>\n",
              "      <td>17.0</td>\n",
              "      <td>94</td>\n",
              "      <td>2.40</td>\n",
              "      <td>2.19</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.35</td>\n",
              "      <td>3.95</td>\n",
              "      <td>1.02</td>\n",
              "      <td>2.77</td>\n",
              "      <td>1285</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>12.25</td>\n",
              "      <td>1.73</td>\n",
              "      <td>2.12</td>\n",
              "      <td>19.0</td>\n",
              "      <td>80</td>\n",
              "      <td>1.65</td>\n",
              "      <td>2.03</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.63</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.17</td>\n",
              "      <td>510</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>12.69</td>\n",
              "      <td>1.53</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.7</td>\n",
              "      <td>80</td>\n",
              "      <td>1.38</td>\n",
              "      <td>1.46</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1.62</td>\n",
              "      <td>3.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>2.06</td>\n",
              "      <td>495</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>11.96</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.30</td>\n",
              "      <td>21.0</td>\n",
              "      <td>101</td>\n",
              "      <td>3.38</td>\n",
              "      <td>2.14</td>\n",
              "      <td>0.13</td>\n",
              "      <td>1.65</td>\n",
              "      <td>3.21</td>\n",
              "      <td>0.99</td>\n",
              "      <td>3.13</td>\n",
              "      <td>886</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>12.81</td>\n",
              "      <td>2.31</td>\n",
              "      <td>2.40</td>\n",
              "      <td>24.0</td>\n",
              "      <td>98</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.83</td>\n",
              "      <td>5.70</td>\n",
              "      <td>0.66</td>\n",
              "      <td>1.36</td>\n",
              "      <td>560</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>12.00</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.00</td>\n",
              "      <td>19.0</td>\n",
              "      <td>86</td>\n",
              "      <td>2.42</td>\n",
              "      <td>2.26</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.43</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1.38</td>\n",
              "      <td>3.12</td>\n",
              "      <td>278</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>13.63</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.70</td>\n",
              "      <td>17.2</td>\n",
              "      <td>112</td>\n",
              "      <td>2.85</td>\n",
              "      <td>2.91</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.46</td>\n",
              "      <td>7.30</td>\n",
              "      <td>1.28</td>\n",
              "      <td>2.88</td>\n",
              "      <td>1310</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>12.37</td>\n",
              "      <td>0.94</td>\n",
              "      <td>1.36</td>\n",
              "      <td>10.6</td>\n",
              "      <td>88</td>\n",
              "      <td>1.98</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.95</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1.82</td>\n",
              "      <td>520</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1q_syzFy8iLb"
      },
      "source": [
        "### 4.2 Hierarchical Agglomerative Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HS3gNlTJ8iLi"
      },
      "source": [
        "# fitting agglomerative clustering with 5 clusters\n",
        "from sklearn.cluster import AgglomerativeClustering as AC\n",
        "\n",
        "agglo = AC(n_clusters = 5) .fit(X)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get the labels\n",
        "labels = agglo.labels_\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUAKGHfWXiXO",
        "outputId": "6e671388-9add-4dad-a91b-9e3536e2d195"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 1, 1, 3, 0,\n",
              "       0, 1, 2, 1, 2, 0, 3, 0, 1, 1, 1, 2, 1, 3, 1, 1, 3, 2, 1, 1, 3, 3,\n",
              "       3, 3, 3, 3, 3, 1, 1, 1, 2, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 3, 1, 1,\n",
              "       1, 3, 3, 3, 3, 3, 1, 3, 2, 3, 1, 0, 3, 1, 1, 3, 3, 3, 3, 3, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
              "       2, 2, 4, 2, 4, 4, 2, 2, 2, 2, 4, 2, 4, 4, 4, 4, 2, 4, 4, 2, 4, 4,\n",
              "       4, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJZMZN6q8iLt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "df0ebdfd-2feb-48d6-d133-23aa61cbf2a8"
      },
      "source": [
        "# Creating a scatter plot of the cluster analysis\n",
        "agglo_clust = np.unique(labels)\n",
        "agglo_pred = agglo.labels_\n",
        "plt.figure(figsize = (10, 7))\n",
        "for cluster in agglo_clust:\n",
        "    row_ix = np.where(agglo_pred == cluster)\n",
        "    plt.scatter(X[row_ix, 0], X[row_ix, 1], label = cluster)\n",
        "    \n",
        "plt.title(\"Hierarchical Agglomerative Clustering (clusters = 5)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**: There doesn't seem to be clear boundaries between clusters"
      ],
      "metadata": {
        "id": "rpgDEsSOZiND"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A dendrogram of our agglomerative clustering analysis"
      ],
      "metadata": {
        "id": "4FXu2sZcnwJ-"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBflNGcg8iLw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "416593b3-efe3-437c-da91-29b138195511"
      },
      "source": [
        "from scipy.cluster import hierarchy as shc\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.title(\"Hierarchical Clustering Dendrogram\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='ward'))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The x-axis contains the samples and y-axis represents the distance between these samples. If we were to draw a horizontal line that cuts through all the vertical blue lines, we would cut through three lines. The vertical line with maximum distance is the first blue line and hence we can decide a threshold of 20 and cut the dendrogram"
      ],
      "metadata": {
        "id": "q_QBt2nbaScs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "plt.title(\"Hierarchical Clustering Dendrogram\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='ward'))\n",
        "plt.axhline(y=20, color='r', linestyle='--')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "Ci3Z4cseaX9q",
        "outputId": "2f3e2c92-fb7a-40d2-e6bd-252355ea0515"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7fa540a47090>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have 3 clusters as the red line cuts the dendrogram at two points. Let’s now improve our agglomerative clustering model by using 3 clusters"
      ],
      "metadata": {
        "id": "m7KnqZcgamYL"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtRWu3vz8iL-"
      },
      "source": [
        "agglo = AC(n_clusters = 3) .fit(X)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = agglo.labels_\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jhu-78-Da6CF",
        "outputId": "c30b4db0-a89d-47d0-90f0-87a68bf17b0b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 0, 0, 0, 2,\n",
              "       2, 0, 1, 0, 1, 2, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a scatter plot of the cluster analysis\n",
        "agglo_clust = np.unique(labels)\n",
        "agglo_pred = agglo.labels_\n",
        "plt.figure(figsize = (10, 7))\n",
        "for cluster in agglo_clust:\n",
        "    row_ix = np.where(agglo_pred == cluster)\n",
        "    plt.scatter(X[row_ix, 0], X[row_ix, 1], label = cluster)\n",
        "    \n",
        "plt.title(\"Hierarchical Agglomerative Clustering (clusters = 3)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "LdUVPJ44bwuJ",
        "outputId": "49e97371-b0ee-4fb9-e905-79d2825733e9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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dpLPOOquhfjIiBQAAYmXbzlGtu/E+jY5PyEkaHZ/Quhvv07ado3Wfc3R0VAsWLDj+eXd3t0ZH6z9fDoEUAACIlU3bd2li8mhB28TkUW3aviuiHpXH1B6AzAt6CgFAY/aNT9TUXo2uri7t2bPn+Od79+5VV1fjP+eMSAHItDCmEAA0Zn5ne03t1Xj5y1+uhx9+WL/61a/07LPP6vrrr9dll11W9/lyCKQAZFqSphCArFi7fJHaW1sK2tpbW7R2+aK6zzlz5kx9/vOf1/Lly3XmmWdqzZo1OvvssxvtKlN7ALItjCkEAI3JTa0HPeV+6aWX6tJLLw2ii8cRSAHItPmd7RotETQ1MoUAoHGrlnQlIleRqT0AmRbGFAKA7GBECkCmhTWFACAbCKQAZF5SphAAxE/DU3tm1mZm95jZL8zsATP72yA6BgAAEHdBjEg9I+li59xTZtYq6cdmdotz7qcBnBsAACC2Gh6Rcp6n/E9b/X+u0fMCAFJiZIt0zWJpoNP7OLIl6h4ho9797nfrhS98oRYvXhzYOQNZtWdmLWb2c0mPSvqhc+7uEse8z8yGzWz4wIEDQTwtACDuRrZIN18hHdwjyXkfb76CYAqReNe73qUf/OAHgZ4zkEDKOXfUOXeupG5J55nZlFDPOfdl51yvc653zpw5QTwtACDubrtKmiyq0zU54bUDlYQwkvnqV79aL3jBCwLo3AmB1pFyzo1LukPSJUGeFwCQUAf31tYOSIkayQxi1d4cM+v0/98u6bWSftnoeQEAKdDRXVs7ICVqJDOIEal5ku4wsxFJP5OXI/V/AjgvACDplq2XWou222lt99qBchI0ktlw+QPn3IikJQH0BQCQNj1rvI+3XeXdBDu6vSAq1w6U0tHtT+uVaI8Z9toDAISrZ4304fulgXHvI0EUphPSSOZb3/pWvepVr9KuXbvU3d2tr371qw2dT2KLGAAAEDchjWRed911AXSuEIEUkDUjW5hmARB/PWsS8buJQArIktyS4txqmNySYikRv7AAIG7IkQKyJEFLigEgCQikgCxJ0JJiAEgCAikgSyiOCACBIpACsoTiiAAQKAIpIEt61kgrN0sdCySZ93HlZhLNAaTenj17dNFFF+mss87S2WefrcHBwUDOy6o9IGsSsqQYAII0c+ZMffazn9XSpUv15JNP6mUve5le+9rX6qyzzmrovIxIAQCA2BnaPaS+rX3qubZHfVv7NLR7qKHzzZs3T0uXLpUknXzyyTrzzDM1OjracD8ZkQIAALEytHtIA3cN6PDRw5KksUNjGrhrQJK0YuGKhs//61//Wjt37tQrXvGKhs/FiBQAAIiVwR2Dx4OonMNHD2twR+N5TU899ZTe8IY36HOf+5ye97znNXw+AikAABAr+w/tr6m9WpOTk3rDG96gt7/97Vq9enVD58ohkAIAALEyd/bcmtqr4ZzTe97zHp155pn6yEc+Uvd5ihFIAQCAWOlf2q+2lraCtraWNvUv7a/7nHfeeae+8Y1v6Pbbb9e5556rc889V9///vcb7SrJ5gAAIF5yCeWDOwa1/9B+zZ09V/1L+xtKNL/wwgvlnAuqi8cRSAEAgNhZsXBFICv0wsbUHgAAQJ0IpAAAQFOEMbUWpHr6RyAFAABC19bWpscffzy2wZRzTo8//rja2tqmPzgPOVIAACB03d3d2rt3rw4cOBB1V8pqa2tTd3d3TV9DIAUAAELX2tqq008/PepuBI6pPQAAgDoRSAFAo0a2SNcslgY6vY8jW6LuEYAmYWoPABoxskW6+QppcsL7/OAe73NJ6lkTXb8ANAUjUgDQiNuuOhFE5UxOeO0AUo9ACgAacXBvbe0AUoVACgAa0VFmqXS5dgCpQiAFAI1Ytl5qbS9sa2332gGkHoEUADSiZ420crPUsUCSeR9XbibRHMgIVu0BQKN61hA4ARnFiBQAAECdCKQAAADqRCAFAABQJwIpAEBysT0PIkayOQAgmdieBzHAiBQAIJnYngcxQCAFAEgmtudBDBBIAQCSie15EAMEUgCAZGJ7HsQAgRQAoDZxWSnH9jyIAVbtAQCqF7eVcmzPg4gxIgUAqB4r5YACBFIAgOqxUg4oQCAFAKgeK+WAAgRSAIDqsVIOKEAgBQCoHivlgAKs2gMA1IaVcsBxjEgBAADUiUAKAACgTgRSAAAAdSKQAgAAqBOBFAAAQJ0IpAAAAOpEIAUAAFAnAikgLCNbpGsWSwOd3seRLVH3CAAQMApyAmEY2SLdfIU0OeF9fnCP97lEIUMASBFGpIAw3HbViSAqZ3LCawcApAaBFBCGg3trawcAJBKBFBCGju7a2gEAiUQgBYRh2Xqptb2wrbXdawcApAaBFBCGnjXSys1SxwJJ5n1cuZlEcwBIGVbtAWHpWUPgBAApx4gUgPijJheAmGJECkC8UZOrspEtXlmNg3u9xQzL1nNdgCZiRApAvFGTq7xckHlwjyR3IshkxA5omoYDKTNbYGZ3mNmDZvaAmfUH0TEAkERNrkoIMoHIBTEidUTSXznnzpL0SkkfNLOzAjgvAFCTqxKCTCByDQdSzrkx59wO//9PSnpIUlej5wUASdTkqoQgE4hcoDlSZnaapCWS7i7x2PvMbNjMhg8cOBDk0wJIM2pylUeQCUTOnHPBnMjsuZL+RdKnnXM3Vjq2t7fXDQ8PB/K8AJBprNoDQmdm9zrneks9Fkj5AzNrlfQdSd+cLogCAASIwq9ApIJYtWeSvirpIefc3zfeJQAAgGQIIkfqAkl/JuliM/u5/+/SAM4LAAAQaw1P7TnnfizJAugLAABAorBFDJBA23aOatP2Xdo3PqH5ne1au3yRVi2h6ggANBuBFJAw23aOat2N92li8qgkaXR8QutuvE+SCKYAoMnYaw9ImE3bdx0PonImJo9q0/ZdEfUIALKLQApImH3jEzW1AwDCQyAFJMz8zvaa2gEA4SGQAhJm7fJFam9tKWhrb23R2uWLIuoRUIORLdI1i6WBTu/jyJaoewQ0hGRzIGFyCeWs2kPijGyRbr5CmvSnoQ/u8T6XqM6OxApsr71asNceAGTQNYu94KlYxwLpw/c3vz9AlULfaw8ICvWRgBQ7uLe2diABCKQQG2HXRyJIAyLW0V1mRKq7+X0BAkKyOWIjzPpIuSBtdHxCTieCtG07Rxs+N1CXLCZdL1svtRatLm1t99qBhCKQQmyEWR+JIpaIlVzS9cE9ktyJpOu0B1M9a6SVm72cKJn3ceVmEs2RaEztITbmd7ZrtETQFER9JIpYIlZuu+rEyrWcyQmvPe1BRc+a9H+PyBRGpBAbYdZHooglYoWkayA1CKQQG6uWdGnD6nPU1dkuk9TV2a4Nq88JJCGcIpaIlXLJ1SRdA4nD1B5iZdWSrlBW0lHEErGybH1hYUqJpGsgoQikkBlhBWlAzXI5Qrdd5U3ndXR7QRS5Q0DiEEgBQBRIugZSgRwpAACAOhFIAQAA1IlACgAAoE4EUgCA+MviljpIBJLNAQDxlttSJ1cuIreljkTCPiLHiBQAIN4qbakTB4yWZRojUgCAeIvzljqMlmUeI1IAgHiL85Y6cR8tQ+gIpAAA8bZsvbeFTr64bKkT59EyNAWBFAAg3nrWSCs3Sx0LJJn3ceXmeEyd1ThaNrR7SH1b+9RzbY/6tvZpaPdQiJ1DM5AjBQCIv7huqVPDBtRDu4c0cNeADh89LEkaOzSmgbsGJEkrFq5oRm8RAkakAACoVw2jZYM7Bo8HUTmHjx7W4I7BJnUWYWBECgCARlQ5Wrb/0P6S7WOHxoLuEZqIESkAaAJyYzB39tyyj/F+SC4CKQAIWS43ZuzQmJzc8dwYbp7Z0r+0v+xjTO8lF4EUAISM3BhIlRPKy037If4IpAAgZOVuktw8s2fe7Hkl2ytN+yHeCKQAIGTlbpLcPLOnf2m/2lraCtraWtoqTvsh3gikAKBYwJvQhnrzZMPcRFmxcIUGzh/QvNnzZDLNmz1PA+cPUEcqwcw51/Qn7e3tdcPDw01/XgCYVvEmtJJXYLHBStpDu4c0uGNQ+w/t19zZc9W/tL/xm2dIfQVQyMzudc71lnyMQAoIycgWb+PSg3u97SKWrefmlgTXLJYO7pna3rFA+vD9ze9PJUnqK5BglQIpCnICYSgeKTi4x/tcSm0wtW3nqDZt36V94xOa39mutcsXadWSrqi7VZ38oFdl/riM4ya0bJgLRI4cKSAMt11VON0ieZ/fdlU0/QnZtp2jWnfjfRodn5CTNDo+oXU33qdtO0ej7tr0ckHvwT0qG0RJ5TenjVKNG+YCCB6BFBCGRkcKEpZAvGn7Lk1MHi1om5g8qk3bd0XUoxqUCnqLldmENnLL1kut7RqaPUt93fPVc9oC9S3o0tCS10fdMyAzCKSAMDQyUlA8QpKbFoxxMLVvvHQgUq49VioGt5U3oY1czxoNXfBeDcw5RWOtM+XMNDazRQN7f0DVdKBJCKSAMPgjBQWqHdVI4LTg/M72mtqD1tA+dmWD3gXSwLiXtB3HIMo3+NjdOmxW0EbVdKB5CKSAMPSs8UYxOhao5lGNBCYQr12+SO2tLQVt7a0tWrt8UejP3fA+do0EvTFA1XQgWqzaA8LSs6a+kYyO7jJL2uObQJxbnRfFqr1K+9hVVacp9xoltFTF3NlzNXZorGQ7gPARSAFxs2x96SKLMR8hWbWkK5JyB4GMyNQb9MZA/9J+Ddw1UBBMsuUI0DwEUgmV6Jo9qCzhIyTNlvURmdyoW+BV0wFUhcrmCZSr2ZO/3Ly9tUUbVp9DMIXMyeVIFY/IsH8ZgKBUqmxOsnkCJbpmDxAwNoEFECWm9hIo0TV7gBCsWLiCwKkYez0CTcGIVAJFXbMHQMwlsKgrkFQEUgkUZc0eAAmQwKKuQFIxtZdAUdbsAZAACSzqCiQVgVRCRVWzB0ACJLCoK5BUTO0BQNokfNsbIEkIpAAgbRrZ6xFATZjaA4A0SvC2N0CSMCIFAFkyskW6ZrE00Ol9pCRCagztHlLf1j71XNujvq19Gto9FHWXMoERKQBoRJIKX+bqS+VKI+TqS0nx7TOqUrxV0tihMQ3cNSBJFKsNGSNSAIKRxZGOpBW+pL5Uag3uGCzYb1KSDh89rMEdgxH1KDsIpAA0LmkBRVCSFphQXyq19h/aX1M7gkMgBaBxSQsogpK0wKRcHSnqSyXe3Nlza2pHcAikADQuaQFFUJIWmFBfKrX6l/arraWtoK2tpU39S/sj6lF2EEgBaFzSAoqgJC0wob5Uaq1YuEID5w9o3ux5MpnmzZ6ngfMHSDRvAnPONf1Je3t73fDwcNOfF0BIileDSV5AkYWbdJJW7QGoi5nd65zrLfUY5Q8ANC4XOGQxoKDwJZBpgQRSZvY1SX8q6VHn3OIgzgkgYeIUUDBKBKBJgsqR+rqkSwI6FwDUL6ulGABEIpBAyjn3I0m/C+JcQBC27RzVBRtv1+lXDumCjbdr287RqLuEnLALd2a1FAOASDQtR8rM3ifpfZL0ohe9qFlPiwzatnNU6268TxOTRyVJo+MTWnfjfZKkVUu6ouwamrFFSVZLMQCIRNPKHzjnvuyc63XO9c6ZM6dZT4sM2rR91/EgKmdi8qg2bd8VUY9wXDNGi7JaigFAJKgjhdTZNz5RUzuaqBmjRUmr7QQg0QikkDrzO9trakcTVRotCip3iqKTAJookEDKzK6T9BNJi8xsr5m9J4jzAvVYu3yR2ltbCtraW1u0dvmiiHqE48qNFr2kL9iVdj1rpA/fLw2Mex8JogCEJKhVe291zs1zzrU657qdc18N4rxAPVYt6dKG1eeoq7NdJqmrs10bVp9DonkclBstevhWVtoBSCS2iAEQvYFOSaV+F5k3qgQAEaq0RQw5UgCil/aVdmHXzgIQGQIpANFL80o7Kq0DqUYgBSB6aV5pR6V1INWaVtkcACoq3vQ4Nx2W9I2Hs1ppnY2jkREEUgDipxlbyTRLR7c/rVeiPa3S9PoB02BqD0D8pGk6rJ78r6Qnp6fp9QOmwYgUgPhJ03RYbgSm2mmuNIzmpOn1A6ZBIAUgftI2HVac/1VJpdGcpARSaXv9gAqY2gMQP2kuhzCdNIzmZPn1Q+YwIgU02dBT2XEAACAASURBVLado9q0fZf2jU9ofme71i5fxPY1xWqdDkuTZo/mhLG6LsuvHzKHLWKAJtq2c1TrbrxPE5NHj7e1t7awFyBOKM6RkrzRnDDqajXzuYAEY4sYICY2bd9VEERJ0sTkUW3aviuiHiF2mlmclNV1QMOY2gOaaN/4RE3tyKhaktMbkYZ8LCBijEgBTTS/s72mdiAUuTpVKpPaweo6oGoEUkATrV2+SO2tLQVt7a0tWrt8UUQ9QuYUbKJcQo2r64Z2D6lva596ru1R39Y+De0eCqijQDIwtQc0US6hnFV7iEypvKicjgU1ra4b2j2kgbsGdPjoYUnS2KExDdw1IElasXBFEL0FYo9ACmiyVUu6CJwQnbL5TyZ9+P6aTjW4Y/B4EJVz+OhhDe4YJJBCZjC1BwBZUi7/qY68qP2H9tfUDqQRgRQAZEmAVcfnzp5bUzuQRgRSQKNyK6AGOr2PI1uy2QckQ4B1qvqX9qutpa2gra2lTf1L+wPqLBB/5EgBjSiuDH1wj/e51LzK0HHoA5IloDpVuTyowR2D2n9ov+bOnqv+pf3kRyFT2CIGaMQ1i8vsi7ag5sTdRPcBAFKMLWKAsMShMnQc+tAMTF8CiCECKaARAa6ASnQfwlZQRNKdmL4kmAIQMQIpoBEBroBKdB+CVjz6dMtfs7kugFgi2RxoRC5h97arvKm0ju6aKkOnpg9BKpU8X07api8BJA6BFNCogFZAJb4PQam0hUmxNE1fAkgkpvYAxEu1o0xJn74EkAoEUgDipdwoU/sLAikiCQBBYmoP0RrZkp7cHgRj2frCHCnJG3163d/x3gAQOwRSiA4VuVFK2pLnAaQagRSiUyqpOLeknZtmtqUpeR5AqpEjhehkpSI3ACC1GJFCdDq6y+wRl+4l7dt2jmrT9l3aNz6h+Z3tWrt8kVYt6Yq6WwCAOjAiheiksSL3NLbtHNW6G+/T6PiEnKTR8Qmtu/E+bds5GnXXko19+ABEhEAK0elZ4y1hz9CS9k3bd2li8mhB28TkUW3aviuiHqUA+/ABiBBTe4hWxpKK942Xrthdrh1VYNECgAgxIgU00fzO9praUQUWLaQD07NIKAIpoInWLl+k9taWgrb21hatXb4ooh6lQLnFCSlftJAqTM8iwQikgCZataRLG1afo67Odpmkrs52bVh9Dqv2GpHBRQupU2l6Fog5cqSAJlu1pIvAKUhUQk8+pmeRYARSAJIvY4sWUiejNeWQDkztAQCixfQsEowRKYSC6t0Aqsb0LBKMQAqBy1XvzhWezFXvlkQwhRNGtnDjxAlMzyKhmNpD4KjejWmx3B1AShBIIXBU78a0WO4OICUIpBA4qndjWix3B5ASBFIIHNW7MS2qkQNICQIpBI7q3ZhW2pa7V7FP3NDuIfVt7VPPtT3q29qnod1DEXQUQNBYtYdQUL0bFaVpuXsucT6X85VLnJeOfz9Du4c0cNeADh89LEkaOzSmgbsGJEkrFq5odo9rw+pKoCJzzjX9SXt7e93w8HDTnxcAAnfN4jJVuRdIH75fktS3tU9jh8amHDJv9jzd+sZbq3ueKAKa4iBR8kYOV24mmEKmmNm9zrneUo8xIgUkEAVPY6SKxPn9h/aXPKRc+xRVjHqFotLqSgIpQBI5UkDi5Aqejo5PyOlEwdNtO0ej7lo2VZE4P3f23JKHlGufIqpyEayuBKZFIAUkDAVPY6aKxPn+pf1qa2krOKStpU39S/ure46oAhpWV9anisUHSA8CKSBhKHgaMz1rvJyhjgWSzPtYlEO0YuEKDZw/oHmz58lkmjd7ngbOH6g+0TyqgCZtqyubgar9mUOOFJAw8zvbNVoiaKLgaYSq2CduxcIV9a/QW7a+dNJ32AFNmlZXNgt5ZZlDIAUkzNrliwo2hZYoeJp6UQY0bCZcG/LKModACkiY3Oq8UFftUTsofghokqGju0w5DPLK0opACoiJWkoahFrwNKql9kAaRDUNi8gQSAExkCtpkJuuy5U0kBRYwFR1oEaOB1A/8soyh0AKiIFKJQ2CCKRqCtTI8QAawzRsplD+IGG27RzVBRtv1+lXDumCjbdThDElwi5pUFPtKWoHAUDVCKQShIrW6VWudEFQJQ1qCtTCrh1EsUIAKUIglSBUtE6vtcsXqb21paAtyJIGNQVqVRSYrBvFCgGkDDlSCUJF6/QKu6RBzbWnwsrxSGEi+9DuIQ3uGNT+Q/s1d/Zc9S/tr7/wJoDECSSQMrNLJA1KapH0FefcxiDOi0JUtE63MEsaNKX2VDVSlsg+tHtIA3cN6PDRw5KksUNjGrhrQJIIpoCMaDiQMrMWSV+Q9FpJeyX9zMy+55x7sNFzoxAVrdGIUGtPVStlxQoHdwweD6JyDh89rMEdgwRSQEYEkSN1nqRHnHO7nXPPSrpe0uUBnBdFVi3p0obV56irs10mqauzXRtWnxP9zRGoVrlE9pf0JTIBff+h/TW1A0ifIKb2uiTl/4m5V9Irig8ys/dJep8kvehFLwrgabMpFqMKQL1KFSt8SZ/0i28lspL63NlzNXZorGQ7gGxo2qo959yXnXO9zrneOXPmNOtpAcRNzxrpw/dLA+Pex4dvLZ+AHnP9S/vV1tJW0NbW0qb+pf0R9QhAswUxIjUqaUHe591+GwBML8EJ6Lk8KFbtAdkVRCD1M0kvMbPT5QVQb5H0tgDOCyALEp6AvmLhCgInIMMantpzzh2R9CFJ2yU9JGmLc+6BRs8LICPCrqQOACEKpI6Uc+77kr4fxLkAZEypBPRl62OfaA4AEpXNAcRBWJXUASBk7LUHAABQJwIpAACAOjG1h8zatnM0+r3nACQOG1UjH4EUMmnbztGCfQtHxye07sb7JIlgCsBxxUHTq7tfrZseuYmNqnEcU3toim07R3XBxtt1+pVDumDj7dq2M9qarZu27yrY/FmSJiaPatP2XRH1CEDcDO0e0sBdAxo7NCYnp7FDY7ph1w1lN6pGNhFIIXS50Z/R8QmtnPFj3fD0e3XZtrP19N+9NLLNafeNT9TUDiB7BncMTgmaymGj6uwikELocqM/l834sTa2fkXdMx7TDHOaNTHmbU4bQTA1v7O9pnYA2VNLcMRG1dlFIIXQ5UZ5Pjpzi2bZs4UPRrQ57drli9Te2lLQ1t7aorXLFzW9LwDiqdrgiI2qs41ACqHLjfLMt8dKHxDB5rSrlnRpw+pz9PxZrcfbnjOTHwcAJ/Qv7VdbS1tBW1tLm9686M2aN3ueTKZ5s+dp4PwBEs0zjFV7KCnI0gBrly/Suhvv0z53qrpLBVMRbk57ePLY8f+PT0yycg/AcbngiFIHqIRAKkOqrX0SdGmA3Nd8Zegd+ujkFwun9yLcnLbSyj0CqWyhLhDKWbFwBe8FVMRcRkaUWsY7cNeAhnYPTTk2jNIAq5Z0aeDjf6tZb/iC1LFAknkfV26ObI81Vu5Bqu1nAwCKMSKVEaWW8eZqnxT/tRVqgBGjzWnnd7ZrtMT3xMq9bKnlZwOQGMFEIUakMqLcMt5S7VkpDcDKPUi1/WwAjGCiGIFURpRbxpvfnqs+Pjo+ISs6Lo0BRm7lXldnu0xSV2e7Nqw+h/yojKnmZwPIqTSCiWxiai8j+pf2a+CugYJfAPm1T4oTzJ0k8z92pXhD31VLulL5faF60/1sNANTRcnBCCaKEUhlxHTLeEslmOeCqDuvvLjZ3QWaJuol7rmpIjbBTYa5s+dq7NBYyXZkkznnmv6kvb29bnh4uOnPi/JOv3JIpd4JJulXG/llDoSlb2tfyRvzvNnzdOsbb42gR6ikOPCVvBHMKItyMqIZPjO71znXW+oxRqQgiRVsQFSYKkqWqEcwizGiGT0CKUg6UX08f3ovjQnmQNwwVZQ8zSzSOd1oE+U7oseqPUhiBRtQ0cgW6ZrF0kCn93FkS2CnLrefW5Y2wR3aPaS+rX3qubZHfVv7qiolUM/XJE01pRYY0YweOVIAUMnIFunmK6TJvKnv1vZAq/JnOcelnpyjUl8jSZ3P6dSV512ZmmtXTf4cOXbNUSlHikAKFQW5eTGQSNcslg7umdresUD68P3N70/K1BMIlPsaKfrE7yD1XNsjV2IZkMk08s4RSeWDSknqOKlD616xLhXXImqVAimm9lBWrrbU6PiEnE5sXrxt52jUXQOa5+De2toxRaVpuHqmpio9lqbimNUUi12xcIUGzh9Qx0kdU447+OxBffzHH0/ltGecEEihrDA2LwYSp6O7tnYUmC7Pp57K8tMl4qclP6ja/LkVC1doVuuskuc44o6kJrCMKwIplBXq5sUxkdsW5/Qrh3TBxtsZbcNUy9Z7OVH5Wtu9dpSUPwL1sR9/rOKWKvUk25f6mnxpWfGYG22aN3ueTKZ5s+eVnbasdwQPjaP8AcpKe22p4m1xclOXksgDa1CqcutyCeW3XeVN53V0e0FUQInmzZKf0N7xnA455/TEs0/UndxeLkG+OGenXB5u7uZeT12m3GMb7t6gg88eLHgsbSseqy21UK6MRu4xhIdkc5RVHGhIXm2ptJRFyG3QXIxtcRqT9vdNElVKSM5586I36+Ov/Hjd58sleQ/uGCx7Q88X1KqyLK94zDe0e0ifuPMTmjw2WdA+02bqUxd+KpPXJEhUNkddcje91IwsFMnC1GUUKuXWpeW9M5243dxLFW0sdsOuG7TkhUuq6melIpDVTCMFOWrUzOKYcZa7Bhvv2ajxZ8YlsWqvWQikUNGqJV2pvfmlfeoyKkkMUIMMfJq1ZUctfa42R6baatiVVtqVm2KaYTPknItFYJlWBJXRINkcmbV2+SK1t7YUtLEtTuPKBaJxDVCrqR5di0qjNUGptc/V5shUG3BVWmlXLnn8Mxd+RiPvHNGtb7y1ppt9WiqYp+X7wFQEUsgstsUJx0UvnVNTe9SCDnyasWVHrX2ebpVbTrUBV6WVdrWsNJtO0EFuVNLyfaA0pvaQaWFOXcZp5Voz+3LHLw/U1B61oAOfZmxCXGufi1fGtc9s19NHni44Jj9vabppw+lW2lUzxVTN1GQQG/LGIV+NjYWrF4fXq1YEUohEnIKMMMSptEKz+5K0HKnnnfS8KUvoc+316F/aX3JF23TJ1bXcQOoJ1oqDm0rlC/JXf40dGtMn7vzE8XOUO18tqs0jazTIbVa+2nTK9Xfs0Jh6ru1JTMAQtri8XrViag9Nl4WtZ+JUFb7ZfUlajpSZ1dQ+nXqmtmqd+qmniGWpft76xlun5C1tvGfjlCX0k8cmtfGejVWfezrVTk3WU/W8nucJW6X+MtV3Qlxer1oRSKHp4hRkhCVOozLN7kvSkvgPPjN1NKpSezXKBSnl1HoDCTIPqVhu6Xy17fWodqSploCxVDJ3LSNaYSaDV5OjVlPAMLLF20x7oNP7OLIlgF5Grxn5hWFgag9lhTX9FqcgIyxxKq3Q7L7Esf5YpWmzZuQ0TaeeG0jNU2sjW2JTnb3cdGrxNa+26nm5KaGO53SUDACLnyfsKaX876NSsdKqAoaRLdLNV0iT/s/0wT3e51Liqu0Xi8PPYj0YkUJJYU6/JW3qpx5xGpWJoi+rlnTpzisv1q82rtCdV14ceRBVadqsf2m/Wme0FnxN64zWpm4z0ugU1rRyN9+DeyQ56eAePf2dD2rgU/9jys90x0kdZU8TxEjN0O6hKYnukleBu9Q1X7FwhfqX9mvu7Lnaf2i/BncMTulDuRE951xVI1rNmFLKjVKayk8ZV/V633bViSAqZ3LCa0+4IKaso0AghZLCnH6LU5ARljiVVohTX6JQzU2yeKusZm6dNbR7SBNHpo4YBnoDKXHznWXP6i+e/acpfyCte8U6zbTSkxVB5PIM7hickoMlSc896bklR3+qyR8rN5LzxLNPVDUF2swppUrBUlWv98G9tbUnSK1T1nGpzcXUHkoKc/otjlM/YWi0tEKQU6tprlA/nelukoM7BnXEHSl47Ig70pSl6eX2wAt8a48yN9n59rgmni3cvme6aahGl+2Xez3K5aRVUzqg0pRQNVOglb4+6OX4pVZ1St5eh1Wdt6PbH1ks0Z4C1U5Zx2mFHyNSKCns6bc4Tf1Ua9vOUV2w8XadfuWQLth4e/3TnFUkik43tRpYXzJgummzKBNcy+2BN6t1VrA3gzI32X3uFO9j0R9I001DNXJtap3GrOb1aXRKqNzXv7r71YEX0iw16rLxP22sesNoLVsvtRb9Hm5t99ozJE4r/AikUFIWpt9qEVjOWIlcFd18xZRgqtLUahbKR0jBDdtPd5MNPT+pgqYFcSVuvk+7k3T1ES85udwfSGFcm1qDnmr60OgqxnJf/6O9PwrkZl38XpZU06rOAj1rpJWbpY4Fksz7uHJz4hPNaxWnFX5M7aGkrEy/VatSYFPTNamUKJr3i7DS1GpgfYmxIIftp1v5VW8BzSA0bZWS/956+pb1ant6v/a5U3T1kTX63rELK/6BFMa1qXYlXq19aHTD3lJfv+5f15U8tpabdShTUD1rMhc4FYvTCj8CKZSV5byaYoHljFWZKFqpZEEWykcEvaVGpZtsrTf2IDU1iOtZo1k9awpy77qm+QMprGtTS9CzYuEK7Xx0p779b9/WMXdMM2yGLj/j8qa8PkHcrNkeJhxR/gFUjEAKqEJgtZiqTBRdu3xRwbYu0omp1U3bd8WmRlVYmj1s3+hoRiPPKzU3iKv1D6Sork3O0O4h3fTITTrmjkmSjrljuumRm7TkhUtC71cQN+s4TUGlSZR/ABUjkAKqUCmwqcmy9YXF9KSSiaLTTa0W98UkXfTSObX1JUBBF2+N07B92KIOVKI23aq4KEd0grhZZ+m93Gxx+dkhkAKqEFjOWC6voYoK0+VGDlYt6dLwb36nb/70P5SrduQkfefeUfW++AXB9LMGYWyKHKdhe4SnmvyhqEd0Gr1Z815OP2tm4bmc3t5eNzw8HMq5w9rWBMmS9vfBBRtvLzm99/xZrTo8eWzKyFmYBTjL9aWrs113Xnlx3ecNun4P4qdva1/J0Zp5s+fp1jfeWvGYGTZDzrlEvDd4Lyefmd3rnOst9ViqRqTC+MsYyZOF90G5xPLfPz21YnTYK/rCSn6Patiem1648q+vU+k/5ItrRJUqYJnLmYqyEGO14jIFhXCkqo5UmNuaIDmy8D6oNbE8zBV9ado7sZrtSFC/4utbTqUaUTNs6m0rqkKMUny2KUF0UhVIZWFZeFqEWZk7C++DcgVTO9tbSx4fZlCTpuKtcaqWnEblKrnnK1cjKlfAslw6ShSr4Ai8IaUskErTX8ZpFnZl7jDfB3HZmqXcRsQDl53d9KAmTZsiR53YnHaVrmO1FcmjrERfjMAbUspypAJboo5QhV2ZO6z3QdxyryrVA2p2on1aireyVD08Q7uHZGYlR5Tyk8unE6dVcATekFIWSLGtSTKEPfUW1vsgKVuzpCWoiUKcbtJpkpsCyyWI56v1+sapECOBN6SUBVISN5EkCKxKeAVhvA+CCgDTXpohyeJ0k06TcrlRM2xGTZsL58RlFRyBN6QUBlKIvzhNwdYS1AQRAMZtehBTxeUmnSblprqcc4m+1gTekAikEIG4TMHWGtQEEQAmZXoQCFKap8AIvEEghUjEYQq21qAmiAAw0aUZRrZUtbUNUIwpMKQZgRQkZTNvp56gptEAsBn5YUEofj987qyH9fL7/seJzZYP7vE2X5bCD6YI4BIv6Ckwqs8jTgikkNm8nSiCmjjlh5VT6v0w/96rJSu6VpMTXoATZlAzssUL2KII4BCooKbAqtnoGGimVBXkRH2ysKVKKRe9dE5N7UFIQvHKUu+HeXqs9MEH94bbmduuOhFE5eQCOGQSRTARN4xIIdl5Ow2445cHamoPShzywyop9brvc6eq20oEUx3d4XamXKAWdgCH2KIIJuKGESlkdmudoAPIuGwfU7eRLdI1i/XvbW/Xj0+6QpfN+PHxh64+skYTek7h8a3tXr5SmMoFamEHcIitOG0RA0gEUlC6Np2tRZABZNj7B4Yul4t0cI9myKl7xmPa2PqV48HUD1v+WPcv/aTUsUCSeR9Xbg4/T2nZei9gy9eMAA6x1b+0X20tbQVtrABElJjaQ2zqOjVbkInfia8PVSIXaZY9q4/O3KJ7Z71Wa5cv0suXXCLp/c3tVy5QY9UefBTBRNxYqQ0kw9bb2+uGh4eb/rxAsaDKPpx+5ZBK/SSZpF9tjMEv+OlKCAx0SuW+g4HxZvUSKZDFUipIPzO71znXW+qxhkakzOxNkgYknSnpPOcc0RESJajE71jXh6qmhEBHt9dejFwk1CCrpVSQbY3mSN0vabWkHwXQFyCxwsgzG9o9pL6tfeq5tkd9W/s0tHuovhNVU0KAXCQEIKulVJBtDY1IOecekiQzC6Y3QEIFnWcWaNHBakoIkIuEAGS1lAqyjWRzIABB54VUKjpYcyBV7bRdzxoCJzQk1lPcQEimndozs382s/tL/Lu8licys/eZ2bCZDR84EG7BQ6CZwih9EGjRQabt0CRZLaWCbJt2RMo595ognsg592VJX5a8VXtBnBOIgzBKHzyvdY4OTj5asr1mTNuhSbJaSgXZxtQe0KAw8kKeeXS5XMf1shmTx9vcsVY98+jy+k7ItB2aJO5bIAFBa2jVnpm93sz2SnqVpCEz2x5Mt4DmCGJblzC22Hls/9k6PLZax57tlHPSsWc7dXhstR7bf3bd5wQABK/RVXvflfTdgPoCNFVQNW+CrJCe0zmrVb9/YomOPLGkoP35s1rrPicAIHjstYfMCqrmzaolXdqw+hx1dbbLJHV1tmvD6nNqm97wNwzWQKd0zWJdcuxfSx4WwUYEAIAKyJFCojVSdiDI3KaG8kJKVB7/hPuSDs04ou8du7Dg0IMTkyVOAACICiNSSKxGyw6EkdtUlwobBhejHg8AxAuBFBKr0am52NS8KVN5fL49PqXtopfWUf4AABAapvaQWI1OzcWm5k2ZyuP73ClT2u74JcVsASBOCKSQWEFsRxGLmjfL1hfmSEl62p2kq49MrfvEnmUAEC9M7SGxYjM116ieNdLKzVLHAkkmdSzQ1a1/OSXRXCJHKipDu4fUt7VPPdf2qG9rn4Z2D0XdJQAxwYgUEis2U3MB2Hb0Am16ZrP2HZ7Q/LZ2XbR4jtrvHQ20NhXqM7R7SAN3DRzfRHrs0JgG7hqQpNo3kAaQOuYiKEzT29vrhoeHm/68QBwVFwaVvKDpDS/r0h2/PBDbILGR0hNJ0re1T2OHxqa0z5s9T7e+8dYIegSg2czsXudcb6nHGJECIlZu9eEdvzygO6+8OKJeVRZUVfgk2H9of03tALKFHCkgYmFsehy2oKrCJ8Hc2XNrageQLQRSQMTKJZB3zmpteEPlsCQx+KtX/9J+tbW0FbS1tbSpf2l/czpQtH2QRqYWagUQHab2kEpJyt8ptelxa4vpqcNH9PunvS1h4jZ1FkTpiaTIJZQP7hjU/kP7NXf2XPUv7W9OonmJ7YN08xXe/3umlscA0HwkmyN1yiVv17yRcBMVB36Hnjmi8RL76nV1tscibyqJ1ziRrllcslirOhZIH76/+f0BMopkc8RWGCNHlfJ34nqTLy4MevqVpesUxWXqLA2lJxIxallm+6Cy7QCajkAKkQlr5Vca8neSMHUWi6rwdUrMqsMy2wepo7v5fQFQEsnmiExYK7/KBRtxCkKmk5qq7TGVmFWHy9ZLrUXv29Z2rx1ALBBIITJhjRylIQhZtaRLG1afo67Odpm83Cjyj4KTmFHLEtsHaeVmEs2BGGFqD5EJa/oqDfk7UrKnzuIuCVOnx/WsIXACYoxACpEptew/qJEjghBUEuZ7D0C2EEghMmkZOULy8N4DEBTqSAEAAFRQqY4UyeYAAAB1IpACAACoE4EUAABAnUg2j7FEbGEBAECGEUjFVGK2sAAAIMOY2oupxGxhAQBAhhFIxVRitrAAACDDCKRiKg0b7wIAkHYEUjGVho13AQBIO5LNY4otLAAAiD8CqRhj410AAOKNqT0AAIA6EUgBAADUiUAKAACgTgRSAAAAdSKQAgAAqBOBFAAAQJ0IpAAAAOpEIAUAAFAnAikAAIA6EUgBAADUiUAKAACgTgRSAAAAdTLnXPOf1OyApN80/YmT41RJj0XdCVSN1ytZeL2ShdcredL4mr3YOTen1AORBFKozMyGnXO9UfcD1eH1ShZer2Th9UqerL1mTO0BAADUiUAKAACgTgRS8fTlqDuAmvB6JQuvV7LweiVPpl4zcqQAAADqxIgUAABAnQikAAAA6kQgFVNmtsnMfmlmI2b2XTPrjLpPKM/M3mRmD5jZMTPLzLLfpDGzS8xsl5k9YmZXRt0flGdmXzOzR83s/qj7gumZ2QIzu8PMHvR/F/ZH3admIZCKrx9KWuyc65H0b5LWRdwfVHa/pNWSfhR1R1CambVI+oKk10k6S9JbzeysaHuFCr4u6ZKoO4GqHZH0V865syS9UtIHs/LzRSAVU865W51zR/xPfyqpO8r+oDLn3EPOuV1R9wMVnSfpEefcbufcs5Kul3R5xH1CGc65H0n6XdT9QHWcc2POuR3+/5+U9JCkrmh71RwEUsnwbkm3RN0JIOG6JO3J+3yvMvKLHmgmMztN0hJJd0fbk+aYGXUHsszM/lnS3BIP/Y1z7ib/mL+RN2T6zWb2DVNV83oBQJaZ2XMlfUfSf3POPRF1f5qBQCpCzrnXVHrczN4l6U8lLXMU/IrcdK8XYm9U0oK8z7v9NgABMLNWeUHUN51zN0bdn2Zhai+mzOwSSR+VdJlz7umo+wOkwM8kvcTMTjezkyS9RdL3Iu4TkApmZpK+Kukh59zfR92fZiKQiq/PSzpZ0g/N7Odm9qWoO4TyzOz1ZrZX0qskDZnZ9qj7hEL+4o0PSdouLxF2i3PugWh7hXLM7DpJP5G0yMz2mtl7ZWSiygAAAE9JREFUou4TKrpA0p9Juti/Z/3czC6NulPNwBYxAAAAdWJECgAAoE4EUgAAAHUikAIAAKgTgRQAAECdCKQAAADqRCAFAABQJwIpAACAOv3/ITdgAVtMVQMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**: The new model is better than the first"
      ],
      "metadata": {
        "id": "3E9iVvBpojIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# add these cluster labels to our dataset\n",
        "df['agg_cluster_group'] = labels"
      ],
      "metadata": {
        "id": "VQ4ms3eBq20X"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "YLlbXLCSq_xi",
        "outputId": "c6bc31a2-db86-494f-8150-94f4eea2bf82"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Alcohol  Malic_Acid   Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
              "114    12.08        1.39  2.50          22.5         84           2.56   \n",
              "169    13.40        4.60  2.86          25.0        112           1.98   \n",
              "22     13.71        1.86  2.36          16.6        101           2.61   \n",
              "153    13.23        3.30  2.28          18.5         98           1.80   \n",
              "134    12.51        1.24  2.25          17.5         85           2.00   \n",
              "151    12.79        2.67  2.48          22.0        112           1.48   \n",
              "102    12.34        2.45  2.46          21.0         98           2.56   \n",
              "137    12.53        5.51  2.64          25.0         96           1.79   \n",
              "156    13.84        4.12  2.38          19.5         89           1.80   \n",
              "53     13.77        1.90  2.68          17.1        115           3.00   \n",
              "\n",
              "     Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity   Hue  \\\n",
              "114        2.29                  0.43             1.04             2.90  0.93   \n",
              "169        0.96                  0.27             1.11             8.50  0.67   \n",
              "22         2.88                  0.27             1.69             3.80  1.11   \n",
              "153        0.83                  0.61             1.87            10.52  0.56   \n",
              "134        0.58                  0.60             1.25             5.45  0.75   \n",
              "151        1.36                  0.24             1.26            10.80  0.48   \n",
              "102        2.11                  0.34             1.31             2.80  0.80   \n",
              "137        0.60                  0.63             1.10             5.00  0.82   \n",
              "156        0.83                  0.48             1.56             9.01  0.57   \n",
              "53         2.79                  0.39             1.68             6.30  1.13   \n",
              "\n",
              "     OD280  Proline  kmeans_cluster_group  agg_cluster_group  \n",
              "114   3.19      385                     1                  0  \n",
              "169   1.92      630                     0                  1  \n",
              "22    4.00     1035                     2                  2  \n",
              "153   1.51      675                     0                  1  \n",
              "134   1.51      650                     0                  1  \n",
              "151   1.47      480                     0                  1  \n",
              "102   3.38      438                     1                  0  \n",
              "137   1.69      515                     0                  1  \n",
              "156   1.64      480                     0                  1  \n",
              "53    2.93     1375                     2                  2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7a9ad6de-e238-42c7-8d58-e6c20a5b41cf\">\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_Acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Ash_Alcanity</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total_Phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid_Phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color_Intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280</th>\n",
              "      <th>Proline</th>\n",
              "      <th>kmeans_cluster_group</th>\n",
              "      <th>agg_cluster_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>12.08</td>\n",
              "      <td>1.39</td>\n",
              "      <td>2.50</td>\n",
              "      <td>22.5</td>\n",
              "      <td>84</td>\n",
              "      <td>2.56</td>\n",
              "      <td>2.29</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0.93</td>\n",
              "      <td>3.19</td>\n",
              "      <td>385</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>13.40</td>\n",
              "      <td>4.60</td>\n",
              "      <td>2.86</td>\n",
              "      <td>25.0</td>\n",
              "      <td>112</td>\n",
              "      <td>1.98</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.11</td>\n",
              "      <td>8.50</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.92</td>\n",
              "      <td>630</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>13.71</td>\n",
              "      <td>1.86</td>\n",
              "      <td>2.36</td>\n",
              "      <td>16.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.61</td>\n",
              "      <td>2.88</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.69</td>\n",
              "      <td>3.80</td>\n",
              "      <td>1.11</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1035</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>13.23</td>\n",
              "      <td>3.30</td>\n",
              "      <td>2.28</td>\n",
              "      <td>18.5</td>\n",
              "      <td>98</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.87</td>\n",
              "      <td>10.52</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.51</td>\n",
              "      <td>675</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>12.51</td>\n",
              "      <td>1.24</td>\n",
              "      <td>2.25</td>\n",
              "      <td>17.5</td>\n",
              "      <td>85</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.25</td>\n",
              "      <td>5.45</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.51</td>\n",
              "      <td>650</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>12.79</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2.48</td>\n",
              "      <td>22.0</td>\n",
              "      <td>112</td>\n",
              "      <td>1.48</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.26</td>\n",
              "      <td>10.80</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.47</td>\n",
              "      <td>480</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>12.34</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.46</td>\n",
              "      <td>21.0</td>\n",
              "      <td>98</td>\n",
              "      <td>2.56</td>\n",
              "      <td>2.11</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.31</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.80</td>\n",
              "      <td>3.38</td>\n",
              "      <td>438</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>12.53</td>\n",
              "      <td>5.51</td>\n",
              "      <td>2.64</td>\n",
              "      <td>25.0</td>\n",
              "      <td>96</td>\n",
              "      <td>1.79</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.63</td>\n",
              "      <td>1.10</td>\n",
              "      <td>5.00</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.69</td>\n",
              "      <td>515</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>13.84</td>\n",
              "      <td>4.12</td>\n",
              "      <td>2.38</td>\n",
              "      <td>19.5</td>\n",
              "      <td>89</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.56</td>\n",
              "      <td>9.01</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.64</td>\n",
              "      <td>480</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>13.77</td>\n",
              "      <td>1.90</td>\n",
              "      <td>2.68</td>\n",
              "      <td>17.1</td>\n",
              "      <td>115</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.79</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.68</td>\n",
              "      <td>6.30</td>\n",
              "      <td>1.13</td>\n",
              "      <td>2.93</td>\n",
              "      <td>1375</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a9ad6de-e238-42c7-8d58-e6c20a5b41cf')\"\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KX6mL6M8iMU"
      },
      "source": [
        "## 5. Summary of Findings\n",
        "\n",
        "The optimal alue of k was 3 for both models. However, none of the models has clear cut boundaries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVOLFUk28iMV"
      },
      "source": [
        "## 6. Recommendations\n",
        "\n",
        "I would recommend further improving the models so that clear boundaries between clusters can be achieved."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "det4_d9T8iMW"
      },
      "source": [
        "## 7. Challenging your Solution\n",
        "\n",
        "#### a) Did we have the right question? Yes\n",
        "\n",
        "\n",
        "#### b) Did we have the right data? Yes\n",
        "\n",
        "\n",
        "#### c) What can be done to improve the solution? \n",
        "- Try other clustering algorithms \n",
        "\n",
        "- Address highly correlated variables and outliers"
      ]
    }
  ]
}