Market_Basket_analysis.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Mall_Customers.csv\")\n",
        "df.shape"
      ],
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            "text/plain": [
              "(200, 5)"
            ]
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        "df.head()"
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          "data": {
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
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            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
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              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
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              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>70.000000</td>\n",
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              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4459bd48-6673-4acb-9725-e5d469f1865e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4459bd48-6673-4acb-9725-e5d469f1865e')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4459bd48-6673-4acb-9725-e5d469f1865e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PK1ZkKNyhM1",
        "outputId": "7b5319f4-e589-44bd-cb30-96049883aee7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
              "       'Spending Score (1-100)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Gender.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qArjzOwykvr",
        "outputId": "d8bb7cfd-7df7-4b83-d644-84c7c90c5157"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    112\n",
              "Male       88\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Annual Income (k$)'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onH7XwKnynrR",
        "outputId": "bdc39cd7-9c30-4bd5-9a79-991b45377fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60.56"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Spending Score (1-100)'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfnVIFkXyq-H",
        "outputId": "7c955fd9-6863-4704-8d04-99906342d65a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50.2"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['Gender']).mean()[['Annual Income (k$)','Spending Score (1-100)']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "8snueJisytsQ",
        "outputId": "e72aaafb-f7ed-4716-a812-1ce093ee6890"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Annual Income (k$)  Spending Score (1-100)\n",
              "Gender                                            \n",
              "Female           59.250000               51.526786\n",
              "Male             62.227273               48.511364"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>59.250000</td>\n",
              "      <td>51.526786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>62.227273</td>\n",
              "      <td>48.511364</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      background-color: #E8F0FE;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-9450505a-b332-4fd6-a47f-0ee7c9260307 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-9450505a-b332-4fd6-a47f-0ee7c9260307');\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",
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              "\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2663cdfd-b034-47d0-b820-9d6137c99361 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[ : , [3,4] ].values\n",
        "X # X is a numpy array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGFuJrWdyylB",
        "outputId": "0398793b-333f-4122-9f25-3c46151245d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 15,  39],\n",
              "       [ 15,  81],\n",
              "       [ 16,   6],\n",
              "       [ 16,  77],\n",
              "       [ 17,  40],\n",
              "       [ 17,  76],\n",
              "       [ 18,   6],\n",
              "       [ 18,  94],\n",
              "       [ 19,   3],\n",
              "       [ 19,  72],\n",
              "       [ 19,  14],\n",
              "       [ 19,  99],\n",
              "       [ 20,  15],\n",
              "       [ 20,  77],\n",
              "       [ 20,  13],\n",
              "       [ 20,  79],\n",
              "       [ 21,  35],\n",
              "       [ 21,  66],\n",
              "       [ 23,  29],\n",
              "       [ 23,  98],\n",
              "       [ 24,  35],\n",
              "       [ 24,  73],\n",
              "       [ 25,   5],\n",
              "       [ 25,  73],\n",
              "       [ 28,  14],\n",
              "       [ 28,  82],\n",
              "       [ 28,  32],\n",
              "       [ 28,  61],\n",
              "       [ 29,  31],\n",
              "       [ 29,  87],\n",
              "       [ 30,   4],\n",
              "       [ 30,  73],\n",
              "       [ 33,   4],\n",
              "       [ 33,  92],\n",
              "       [ 33,  14],\n",
              "       [ 33,  81],\n",
              "       [ 34,  17],\n",
              "       [ 34,  73],\n",
              "       [ 37,  26],\n",
              "       [ 37,  75],\n",
              "       [ 38,  35],\n",
              "       [ 38,  92],\n",
              "       [ 39,  36],\n",
              "       [ 39,  61],\n",
              "       [ 39,  28],\n",
              "       [ 39,  65],\n",
              "       [ 40,  55],\n",
              "       [ 40,  47],\n",
              "       [ 40,  42],\n",
              "       [ 40,  42],\n",
              "       [ 42,  52],\n",
              "       [ 42,  60],\n",
              "       [ 43,  54],\n",
              "       [ 43,  60],\n",
              "       [ 43,  45],\n",
              "       [ 43,  41],\n",
              "       [ 44,  50],\n",
              "       [ 44,  46],\n",
              "       [ 46,  51],\n",
              "       [ 46,  46],\n",
              "       [ 46,  56],\n",
              "       [ 46,  55],\n",
              "       [ 47,  52],\n",
              "       [ 47,  59],\n",
              "       [ 48,  51],\n",
              "       [ 48,  59],\n",
              "       [ 48,  50],\n",
              "       [ 48,  48],\n",
              "       [ 48,  59],\n",
              "       [ 48,  47],\n",
              "       [ 49,  55],\n",
              "       [ 49,  42],\n",
              "       [ 50,  49],\n",
              "       [ 50,  56],\n",
              "       [ 54,  47],\n",
              "       [ 54,  54],\n",
              "       [ 54,  53],\n",
              "       [ 54,  48],\n",
              "       [ 54,  52],\n",
              "       [ 54,  42],\n",
              "       [ 54,  51],\n",
              "       [ 54,  55],\n",
              "       [ 54,  41],\n",
              "       [ 54,  44],\n",
              "       [ 54,  57],\n",
              "       [ 54,  46],\n",
              "       [ 57,  58],\n",
              "       [ 57,  55],\n",
              "       [ 58,  60],\n",
              "       [ 58,  46],\n",
              "       [ 59,  55],\n",
              "       [ 59,  41],\n",
              "       [ 60,  49],\n",
              "       [ 60,  40],\n",
              "       [ 60,  42],\n",
              "       [ 60,  52],\n",
              "       [ 60,  47],\n",
              "       [ 60,  50],\n",
              "       [ 61,  42],\n",
              "       [ 61,  49],\n",
              "       [ 62,  41],\n",
              "       [ 62,  48],\n",
              "       [ 62,  59],\n",
              "       [ 62,  55],\n",
              "       [ 62,  56],\n",
              "       [ 62,  42],\n",
              "       [ 63,  50],\n",
              "       [ 63,  46],\n",
              "       [ 63,  43],\n",
              "       [ 63,  48],\n",
              "       [ 63,  52],\n",
              "       [ 63,  54],\n",
              "       [ 64,  42],\n",
              "       [ 64,  46],\n",
              "       [ 65,  48],\n",
              "       [ 65,  50],\n",
              "       [ 65,  43],\n",
              "       [ 65,  59],\n",
              "       [ 67,  43],\n",
              "       [ 67,  57],\n",
              "       [ 67,  56],\n",
              "       [ 67,  40],\n",
              "       [ 69,  58],\n",
              "       [ 69,  91],\n",
              "       [ 70,  29],\n",
              "       [ 70,  77],\n",
              "       [ 71,  35],\n",
              "       [ 71,  95],\n",
              "       [ 71,  11],\n",
              "       [ 71,  75],\n",
              "       [ 71,   9],\n",
              "       [ 71,  75],\n",
              "       [ 72,  34],\n",
              "       [ 72,  71],\n",
              "       [ 73,   5],\n",
              "       [ 73,  88],\n",
              "       [ 73,   7],\n",
              "       [ 73,  73],\n",
              "       [ 74,  10],\n",
              "       [ 74,  72],\n",
              "       [ 75,   5],\n",
              "       [ 75,  93],\n",
              "       [ 76,  40],\n",
              "       [ 76,  87],\n",
              "       [ 77,  12],\n",
              "       [ 77,  97],\n",
              "       [ 77,  36],\n",
              "       [ 77,  74],\n",
              "       [ 78,  22],\n",
              "       [ 78,  90],\n",
              "       [ 78,  17],\n",
              "       [ 78,  88],\n",
              "       [ 78,  20],\n",
              "       [ 78,  76],\n",
              "       [ 78,  16],\n",
              "       [ 78,  89],\n",
              "       [ 78,   1],\n",
              "       [ 78,  78],\n",
              "       [ 78,   1],\n",
              "       [ 78,  73],\n",
              "       [ 79,  35],\n",
              "       [ 79,  83],\n",
              "       [ 81,   5],\n",
              "       [ 81,  93],\n",
              "       [ 85,  26],\n",
              "       [ 85,  75],\n",
              "       [ 86,  20],\n",
              "       [ 86,  95],\n",
              "       [ 87,  27],\n",
              "       [ 87,  63],\n",
              "       [ 87,  13],\n",
              "       [ 87,  75],\n",
              "       [ 87,  10],\n",
              "       [ 87,  92],\n",
              "       [ 88,  13],\n",
              "       [ 88,  86],\n",
              "       [ 88,  15],\n",
              "       [ 88,  69],\n",
              "       [ 93,  14],\n",
              "       [ 93,  90],\n",
              "       [ 97,  32],\n",
              "       [ 97,  86],\n",
              "       [ 98,  15],\n",
              "       [ 98,  88],\n",
              "       [ 99,  39],\n",
              "       [ 99,  97],\n",
              "       [101,  24],\n",
              "       [101,  68],\n",
              "       [103,  17],\n",
              "       [103,  85],\n",
              "       [103,  23],\n",
              "       [103,  69],\n",
              "       [113,   8],\n",
              "       [113,  91],\n",
              "       [120,  16],\n",
              "       [120,  79],\n",
              "       [126,  28],\n",
              "       [126,  74],\n",
              "       [137,  18],\n",
              "       [137,  83]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X[:,0], X[:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "eeRT5M_RzGQH",
        "outputId": "50f591f5-d252-411d-9951-057e09544414"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X1 = df.iloc[ : , [3,4] ]\n",
        "X1 # X1 is a dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "S7d4ABKuzLC8",
        "outputId": "c4790a0e-2854-460f-b75c-c6c0ca105199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Annual Income (k$)  Spending Score (1-100)\n",
              "0                    15                      39\n",
              "1                    15                      81\n",
              "2                    16                       6\n",
              "3                    16                      77\n",
              "4                    17                      40\n",
              "..                  ...                     ...\n",
              "195                 120                      79\n",
              "196                 126                      28\n",
              "197                 126                      74\n",
              "198                 137                      18\n",
              "199                 137                      83\n",
              "\n",
              "[200 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1302c51c-7e5a-4815-a429-933b3ea37c3c\" 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>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1302c51c-7e5a-4815-a429-933b3ea37c3c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1302c51c-7e5a-4815-a429-933b3ea37c3c 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-1302c51c-7e5a-4815-a429-933b3ea37c3c');\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",
              "\n",
              "\n",
              "<div id=\"df-ce9e9af6-890d-4488-a9e8-1b2213cc6726\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ce9e9af6-890d-4488-a9e8-1b2213cc6726')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ce9e9af6-890d-4488-a9e8-1b2213cc6726 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hopkins Statistics:**\n",
        "\n",
        "The Hopkins statistic, is a statistic which gives a value which indicates the cluster tendency, in other words: how well the data can be clustered.\n",
        "\n",
        "If the value is between {0.01, ...,0.3}, the data is regularly spaced.\n",
        "\n",
        "If the value is around 0.5, it is random.\n",
        "\n",
        "If the value is between {0.7, ..., 0.99}, it has a high tendency to cluster."
      ],
      "metadata": {
        "id": "YXw8_d2ezTB-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import NearestNeighbors\n",
        "from random import sample\n",
        "from numpy.random import uniform\n",
        "import numpy as np\n",
        "from math import isnan\n",
        "\n",
        "def hopkins(X):\n",
        "    d = X.shape[1]\n",
        "    #d = len(vars) # columns\n",
        "    n = len(X) # rows\n",
        "    m = int(0.1 * n)\n",
        "    nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
        "\n",
        "    rand_X = sample(range(0, n, 1), m)\n",
        "\n",
        "    ujd = []\n",
        "    wjd = []\n",
        "    for j in range(0, m):\n",
        "        u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
        "        ujd.append(u_dist[0][1])\n",
        "        w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
        "        wjd.append(w_dist[0][1])\n",
        "\n",
        "    H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
        "    if isnan(H):\n",
        "        print(ujd, wjd)\n",
        "        H = 0\n",
        "\n",
        "    return H"
      ],
      "metadata": {
        "id": "5CY4hsERzO41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hopkins(X1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LC4Q2KRzb7F",
        "outputId": "088202a4-795b-4eda-afef-8575bdd6b0b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7442377143536254"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hopkins(X1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLTH6zczzhGH",
        "outputId": "306b69ed-d1d9-4b50-8ffd-d65b4c4115ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8012320313279011"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l = []\n",
        "for i in range(1000):\n",
        "    l.append(hopkins(X1))\n",
        "\n",
        "sum(l)/len(l)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s35Yt_ogzjsQ",
        "outputId": "2f7e1dbd-35cd-4af9-fd92-26756e80612c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.760707783920338"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "pHrrJXYrzmar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Clusters. Assume with no. of clusters = 2\n",
        "kmeans = KMeans(n_clusters=2)\n",
        "kmeans.fit(X)\n",
        "\n",
        "# see what centroid values the algorithm generated for the final clusters\n",
        "print(kmeans.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pAHpXrwKzz29",
        "outputId": "47eeaca6-bd79-46f0-f942-46d74c7fecc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[61.19101124 73.47191011]\n",
            " [60.05405405 31.54054054]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CB2Pz8Fz2gW",
        "outputId": "ae0a7276-8c3b-4266-8234-7700158fda4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1\n",
            " 0 1 0 1 0 1 0 1 0 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 0\n",
            " 1 0 0 1 1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1\n",
            " 0 1 1 1 1 1 0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1\n",
            " 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot the data points again on the graph and\n",
        "# visualize how the data has been clustered.\n",
        "\n",
        "plt.scatter(X[:,0],X[:,1], c=kmeans.labels_, cmap='rainbow')\n",
        "plt.scatter(kmeans.cluster_centers_[:,0] ,kmeans.cluster_centers_[:,1], color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "v7lPWsnU0fQp",
        "outputId": "2ccff6d6-fd31-4203-f149-d363dd7301c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Clusters. Assume with no. of clusters = 2\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(X)\n",
        "\n",
        "# see what centroid values the algorithm generated for the final clusters\n",
        "print(kmeans.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNY9RJUY0nNv",
        "outputId": "d403e221-3af4-4f46-fcb4-514c3ae9a449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[86.53846154 82.12820513]\n",
            " [87.         18.63157895]\n",
            " [44.15447154 49.82926829]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vn8VK-GL0xty",
        "outputId": "e3037a98-5ebd-4640-9924-bcedccbded05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 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 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 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 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1\n",
            " 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot the data points again on the graph and\n",
        "# visualize how the data has been clustered.\n",
        "\n",
        "plt.scatter(X[:,0],X[:,1], c=kmeans.labels_, cmap='rainbow')\n",
        "plt.scatter(kmeans.cluster_centers_[:,0] ,kmeans.cluster_centers_[:,1], color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "3flVvl6i01f9",
        "outputId": "f6d92f1e-ea2e-498a-e791-a264fd8a0aed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Clusters. Assume with no. of clusters = 2\n",
        "kmeans = KMeans(n_clusters=4)\n",
        "kmeans.fit(X)\n",
        "\n",
        "# see what centroid values the algorithm generated for the final clusters\n",
        "print(kmeans.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqeKsRgA06qO",
        "outputId": "4c8a7a4d-1ad3-4d42-bba6-a8b98093e6e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[48.26       56.48      ]\n",
            " [86.53846154 82.12820513]\n",
            " [87.         18.63157895]\n",
            " [26.30434783 20.91304348]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyJX4lAO0_lA",
        "outputId": "a1e167a4-a565-40b3-b3b8-39e9bbda07bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3\n",
            " 0 3 0 3 0 3 0 3 0 0 0 0 0 0 0 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 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 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1\n",
            " 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
            " 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot the data points again on the graph and\n",
        "# visualize how the data has been clustered.\n",
        "\n",
        "plt.scatter(X[:,0],X[:,1], c=kmeans.labels_, cmap='rainbow')\n",
        "plt.scatter(kmeans.cluster_centers_[:,0] ,kmeans.cluster_centers_[:,1], color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "zx5yXQgK1DRh",
        "outputId": "2fc3840f-bae4-411c-cc30-42225d2aa7c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Clusters. Assume with no. of clusters = 2\n",
        "kmeans = KMeans(n_clusters=5)\n",
        "kmeans.fit(X)\n",
        "\n",
        "# see what centroid values the algorithm generated for the final clusters\n",
        "print(kmeans.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNt19Yq41G4q",
        "outputId": "d40dadb6-043d-4ee9-a9ee-ee14b5992eed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[88.2        17.11428571]\n",
            " [55.2962963  49.51851852]\n",
            " [86.53846154 82.12820513]\n",
            " [26.30434783 20.91304348]\n",
            " [25.72727273 79.36363636]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uoPK-301MtJ",
        "outputId": "87413d79-083e-458c-d9d9-0b1f559a4fc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3\n",
            " 4 3 4 3 4 3 1 3 4 1 1 1 1 1 1 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 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 2 0 2 1 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 1 2 0 2 0 2\n",
            " 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0\n",
            " 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot the data points again on the graph and\n",
        "# visualize how the data has been clustered.\n",
        "\n",
        "plt.scatter(X[:,0],X[:,1], c=kmeans.labels_, cmap='rainbow')\n",
        "plt.scatter(kmeans.cluster_centers_[:,0] ,kmeans.cluster_centers_[:,1], color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "oHe6hSij1je0",
        "outputId": "b7631c79-d8e2-45ac-b1b7-3a76406595dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Clusters. Assume with no. of clusters = 2\n",
        "kmeans = KMeans(n_clusters=6)\n",
        "kmeans.fit(X)\n",
        "\n",
        "# see what centroid values the algorithm generated for the final clusters\n",
        "print(kmeans.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QG4wiZv61m_U",
        "outputId": "5f58f975-37d8-47bc-8277-fc78ef8a6317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 55.2962963   49.51851852]\n",
            " [ 25.72727273  79.36363636]\n",
            " [ 78.55172414  82.17241379]\n",
            " [109.7         82.        ]\n",
            " [ 26.30434783  20.91304348]\n",
            " [ 88.2         17.11428571]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHzYe9xf1rfQ",
        "outputId": "b274dc2c-6f61-4fac-fc58-be445c5a15e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4\n",
            " 1 4 1 4 1 4 0 4 1 0 0 0 0 0 0 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 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 2 5 2 0 2 5 2 5 2 0 2 5 2 5 2 5 2 5 2 0 2 5 2 5 2\n",
            " 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 3 5 3 5\n",
            " 3 5 3 5 3 5 3 5 3 5 3 5 3 5 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot the data points again on the graph and\n",
        "# visualize how the data has been clustered.\n",
        "\n",
        "plt.scatter(X[:,0],X[:,1], c=kmeans.labels_, cmap='rainbow')\n",
        "plt.scatter(kmeans.cluster_centers_[:,0] ,kmeans.cluster_centers_[:,1], color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "3uxTnUzG10nP",
        "outputId": "082c8579-a18e-4fdb-88ef-1fb6a0ffcb84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Elbow Curve**"
      ],
      "metadata": {
        "id": "YU31_gBo2CZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = [] # within cluster sum of squares\n",
        "for i in range(1,11):\n",
        "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, random_state=0)\n",
        "    kmeans.fit(X)\n",
        "    wcss.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "bxHDO0ga15zP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,11), wcss)\n",
        "plt.title(\"Elbow Curve\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "sJjSKMyX2H_k",
        "outputId": "0502a2b3-8cbb-44e4-d44e-409ffc03406f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, we understand that the right number of clusters is 5."
      ],
      "metadata": {
        "id": "j9N00rqn2Qq5"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "91QP2XMQ2L2f"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}