240920_과제_캐글(비지도학습).ipynb

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      "authorship_tag": "ABX9TyNAMACsVWD2RsxRYm+1XbRm",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ggoddll99/24-2ESAA/blob/main/240920_%EA%B3%BC%EC%A0%9C_%EC%BA%90%EA%B8%80(%EB%B9%84%EC%A7%80%EB%8F%84%ED%95%99%EC%8A%B5).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Customers clustering : DBSCAN and AP**"
      ],
      "metadata": {
        "id": "gRw0FQfq6uJU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Introduction**\n",
        "The main task of clustering is to discover „natural“ groups within an unlabelled dataset - this means that's an unsuprvised ML task. And it's an important one, as as it is used in many scientific, engineering and business domains. One of the most known application of clustering are:\n",
        "* customers segmentation (for efficient marketing)\n",
        "* image segmentation\n",
        "* documents clusterisation."
      ],
      "metadata": {
        "id": "ObC-asLx60Si"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Reading data**\n",
        "In this section, raw data will be read, overviewed and checked if any cleaning is required."
      ],
      "metadata": {
        "id": "yia89v8U69ox"
      }
    },
    {
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        },
        "id": "dbC8Y0t36ZDB",
        "outputId": "a3315331-920c-44b7-ff36-616095613fbf"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pandas version: 2.1.4\n",
            "numpy version: 1.26.4\n",
            "seaborn version: 0.13.1\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "\n",
        "print(\"pandas version: {}\".format(pd.__version__))\n",
        "print(\"numpy version: {}\".format(np.__version__))\n",
        "print(\"seaborn version: {}\".format(sns.__version__))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYuQQ4ch7IDz",
        "outputId": "874f7a69-b900-4054-dc10-c4f36b79ecd0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mall_data = pd.read_csv('/content/drive/MyDrive/24-1/ESAA/과제/Mall_Customers.csv')\n",
        "\n",
        "print('There are {} rows and {} columns in our dataset.'.format(mall_data.shape[0],mall_data.shape[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "N7wwC3dD7EmY",
        "outputId": "507a415e-9598-41a0-aa04-4f73d9105091"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 200 rows and 5 columns in our dataset.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mall_data.sample(10)"
      ],
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          "height": 363
        },
        "id": "fSVSG2TJ7GQS",
        "outputId": "ec8a0c08-0975-4349-9144-2aee3c0c3644"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "98           99    Male   48                  61                      42\n",
              "106         107  Female   66                  63                      50\n",
              "24           25  Female   54                  28                      14\n",
              "54           55  Female   50                  43                      45\n",
              "72           73  Female   60                  50                      49\n",
              "68           69    Male   19                  48                      59\n",
              "135         136  Female   29                  73                      88\n",
              "65           66    Male   18                  48                      59\n",
              "181         182  Female   32                  97                      86\n",
              "143         144  Female   32                  76                      87"
            ],
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              "summary": "{\n  \"name\": \"mall_data\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47,\n        \"min\": 25,\n        \"max\": 182,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          182,\n          107,\n          69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 66,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18,\n          66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 28,\n        \"max\": 97,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          97,\n          63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 14,\n        \"max\": 88,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          86,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
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              "      async function convertToInteractive(key) {\n",
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              "          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",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mall_data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71.77644232399086,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          200.0,\n          100.5,\n          150.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.50135224289181,\n        \"min\": 13.96900733155888,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.85,\n          36.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.01038341270951,\n        \"min\": 15.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          60.56,\n          61.5,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.42496609345541,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50.2,\n          50.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 5 columns:\n",
        "* Customer ID - numerical - unique customer * number - integer\n",
        "* Gender - categorical - binary (Male/Female)\n",
        "* Age - numerical - integer\n",
        "* Annual Income (k$) - numerical - integer\n",
        "* Spending Score (1-100) - numerical - integer\n",
        "There is one binary, categorical column: **gender**. You may be tempted to one-hot encode it for the clustering. It is:\n",
        "* technically possible\n",
        "* theoretically not forbidden\n",
        "* practically not recommended"
      ],
      "metadata": {
        "id": "8d27UJ8r7SI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mall_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "RBLV3Tss7gXg",
        "outputId": "649bdbc2-b26b-45ba-beb4-456e120f4e76"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bb8ec822-3ca6-47db-9c6e-a7faa55dfe0b');\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-cb7bb420-db06-4da0-9f5f-d4e22d068458\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb7bb420-db06-4da0-9f5f-d4e22d068458')\"\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-cb7bb420-db06-4da0-9f5f-d4e22d068458 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mall_data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71.77644232399086,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          200.0,\n          100.5,\n          150.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.50135224289181,\n        \"min\": 13.96900733155888,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.85,\n          36.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.01038341270951,\n        \"min\": 15.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          60.56,\n          61.5,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.42496609345541,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50.2,\n          50.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mall_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "ww6sTuFj7iDi",
        "outputId": "f3fb473d-e5fb-4a99-b758-2560840d5621"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID                0\n",
              "Gender                    0\n",
              "Age                       0\n",
              "Annual Income (k$)        0\n",
              "Spending Score (1-100)    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no missing data. This simplifies the analysis but it is a very unlikely scenario in a real-life where analysts spend a significant amount of time cleaning their data before the core analysis is performed."
      ],
      "metadata": {
        "id": "Jderx-tK7je4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "K6X2LxB37kZO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3.1 Distributions**\n",
        "In this chapter distributions of numerical variables will be investigated in detail. Data will be stratified by gender - the only categorical variable."
      ],
      "metadata": {
        "id": "ZLl-MfY37pXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "males_age = mall_data[mall_data['Gender']=='Male']['Age'] # subset with males age\n",
        "females_age = mall_data[mall_data['Gender']=='Female']['Age'] # subset with females age\n",
        "\n",
        "age_bins = range(15,75,5)\n",
        "\n",
        "# males histogram\n",
        "fig2, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5), sharey=True)\n",
        "sns.distplot(males_age, bins=age_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
        "ax1.set_xticks(age_bins)\n",
        "ax1.set_ylim(top=25)\n",
        "ax1.set_title('Males')\n",
        "ax1.set_ylabel('Count')\n",
        "ax1.text(45,23, \"TOTAL count: {}\".format(males_age.count()))\n",
        "ax1.text(45,22, \"Mean age: {:.1f}\".format(males_age.mean()))\n",
        "\n",
        "# females histogram\n",
        "sns.distplot(females_age, bins=age_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
        "ax2.set_xticks(age_bins)\n",
        "ax2.set_title('Females')\n",
        "ax2.set_ylabel('Count')\n",
        "ax2.text(45,23, \"TOTAL count: {}\".format(females_age.count()))\n",
        "ax2.text(45,22, \"Mean age: {:.1f}\".format(females_age.mean()))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "id": "Z4cTsYET7tNw",
        "outputId": "befce4ae-23f4-4970-fdbb-f47f10023528"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-90e711b08c1a>:8: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(males_age, bins=age_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
            "<ipython-input-9-90e711b08c1a>:17: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(females_age, bins=age_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Kolgomorov-Smirnov test p-value: {:.2f}'.format(stats.ks_2samp(males_age, females_age)[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xvUoU267xl3",
        "outputId": "3704d29f-accb-4873-c9c1-972caf815bd2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kolgomorov-Smirnov test p-value: 0.49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The average age of male customers is lightly higher than female ones (39.8 versus 38.1). Distribution of male age is more uniform than females, where we can observe that the biggest age group is 30-35 years old. Kolgomorov-Smirnov test shows that the differences between these two groups are statistically insignificant."
      ],
      "metadata": {
        "id": "gPFAlHsi7yiO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def labeler(pct, allvals):\n",
        "    absolute = int(pct/100.*np.sum(allvals))\n",
        "    return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n",
        "\n",
        "sizes = [males_age.count(),females_age.count()] # wedge sizes\n",
        "\n",
        "fig0, ax1 = plt.subplots(figsize=(6,6))\n",
        "wedges, texts, autotexts = ax1.pie(sizes,\n",
        "                                   autopct=lambda pct: labeler(pct, sizes),\n",
        "                                   radius=1,\n",
        "                                   colors=['#0066ff','#cc66ff'],\n",
        "                                   startangle=90,\n",
        "                                   textprops=dict(color=\"w\"),\n",
        "                                   wedgeprops=dict(width=0.7, edgecolor='w'))\n",
        "\n",
        "ax1.legend(wedges, ['male','female'],\n",
        "           loc='center right',\n",
        "           bbox_to_anchor=(0.7, 0, 0.5, 1))\n",
        "\n",
        "plt.text(0,0, 'TOTAL\\n{}'.format(mall_data['Age'].count()),\n",
        "         weight='bold', size=12, color='#52527a',\n",
        "         ha='center', va='center')\n",
        "\n",
        "plt.setp(autotexts, size=12, weight='bold')\n",
        "ax1.axis('equal')  # Equal aspect ratio\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "9KxcF8hl71gv",
        "outputId": "2602a98d-a7ca-4880-e091-7950e20890e6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are slightly more female customers than male ones (112 vs. 87). Females are 56% of total customers."
      ],
      "metadata": {
        "id": "cWMvMrDX72xb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "males_income = mall_data[mall_data['Gender']=='Male']['Annual Income (k$)'] # subset with males income\n",
        "females_income = mall_data[mall_data['Gender']=='Female']['Annual Income (k$)'] # subset with females income\n",
        "\n",
        "my_bins = range(10,150,10)\n",
        "\n",
        "# males histogram\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18,5))\n",
        "sns.distplot(males_income, bins=my_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
        "ax1.set_xticks(my_bins)\n",
        "ax1.set_yticks(range(0,24,2))\n",
        "ax1.set_ylim(0,22)\n",
        "ax1.set_title('Males')\n",
        "ax1.set_ylabel('Count')\n",
        "ax1.text(85,19, \"Mean income: {:.1f}k$\".format(males_income.mean()))\n",
        "ax1.text(85,18, \"Median income: {:.1f}k$\".format(males_income.median()))\n",
        "ax1.text(85,17, \"Std. deviation: {:.1f}k$\".format(males_income.std()))\n",
        "\n",
        "# females histogram\n",
        "sns.distplot(females_income, bins=my_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
        "ax2.set_xticks(my_bins)\n",
        "ax2.set_yticks(range(0,24,2))\n",
        "ax2.set_ylim(0,22)\n",
        "ax2.set_title('Females')\n",
        "ax2.set_ylabel('Count')\n",
        "ax2.text(85,19, \"Mean income: {:.1f}k$\".format(females_income.mean()))\n",
        "ax2.text(85,18, \"Median income: {:.1f}k$\".format(females_income.median()))\n",
        "ax2.text(85,17, \"Std. deviation: {:.1f}k$\".format(females_income.std()))\n",
        "\n",
        "# boxplot\n",
        "sns.boxplot(x='Gender', y='Annual Income (k$)', data=mall_data, ax=ax3)\n",
        "ax3.set_title('Boxplot of annual income')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "_Nlgi1iE72ib",
        "outputId": "f2dd737b-4465-4616-dd9f-eb3dcd7f6ae0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-0d86fc4a2b23>:8: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(males_income, bins=my_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
            "<ipython-input-12-0d86fc4a2b23>:19: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(females_income, bins=my_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Kolgomorov-Smirnov test p-value: {:.2f}'.format(stats.ks_2samp(males_income, females_income)[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwYL0fyQ8QuG",
        "outputId": "80936ee1-72c5-4787-c3db-9dadcad57086"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kolgomorov-Smirnov test p-value: 0.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mean income of males is higher than females (62.2 k$ vs. 59.2k\\$). Also median income of male customers (62.5k$) is higher thn female ones (60k\\$). Standard deviation is similar for both groups. There is one outlier in male group with an annual income of about 140k$. K-S test shows that these two groups are not statistically different."
      ],
      "metadata": {
        "id": "0925i_An8SmP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "males_spending = mall_data[mall_data['Gender']=='Male']['Spending Score (1-100)'] # subset with males age\n",
        "females_spending = mall_data[mall_data['Gender']=='Female']['Spending Score (1-100)'] # subset with females age\n",
        "\n",
        "spending_bins = range(0,105,5)\n",
        "\n",
        "# males histogram\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18,5))\n",
        "sns.distplot(males_spending, bins=spending_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
        "ax1.set_xticks(spending_bins)\n",
        "ax1.set_xlim(0,100)\n",
        "ax1.set_yticks(range(0,17,1))\n",
        "ax1.set_ylim(0,16)\n",
        "ax1.set_title('Males')\n",
        "ax1.set_ylabel('Count')\n",
        "ax1.text(50,15, \"Mean spending score: {:.1f}\".format(males_spending.mean()))\n",
        "ax1.text(50,14, \"Median spending score: {:.1f}\".format(males_spending.median()))\n",
        "ax1.text(50,13, \"Std. deviation score: {:.1f}\".format(males_spending.std()))\n",
        "\n",
        "# females histogram\n",
        "sns.distplot(females_spending, bins=spending_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
        "ax2.set_xticks(spending_bins)\n",
        "ax2.set_xlim(0,100)\n",
        "ax2.set_yticks(range(0,17,1))\n",
        "ax2.set_ylim(0,16)\n",
        "ax2.set_title('Females')\n",
        "ax2.set_ylabel('Count')\n",
        "ax2.text(50,15, \"Mean spending score: {:.1f}\".format(females_spending.mean()))\n",
        "ax2.text(50,14, \"Median spending score: {:.1f}\".format(females_spending.median()))\n",
        "ax2.text(50,13, \"Std. deviation score: {:.1f}\".format(females_spending.std()))\n",
        "\n",
        "# boxplot\n",
        "sns.boxplot(x='Gender', y='Spending Score (1-100)', data=mall_data, ax=ax3)\n",
        "ax3.set_title('Boxplot of spending score')\n",
        "plt.show()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "lfoHeNc98TTU",
        "outputId": "87c41a08-abaf-478c-a208-931a64b64cf7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-95f80557e0ae>:8: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(males_spending, bins=spending_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n",
            "<ipython-input-14-95f80557e0ae>:20: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(females_spending, bins=spending_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor=\"k\", linewidth=2))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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MAADcR74GACDtSpcunea+zs7OKlq0aAZGAwBA9mXTpUiCg4MVHByc4vFXX31VknT+/PlMiggAADyMfA0AwOPZu3evdu/eratXr0qSfHx8VLNmTVWvXt3GkQEAYP+y3RrbcXFxiouLM+9HRkbaMBoAAJAc8jUAIDu7du2a2rVrp127dikgIEDe3t6SpLCwMA0ZMkS1a9fWqlWrVLBgQRtHCgCA/cp2he1JkyZp3Lhxtg4DAACkgnwNAMjO+vfvr4SEBB07dkwlS5a0OHbixAn16NFDAwYM0IoVK2wUYdYSFhamiIgIW4dh1y5cuGDxv3h8Xl5e5n+MApC1ZbvC9ogRIzR06FDzfmRkpPz9/W0YEQAAeBj5GgCQnW3YsEHbt29PUtSWpJIlS2rmzJmqX79+5geWBYWFhemVV7vpTnzcozvjkT744ANbh2D3nF1c9fXiryhuA3Yg2xW2XV1d5erqauswAABAKsjXAIDszNXVNdVltm7dukUe/P8iIiJ0Jz5OMUXqKdHNy9bh4CnnEBshnf1ZERERFLYBO5DtCtsAAAAAANhSp06dFBISomnTpqlRo0by9PSUdO8JpS1btmjo0KHq0qWLjaPMWhLdvJTokd/WYQAA7IhNC9tRUVE6ffq0ef/cuXM6fPiw8ubNq4CAAN24cUMXL17U33//LeneWmTSvTdJ+/j42CRmAACeNuRrAADSZ+rUqUpMTFTnzp119+5dubi4SJLi4+Pl5OSknj176uOPP7ZxlAAA2DebFrb379+vBg0amPfvr7UZEhKiRYsWad26derevbv5eOfOnSVJY8aM0dixYzM1VgAAnlbkawAA0sfV1VVz587V5MmTtX//foWFhUm694++VapUMc/gBgAAj8+mhe369evLMIwUj4eGhio0NDTzAgIAAEmQrwEAeDyenp5q2LChrcMAACBbYo1tAAAAAACs7J9//tGCBQu0e/duXb16VdK9Gdu1atVSaGioChQoYOMIAQCwbw62DgAAAAAAgOxk3759KlGihGbOnCkvLy/VrVtXdevWlZeXl2bOnKlSpUpp//79tg4TAAC7xoxtAAAAAACs6PXXX1eHDh00b948mUwmi2OGYahv3756/fXXtXv3bhtFCACA/aOwDQAAAACAFR05ckSLFi1KUtSWJJPJpCFDhqhSpUo2iAwAgOyDpUgAAAAAALAiHx8f7d27N8Xje/fulbe3dyZGBABA9sOMbQAAAAAArOitt95Snz59dODAATVq1MhcxA4LC9OWLVv0+eef6+OPP7ZxlAAA2DcK2wAAAAAAWNGAAQOUP39+TZs2TZ9++qkSEhIkSY6OjqpSpYoWLVqkjh072jhKAADsG4VtAAAAAACsrFOnTurUqZPu3Lmjf/75R5KUP39+OTs72zgyAACyBwrbAAAAAABkEGdnZ/n6+to6DAAAsh1eHgkAAAAAQCY6c+aMGjZsaOswAACwaxS2AQAAAADIRFFRUfr5559tHQYAAHaNpUgAAAAAALCimTNnpnr88uXLmRQJAADZF4VtAAAAAACsaPDgwfL19ZWLi0uyx+Pj4zM5IgAAsh+bLkWyfft2tWrVSn5+fjKZTFq7dq3FccMw9N5778nX11fu7u5q3LixTp06ZZtgAQB4ipGzAQBIu8DAQE2bNk3nzp1Ldvv+++9tHSIAAHbPpoXt6OhoVahQQXPmzEn2+JQpUzRz5kzNmzdPe/bskYeHh5o2barY2NhMjhQAgKcbORsAgLSrUqWKDhw4kOJxk8kkwzAyMSIAALIfmy5FEhwcrODg4GSPGYah6dOna9SoUWrdurUk6auvvpK3t7fWrl2rzp07Z2aoAAA81cjZAACk3fjx43X79u0Uj5cpU0bnzp3LxIgAAMh+suwa2+fOndPVq1fVuHFjc5uXl5dq1Kih3bt3p/glOS4uTnFxceb9yMjIDI8VsKXVq1crJibG6uO6u7urbdu2VhnLHmIE8PgeJ2eTr4Gs59DxfYp1uKWcl1ytPjY5G0+bMmXKpHrc2dlZgYGBmRQNAADZU5YtbF+9elWS5O3tbdHu7e1tPpacSZMmady4cRkaG5CVxMTE6PL1aEVYsW7s5S4VKmC98ewhRgCP73FyNvkayHru3IlXgmEoIcJJNy5GW21cR3dJ5GxAH374ofr27avcuXPbOhQAALKFLFvYflwjRozQ0KFDzfuRkZHy9/e3YURAxouIkS7eMMnFLccTjxUfe1sBeQ0VskJcD7KHGAFkHvI1kDU5JbrIJd5DTrc9rDJeTOxtKS/rCAOSNHHiRHXs2JHCNgAAVpJlC9s+Pj6SpLCwMPn6+prbw8LCVLFixRTPc3V1laur9R+fBLI6F7ccqtGk6xOPs2fTEknWm6X1IHuIEUD6PU7OJl8DWZers5vaWyFfS9LKTUt0l5wNSBIviwQAwMocbB1ASoKCguTj46MtW7aY2yIjI7Vnzx7VrFnThpEBAIAHkbMBAAAAAJnNpjO2o6KidPr0afP+uXPndPjwYeXNm1cBAQEaPHiwJkyYoOLFiysoKEijR4+Wn5+f2rRpY7ugAQB4CpGzAQB4Mn/++af8/PxsHQYAANmGTQvb+/fvV4MGDcz799faDAkJ0aJFizR8+HBFR0erT58+Cg8PV506dfTjjz/Kzc3NViEDAPBUImcDAPBkeJcEAADWZdPCdv369VNdZ8xkMmn8+PEaP358JkYFAAAeRs4GAMB6jhw5osqVKyshIcHWoQAAYLey7BrbAAAAAABkV7xMEgCAJ2PTGdsAAAAAAGQ3bdu2TfV4RESETCZTJkUDAED2xIxtAAAAAACs6H//+59iY2Pl5eWV7JYzZ84Mue7ly5f1yiuvKF++fHJ3d1e5cuW0f/9+83HDMPTee+/J19dX7u7uaty4sU6dOpUhsQAAkNGYsQ0AAAAAgBWVLl1a7dq1U8+ePZM9fvjwYX333XdWvebNmzdVu3ZtNWjQQOvXr1eBAgV06tQp5cmTx9xnypQpmjlzpr788ksFBQVp9OjRatq0qf78809e+AwAsDsUtgEAAAAAsKIqVaro4MGDKRa2XV1dFRAQYNVrTp48Wf7+/lq4cKG5LSgoyPzfhmFo+vTpGjVqlFq3bi1J+uqrr+Tt7a21a9eqc+fOVo0HAICMxlIkAAAAAABY0bx58/TRRx+leLx06dI6d+6cVa+5bt06Va1aVR06dFDBggVVqVIlff755+bj586d09WrV9W4cWNzm5eXl2rUqKHdu3enOG5cXJwiIyMtNgAAsgIK2wAAAAAAWJGrq6ty5MiRqdc8e/as5s6dq+LFi2vDhg3q16+f3njjDX355ZeSpKtXr0qSvL29Lc7z9vY2H0vOpEmTLNYH9/f3z7gPAQBAOlDYBgAAAADASqKjozO0f0oSExNVuXJlTZw4UZUqVVKfPn3Uu3dvzZs374nGHTFihCIiIszbpUuXrBIvAABPisI2AAAAAABWUqxYMX344Ye6cuVKin0Mw9CmTZsUHBysmTNnWuW6vr6+KlOmjEVb6dKldfHiRUmSj4+PJCksLMyiT1hYmPlYclxdXeXp6WmxAQCQFfDySAAAAAAArGTbtm169913NXbsWFWoUEFVq1aVn5+f3NzcdPPmTf3555/avXu3nJycNGLECL322mtWuW7t2rV14sQJi7aTJ08qMDBQ0r0XSfr4+GjLli2qWLGiJCkyMlJ79uxRv379rBIDAACZicI2AAAAAABWUrJkSa1atUoXL17UihUrtGPHDv3yyy+KiYlR/vz5zS91DA4OlqOjo9WuO2TIENWqVUsTJ05Ux44dtXfvXs2fP1/z58+XJJlMJg0ePFgTJkxQ8eLFFRQUpNGjR8vPz09t2rSxWhwAAGQWCtsAAAAAAFhZQECA3nzzTb355puZcr1q1appzZo1GjFihMaPH6+goCBNnz5dXbt2NfcZPny4oqOj1adPH4WHh6tOnTr68ccf5ebmlikxAgBgTRS2AQAAAADIBlq2bKmWLVumeNxkMmn8+PEaP358JkYFAEDGyPIvj7x165YGDx6swMBAubu7q1atWtq3b5+twwIAAA8hZwMAAAAAMkuWL2z36tVLmzZt0uLFi/X777/rhRdeUOPGjXX58mVbhwYAAB5AzgYAAAAAZJYsXdiOiYnRqlWrNGXKFNWtW1fFihXT2LFjVaxYMc2dO9fW4QEAgP+PnA0AAAAAyExZeo3tu3fvKiEhIcmLLNzd3bVz585kz4mLi1NcXJx5PzIyMkNjBAAA6c/Z5Gs8bQ4d36dYh1vKecnVamPu2rVLCRFOyuNQ0Gpj4smtXr1aMTExGTK2u7u72rZtmyFjAwAA2JssXdjOlSuXatasqffff1+lS5eWt7e3vvnmG+3evVvFihVL9pxJkyZp3LhxmRwpAABPt/TmbPI1njZ37sQrwTCUEOGkGxejrTJm1L9xcohP1F33BKuMB+uIiYlRxPVoJVi5tu3oLqmAdcdE5tixY4c+++wznTlzRitXrlShQoW0ePFiBQUFqU6dOrYODwAAu5WllyKRpMWLF8swDBUqVEiurq6aOXOmunTpIgeH5EMfMWKEIiIizNulS5cyOWIAAJ5O6cnZ5Gs8jZwSXeQS7yGn29bZEqJMMu7a+lMhOQkx0p0bJqv9Wd+5YbJ6oRyZY9WqVWratKnc3d116NAh89NKERERmjhxoo2jAwDAvmXpGduSVLRoUf3888+Kjo5WZGSkfH191alTJxUpUiTZ/q6urnJ1td4jngAAIG3Sk7PJ13hauTq7qX2TrlYZa+/vu5SYkGiVsWB97m45rPZnvXLTEt2VdWb6I3NNmDBB8+bNU7du3bRs2TJze+3atTVhwgQbRgYAgP3L8jO27/Pw8JCvr69u3rypDRs2qHXr1rYOCQAAJIOcDQDAPSdOnFDdunWTtHt5eSk8PDzzAwIAIBvJ8jO2N2zYIMMwVLJkSZ0+fVrDhg1TqVKl1L17d1uHBgAAHkDOBgDAko+Pj06fPq3ChQtbtO/cuTPFp5ABAEDaZPkZ2xERERowYIBKlSqlbt26qU6dOtqwYYOcnZ1tHRoAAHgAORsAAEu9e/fWoEGDtGfPHplMJv39999asmSJ3nrrLfXr18/W4QEAYNcea8Z2kSJFtG/fPuXLl8+iPTw8XJUrV9bZs2etEpwkdezYUR07drTaeAAAPC0yM19L5GwAAB72zjvvKDExUY0aNdLt27dVt25dubq66q233tLrr79u6/AAALBrj1XYPn/+vBISEpK0x8XF6fLly08cFAAAeHLkawAAbCchIUG7du3SgAEDNGzYMJ0+fVpRUVEqU6aMcubMaevwAACwe+kqbK9bt8783xs2bJCXl5d5PyEhQVu2bEmydhgAAMhc5GsAAGzP0dFRL7zwgo4dO6bcuXOrTJkytg4JAIBsJV2F7TZt2kiSTCaTQkJCLI45OzurcOHC+uSTT6wWHAAASD/yNQAAWcOzzz6rs2fPKigoyNahAACQ7aSrsJ2YmChJCgoK0r59+5Q/f/4MCQoAADw+8jUAAFnDhAkT9NZbb+n9999XlSpV5OHhYXHc09PTRpEBAGD/HmuN7XPnzlk7DgAAYGXkawAAbKt58+aSpBdffFEmk8ncbhiGTCZTsu/CAAAAafNYhW1J2rJli7Zs2aJr166ZZ4bdt2DBgicODAAAPDnyNQAAtrN161ZbhwAAQLb1WIXtcePGafz48apatap8fX0t/uUZAABkDeRrAABsq169erYOAQCAbOuxCtvz5s3TokWL9Oqrr1o7HgAAYCXkawAAbC88PFxffPGFjh07JkkqW7asevToIS8vLxtHBgCAfXN4nJPi4+NVq1Yta8cCAACsiHwNAIBt7d+/X0WLFtW0adN048YN3bhxQ1OnTlXRokV18OBBW4cHAIBde6zCdq9evbR06VJrxwIAAKyIfA0AgG0NGTJEL774os6fP6/Vq1dr9erVOnfunFq2bKnBgwfbOjwAAOzaYy1FEhsbq/nz52vz5s0qX768nJ2dLY5PnTrVKsEBAIDHR74GAMC29u/fr88//1xOTv/31dvJyUnDhw9X1apVbRgZAAD277EK27/99psqVqwoSfrjjz8sjvFiKgAAsgbyNQAAtuXp6amLFy+qVKlSFu2XLl1Srly5bBQVAADZw2MVtrdu3WrtOAAAgJWRrwEAsK1OnTqpZ8+e+vjjj83vvdi1a5eGDRumLl262Dg6AADs22OtsZ1ZEhISNHr0aAUFBcnd3V1FixbV+++/L8MwbB0aAAB4ADkbAICkPv74Y7Vt21bdunVT4cKFVbhwYYWGhqp9+/aaPHmyrcMDAMCuPdaM7QYNGqT6CPNPP/302AE9aPLkyZo7d66+/PJLlS1bVvv371f37t3l5eWlN954wyrXAAAgu8qsfC2RswEASI6Li4tmzJihSZMm6cyZM5KkokWLKkeOHDaODAAA+/dYhe3763Xed+fOHR0+fFh//PGHQkJCrBGXJOmXX35R69at1aJFC0lS4cKF9c0332jv3r1WuwYAANlVZuVriZwNAEByIiIilJCQoLx586pcuXLm9hs3bsjJyUmenp42jA4AAPv2WIXtadOmJds+duxYRUVFPVFAD6pVq5bmz5+vkydPqkSJEjpy5Ih27typqVOnpnhOXFyc4uLizPuRkZFWiyejrF69WjExMRkytru7u9q2bZshYwMAsrbMytdS+nO2PeZrAMgu+P6ReTp37qxWrVqpf//+Fu3Lly/XunXr9MMPP9gosqzHISbc1iEAdvVzaE+xInuz5c/iYxW2U/LKK6+oevXq+vjjj60y3jvvvKPIyEiVKlVKjo6OSkhI0AcffKCuXbumeM6kSZM0btw4q1w/s8TExOjy9WhFWPl3Sy93qVAB644JALB/1s7XUvpztj3mawDILmJiYhRxPVoJVv7+4eguie8fFvbs2ZPsP/LWr19fI0eOtEFEWZf7ue22DgGwK/ydAaxc2N69e7fc3NysNt7y5cu1ZMkSLV26VGXLltXhw4c1ePBg+fn5pfgI9YgRIzR06FDzfmRkpPz9/a0WU0aJiJEu3jDJxc06a63Fx95WQF5DhawyGgAgO7F2vpbSn7PtNV8DQHaRECPduWGSu5W+f8TE3pby8sLgh8XFxenu3btJ2u/cuZNhs+btVUxQXSW657Z1GHjKOcSE203BmL8zyCps+ffmsQrbDz9aZhiGrly5ov3792v06NFWCUyShg0bpnfeeUedO3eWJJUrV04XLlzQpEmTUixsu7q6ytXV1WoxZCYXtxyq0STl2ejpsWfTEknRVhkLAGCfMitfS+nP2facrwEgu3B3y6H2Vvr+sXLTEt3l+0cS1atX1/z58zVr1iyL9nnz5qlKlSo2iiprSnTPrUSP/LYOA7Ab/J0BHrOw7eXlZbHv4OCgkiVLavz48XrhhResEpgk3b59Ww4ODhZtjo6OSkxMtNo1AADIrjIrX0vkbAAAkjNhwgQ1btxYR44cUaNGjSRJW7Zs0b59+7Rx40YbRwcAgH17rML2woULrR1Hslq1aqUPPvhAAQEBKlu2rA4dOqSpU6eqR48emXJ9AADsWWbla4mcDQBAcmrXrq3du3fro48+0vLly+Xu7q7y5cvriy++UPHixW0dHgAAdu2J1tg+cOCAjh07JkkqW7asKlWqZJWg7ps1a5ZGjx6t/v3769q1a/Lz89Nrr72m9957z6rXAQAgO8vofC2RswEASEnFihW1ZMkSW4cBAEC281iF7WvXrqlz587atm2bcufOLUkKDw9XgwYNtGzZMhUoYJ1XYefKlUvTp0/X9OnTrTIeAABPk8zK1xI5GwCAB929e1cJCQkW75MICwvTvHnzFB0drRdffFF16tSxYYQAANg/h0d3Ser111/XrVu3dPToUd24cUM3btzQH3/8ocjISL3xxhvWjhEAADwG8jUAALbRu3dvi1x769YtVatWTXPmzNGGDRvUoEED/fDDDzaMEAAA+/dYM7Z//PFHbd68WaVLlza3lSlTRnPmzLH6y6gAAMDjIV8DAGAbu3bt0uzZs837X331lRISEnTq1Cl5eXnp7bff1kcffaTmzZvbMEoAAOzbY83YTkxMlLOzc5J2Z2dnJSYmPnFQAADgyZGvAQCwjcuXL1u8HHLLli1q166dvLy8JEkhISE6evSorcIDACBbeKzCdsOGDTVo0CD9/fff5rbLly9ryJAhatSokdWCAwAAj498DQCAbbi5uSkmJsa8/+uvv6pGjRoWx6OiomwRGgAA2cZjFbZnz56tyMhIFS5cWEWLFlXRokUVFBSkyMhIzZo1y9oxAgCAx0C+BgDANipWrKjFixdLknbs2KGwsDA1bNjQfPzMmTPy8/OzVXgAAGQLj7XGtr+/vw4ePKjNmzfr+PHjkqTSpUurcePGVg0OAAA8PvI1AAC28d577yk4OFjLly/XlStXFBoaKl9fX/PxNWvWqHbt2jaMEAAA+5euwvZPP/2kgQMH6tdff5Wnp6eaNGmiJk2aSJIiIiJUtmxZzZs3T88//3yGBAsAAB6NfA0AgG3Vq1dPBw4c0MaNG+Xj46MOHTpYHK9YsaKqV69uo+gAAMge0lXYnj59unr37i1PT88kx7y8vPTaa69p6tSpfFEGAMCGyNcAANhe6dKlVbp06WSP9enTJ5OjAQAg+0nXGttHjhxRs2bNUjz+wgsv6MCBA08cFAAAeHzkawAAAABAdpeuwnZYWJicnZ1TPO7k5KTr168/cVAAAODxka8BAAAAANldugrbhQoV0h9//JHi8d9++83ihRgAACDzka8BAAAAANldugrbzZs31+jRoxUbG5vkWExMjMaMGaOWLVtaLTgAAJB+5GsAAAAAQHaXrpdHjho1SqtXr1aJEiU0cOBAlSxZUpJ0/PhxzZkzRwkJCRo5cmSGBAoAANKGfA0AAAAAyO7SVdj29vbWL7/8on79+mnEiBEyDEOSZDKZ1LRpU82ZM0fe3t5WDbBw4cK6cOFCkvb+/ftrzpw5Vr0WAADZAfkaAICsIU+ePDKZTEnaTSaT3NzcVKxYMYWGhqp79+42iA4AAPuWrsK2JAUGBuqHH37QzZs3dfr0aRmGoeLFiytPnjwZEZ/27dunhIQE8/4ff/yhJk2aqEOHDhlyPQAAsgPyNQAAtvfee+/pgw8+UHBwsKpXry5J2rt3r3788UcNGDBA586dU79+/XT37l317t3bxtECAGBf0l3Yvi9PnjyqVq2aNWNJVoECBSz2P/zwQxUtWlT16tXL8GsDAGDvyNcAANjOzp07NWHCBPXt29ei/bPPPtPGjRu1atUqlS9fXjNnzqSwDQBAOj12YdsW4uPj9fXXX2vo0KHJPs4lSXFxcYqLizPvR0ZGZlZ4AAArW716tWJiYjJkbHd3d7Vt2zZDxn7aka8B2BNr55pdu3YpIcJJeRwKWm1M2K8NGzZo8uTJSdobNWqkN998U9K9lz6/8847mR0aAAB2z64K22vXrlV4eLhCQ0NT7DNp0iSNGzcu84ICAGSYmJgYXb4erQgr17a93KVCBR7dD4+HfA3AnsTExCjierQSrJRrov6Nk0N8ou66Jzy6M7K9vHnz6n//+5+GDBli0f6///1PefPmlSRFR0crV65ctggPAAC7ZleF7S+++ELBwcHy8/NLsc+IESM0dOhQ835kZKT8/f0zIzwAQAaIiJEu3jDJxS2HVcaLj72tgLyGClllNCSHfA3A3iTESHdumORuhVyTEGWSycEKQSFbGD16tPr166etW7ea19jet2+ffvjhB82bN0+StGnTJpbuAgDgMdhNYfvChQvavHmzVq9enWo/V1dXubq6ZlJUAIDM4OKWQzWadLXKWHs2LZEUbZWxkBT5GoC9cnfLofZWyDV7f9+lxIREK0SE7KB3794qU6aMZs+ebc6NJUuW1M8//6xatWpJknlJEgAAkD52U9heuHChChYsqBYtWtg6FAAAkALyNQAAlmrXrq3atWvbOgwAALIduyh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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Kolgomorov-Smirnov test p-value: {:.2f}'.format(stats.ks_2samp(males_spending, females_spending)[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNOqB4cG8WI1",
        "outputId": "d1b07577-cff6-4367-e17c-e2318886c282"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kolgomorov-Smirnov test p-value: 0.29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A mean spending score for women (51.5) is higher than men (48.5). The K-S test p-value indicates that there is no evidence to reject the null-hypothesis, however the evidence is not so strong as in previous comparisons. Next I will calculate median income for all age groups."
      ],
      "metadata": {
        "id": "ief193X38XJh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "medians_by_age_group = mall_data.groupby([\"Gender\",pd.cut(mall_data['Age'], age_bins)]).median()\n",
        "medians_by_age_group.index = medians_by_age_group.index.set_names(['Gender', 'Age_group'])\n",
        "medians_by_age_group.reset_index(inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c03iWPZW8ZTg",
        "outputId": "5dc49d0a-36eb-42a5-d46e-2784a3c876fe"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-982f56797b65>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  medians_by_age_group = mall_data.groupby([\"Gender\",pd.cut(mall_data['Age'], age_bins)]).median()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12,5))\n",
        "sns.barplot(x='Age_group', y='Annual Income (k$)', hue='Gender', data=medians_by_age_group,\n",
        "            palette=['#cc66ff','#0066ff'],\n",
        "            alpha=0.7,edgecolor='k',\n",
        "            ax=ax)\n",
        "ax.set_title('Median annual income of male and female customers')\n",
        "ax.set_xlabel('Age group')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "EuSF5FIb8awt",
        "outputId": "61a58900-e721-43fb-a599-e5271a79f5d4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A bar chart above shows that the most wealthy customers are in age of 25-45 years old. The biggest difference between women and men is visible in age groups 25-30 (male more rich) and 50-55 (female more rich)."
      ],
      "metadata": {
        "id": "mUYHGtcC8b1_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3.2 Correlations**\n",
        "This chapter investigates correlations between numerical variables."
      ],
      "metadata": {
        "id": "7eobHn1q8dNr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is a negligible correlation between age and annual income of customers for both sex groups."
      ],
      "metadata": {
        "id": "ATcjuJvo8mo5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are week negative correlations (<0.5) between age and spending score for both sex groups."
      ],
      "metadata": {
        "id": "syaVbHfD8pxA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is a negligible correlation between annual income and spending score of customers for both sex groups."
      ],
      "metadata": {
        "id": "Q_f6b-5H8tNw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Clustering**\n",
        "This section of the notebook describes and demonstrates how to use three clustering algorithms:\n",
        "* K-Means\n",
        "* Density-Based Spatial Clustering of Applications with Noise (DBSCAN)\n",
        "* Affinity Propagation."
      ],
      "metadata": {
        "id": "AhhQZ2_x8uwp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4.1 K-Means**"
      ],
      "metadata": {
        "id": "LsDYtQji82Wx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.1.1 Description**\n",
        "1. Split samples into initial groups by using seed points. The nearest samples to these seed point will create initial clusters.\n",
        "2. Calculate samples distances to groups’ central points (centroids) and assign the nearest samples to their cluster.\n",
        "3. The third step is to calculate newly created (updated) cluster centroids."
      ],
      "metadata": {
        "id": "cMb24Vxx84-e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.1.2 Use case - customers segmentation**"
      ],
      "metadata": {
        "id": "XmBP1Mpk9CZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "evGKuS198t1j"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For clustering only numeric columns are used. As mentioned at the beginning the binary variable gender will not be used here."
      ],
      "metadata": {
        "id": "EzdXLjYx9KIf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_numerics = mall_data[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']] # subset with numeric variables only"
      ],
      "metadata": {
        "id": "lnwnbsE79K57"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to find an appropriate number of clusters, the elbow method will be used. In this method for this case, the inertia for a number of clusters between 2 and 10 will be calculated. The rule is to choose the number of clusters where you see a kink or \"an elbow\" in the graph."
      ],
      "metadata": {
        "id": "G9L0o7oV9MhF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.cluster import KElbowVisualizer\n",
        "\n",
        "model = KMeans(random_state=1)\n",
        "visualizer = KElbowVisualizer(model, k=(2,10))\n",
        "\n",
        "visualizer.fit(X_numerics)\n",
        "visualizer.show()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "valMOh3A9OSt",
        "outputId": "558f8e93-6b5f-41e0-e27c-c38420126369"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph above shows the reduction of a distortion score as the number of clusters increases. However, there is no clear \"elbow\" visible. The underlying algorithm suggests 5 clusters. A choice of 5 or 6 clusters seems to be fair."
      ],
      "metadata": {
        "id": "KvvfX2A79Pkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = KMeans(random_state=1)\n",
        "visualizer = KElbowVisualizer(model, k=(2,10), metric='silhouette')\n",
        "\n",
        "visualizer.fit(X_numerics)\n",
        "visualizer.show()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "MS9j9BJ79Qrs",
        "outputId": "21865380-2b38-4f12-8b6e-9617551331ae"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Silhouette score method indicates the best options would be 5 or 6 clusters. Let's compare both."
      ],
      "metadata": {
        "id": "NCdKxHOL9Sc9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KM_5_clusters = KMeans(n_clusters=5, init='k-means++').fit(X_numerics) # initialise and fit K-Means model\n",
        "\n",
        "KM5_clustered = X_numerics.copy()\n",
        "KM5_clustered.loc[:,'Cluster'] = KM_5_clusters.labels_ # append labels to points"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAELNb9U9UMt",
        "outputId": "e93ef0d3-abb3-46f0-f7af-fba3f2694a4b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-Means algorithm generated the following 5 clusters:\n",
        "* clients with low annual income and high spending score\n",
        "* clients with medium annual income and medium spending score\n",
        "* clients with high annual income and low spending score\n",
        "* clients with high annual income and high spending score\n",
        "* clients with low annual income and low spending score"
      ],
      "metadata": {
        "id": "V1aY-1eN9XKe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KM_clust_sizes = KM5_clustered.groupby('Cluster').size().to_frame()\n",
        "KM_clust_sizes.columns = [\"KM_size\"]\n",
        "KM_clust_sizes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Il_lGuMM9a0S",
        "outputId": "6adad851-3540-471a-f8c4-e213d4fa5275"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         KM_size\n",
              "Cluster         \n",
              "0             80\n",
              "1             36\n",
              "2             39\n",
              "3             22\n",
              "4             23"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "KM_clust_sizes",
              "summary": "{\n  \"name\": \"KM_clust_sizes\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 22,\n        \"max\": 80,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          36,\n          23,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The biggest cluster is a cluster number 1 with 79 observations (\"medium-medium\" clients). There are two the smallest ones each containing 23 observations (cluster 3 \"high-high\" and cluster 0 \"low-high\" clients). Below there is a 3D projection of 5 generated clusters. It is not very helpful in terms of a visualisation in a static mode but if you run the code in an interactive environment (e.g. Spyder) you can rotate it!"
      ],
      "metadata": {
        "id": "Yi3wZ1PG9dHC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "fig = plt.figure(figsize=(7, 7))\n",
        "ax = Axes3D(fig, rect=[0, 0, .99, 1], elev=20, azim=210)\n",
        "ax.scatter(KM5_clustered['Age'],\n",
        "           KM5_clustered['Annual Income (k$)'],\n",
        "           KM5_clustered['Spending Score (1-100)'],\n",
        "           c=KM5_clustered['Cluster'],\n",
        "           s=35, edgecolor='k', cmap=plt.cm.Set1)\n",
        "\n",
        "ax.w_xaxis.set_ticklabels([])\n",
        "ax.w_yaxis.set_ticklabels([])\n",
        "ax.w_zaxis.set_ticklabels([])\n",
        "ax.set_xlabel('Age')\n",
        "ax.set_ylabel('Annual Income (k$)')\n",
        "ax.set_zlabel('Spending Score (1-100)')\n",
        "ax.set_title('3D view of K-Means 5 clusters')\n",
        "ax.dist = 12\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "cJCsHbjJ9vmn",
        "outputId": "7b0a5fc7-970b-412d-e2d1-97395e3c3941"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-2aceea3e6f93>:11: MatplotlibDeprecationWarning: The w_xaxis attribute was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use xaxis instead.\n",
            "  ax.w_xaxis.set_ticklabels([])\n",
            "<ipython-input-33-2aceea3e6f93>:12: MatplotlibDeprecationWarning: The w_yaxis attribute was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use yaxis instead.\n",
            "  ax.w_yaxis.set_ticklabels([])\n",
            "<ipython-input-33-2aceea3e6f93>:13: MatplotlibDeprecationWarning: The w_zaxis attribute was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use zaxis instead.\n",
            "  ax.w_zaxis.set_ticklabels([])\n",
            "<ipython-input-33-2aceea3e6f93>:18: MatplotlibDeprecationWarning: The dist attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later.\n",
            "  ax.dist = 12\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly as py\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "def tracer(db, n, name):\n",
        "    '''\n",
        "    This function returns trace object for Plotly\n",
        "    '''\n",
        "    return go.Scatter3d(\n",
        "        x = db[db['Cluster']==n]['Age'],\n",
        "        y = db[db['Cluster']==n]['Spending Score (1-100)'],\n",
        "        z = db[db['Cluster']==n]['Annual Income (k$)'],\n",
        "        mode = 'markers',\n",
        "        name = name,\n",
        "        marker = dict(\n",
        "            size = 5\n",
        "        )\n",
        "     )\n",
        "\n",
        "trace0 = tracer(KM5_clustered, 0, 'Cluster 0')\n",
        "trace1 = tracer(KM5_clustered, 1, 'Cluster 1')\n",
        "trace2 = tracer(KM5_clustered, 2, 'Cluster 2')\n",
        "trace3 = tracer(KM5_clustered, 3, 'Cluster 3')\n",
        "trace4 = tracer(KM5_clustered, 4, 'Cluster 4')\n",
        "\n",
        "data = [trace0, trace1, trace2, trace3, trace4]\n",
        "\n",
        "layout = go.Layout(\n",
        "    title = 'Clusters by K-Means',\n",
        "    scene = dict(\n",
        "            xaxis = dict(title = 'Age'),\n",
        "            yaxis = dict(title = 'Spending Score'),\n",
        "            zaxis = dict(title = 'Annual Income')\n",
        "        )\n",
        ")\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "py.offline.iplot(fig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "nkZx9hvS94pG",
        "outputId": "a0b5cd37-9dd1-454c-abea-5232b9fc1413"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.cluster import SilhouetteVisualizer\n",
        "model = KMeans(n_clusters=5, random_state=0)\n",
        "visualizer = SilhouetteVisualizer(model, colors='yellowbrick')\n",
        "visualizer.fit(X_numerics)\n",
        "visualizer.show()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "GeJk92HP96gk",
        "outputId": "2c7c6379-8b87-42fd-cc45-d8e9d989519c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning:\n",
            "\n",
            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "KM_6_clusters = KMeans(n_clusters=6, init='k-means++').fit(X_numerics) # initialise and fit K-Means model\n",
        "\n",
        "KM6_clustered = X_numerics.copy()\n",
        "KM6_clustered.loc[:,'Cluster'] = KM_6_clusters.labels_ # append labels to points"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIHJPbWo98Z2",
        "outputId": "dea62bee-2863-4e69-f0c6-9449f221f16b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning:\n",
            "\n",
            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-Means algorithm generated the following 6 clusters:\n",
        "\n",
        "0. younger clients with **medium** annual and **medium** spending score\n",
        "1. clients with **high** annual income and **low** spending score\n",
        "2. younger clients with **medium** annual and **medium** spending score\n",
        "3. clients with **high** annual income and **high** spending score\n",
        "4. clients with **low** annual income and **low** spending score\n",
        "5. clients with **low** annual income and **high** spending score\n",
        "\n",
        "There are no distinct groups is terms of customers age."
      ],
      "metadata": {
        "id": "dYbpa-9d-Aau"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KM6_clust_sizes = KM6_clustered.groupby('Cluster').size().to_frame()\n",
        "KM6_clust_sizes.columns = [\"KM_size\"]\n",
        "KM6_clust_sizes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "49x0FM_g-HNy",
        "outputId": "6379cc55-eaf2-4a77-bfac-fbe77c7811bf"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         KM_size\n",
              "Cluster         \n",
              "0             21\n",
              "1             45\n",
              "2             22\n",
              "3             38\n",
              "4             35\n",
              "5             39"
            ],
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              "    }\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-85a7cb41-a8e5-4891-acdc-01d60128b73f 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-85a7cb41-a8e5-4891-acdc-01d60128b73f');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "<div id=\"df-6cd7ebb1-a13c-4286-9f21-43e6aa0f8c7b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6cd7ebb1-a13c-4286-9f21-43e6aa0f8c7b')\"\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",
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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",
              "      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-6cd7ebb1-a13c-4286-9f21-43e6aa0f8c7b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a1888b41-4547-461b-aefe-c388a848f328\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('KM6_clust_sizes')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            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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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a1888b41-4547-461b-aefe-c388a848f328 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('KM6_clust_sizes');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "KM6_clust_sizes",
              "summary": "{\n  \"name\": \"KM6_clust_sizes\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 21,\n        \"max\": 45,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          21,\n          45,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotly interactive 3D plot\n",
        "\n",
        "trace0 = tracer(KM6_clustered, 0, 'Cluster 0')\n",
        "trace1 = tracer(KM6_clustered, 1, 'Cluster 1')\n",
        "trace2 = tracer(KM6_clustered, 2, 'Cluster 2')\n",
        "trace3 = tracer(KM6_clustered, 3, 'Cluster 3')\n",
        "trace4 = tracer(KM6_clustered, 4, 'Cluster 4')\n",
        "trace5 = tracer(KM6_clustered, 5, 'Cluster 5')\n",
        "\n",
        "data = [trace0, trace1, trace2, trace3, trace4, trace5]\n",
        "\n",
        "layout = go.Layout(\n",
        "    title = 'Clusters by K-Means',\n",
        "    scene = dict(\n",
        "            xaxis = dict(title = 'Age'),\n",
        "            yaxis = dict(title = 'Spending Score'),\n",
        "            zaxis = dict(title = 'Annual Income')\n",
        "        )\n",
        ")\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "py.offline.iplot(fig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "GDyhrky6-Iq_",
        "outputId": "81aca446-87b7-4de3-ff4b-eb53e991e9a1"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"af15f0ee-202f-4af1-a8e5-60d60374b2ed\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"af15f0ee-202f-4af1-a8e5-60d60374b2ed\")) {                    Plotly.newPlot(                        \"af15f0ee-202f-4af1-a8e5-60d60374b2ed\",                        [{\"marker\":{\"size\":5},\"mode\":\"markers\",\"name\":\"Cluster 0\",\"x\":[19,20,31,35,64,67,58,37,35,52,35,46,54,45,40,60,53,49,42,36,49],\"y\":[39,6,40,6,3,14,15,13,35,29,35,5,14,32,31,4,4,14,17,26,28],\"z\":[15,16,17,18,19,19,20,20,21,23,24,25,28,28,29,30,33,33,34,37,39],\"type\":\"scatter3d\"},{\"marker\":{\"size\":5},\"mode\":\"markers\",\"name\":\"Cluster 1\",\"x\":[65,48,50,49,59,50,47,51,69,53,70,67,54,63,43,68,70,47,60,60,59,45,49,57,67,46,48,55,50,68,48,47,48,49,67,49,66,54,68,66,65,63,49,51,50],\"y\":[35,36,55,52,60,45,41,50,46,46,56,52,59,51,50,48,55,42,49,56,47,53,42,51,41,44,46,58,46,55,49,47,42,48,59,56,50,46,43,48,52,43,59,43,57],\"z\":[38,39,40,42,43,43,43,44,44,46,46,47,47,48,48,48,49,49,50,50,54,54,54,54,54,54,54,57,58,59,60,60,61,62,62,62,63,63,63,63,63,65,65,67,67],\"type\":\"scatter3d\"},{\"marker\":{\"size\":5},\"mode\":\"markers\",\"name\":\"Cluster 2\",\"x\":[21,23,22,23,30,35,24,22,20,35,25,31,29,35,23,21,18,21,30,20,24,24],\"y\":[81,77,76,94,72,99,77,79,66,98,73,73,82,61,87,73,92,81,73,75,92,65],\"z\":[15,16,17,18,19,19,20,20,21,23,24,25,28,28,29,30,33,33,34,37,38,39],\"type\":\"scatter3d\"},{\"marker\":{\"size\":5},\"mode\":\"markers\",\"name\":\"Cluster 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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = KMeans(n_clusters=6, random_state=0)\n",
        "visualizer = SilhouetteVisualizer(model, colors='yellowbrick')\n",
        "visualizer.fit(X_numerics)\n",
        "visualizer.show()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "zP2w31uP-Ksv",
        "outputId": "07a25395-9644-4253-c33c-74f9e09c5ac7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning:\n",
            "\n",
            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4.2 DBSCAN**"
      ],
      "metadata": {
        "id": "i6eX2wQ9-OX4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.2.1 Description**"
      ],
      "metadata": {
        "id": "1ZT7-d3I-Q_q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.2.2 Use case - customers segmentation**"
      ],
      "metadata": {
        "id": "knN1yPd7-Ucs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import DBSCAN"
      ],
      "metadata": {
        "id": "Kj6Zu29B-Zyv"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In DBSCAN there are two major hyperparameters:\n",
        "\n",
        "* eps\n",
        "* min_samples\n",
        "\n",
        "It is difficult arbitrarily to say what values will work the best. Therefore, I will first create a matrix of investigated combinations."
      ],
      "metadata": {
        "id": "sbdC4wW0-Q9_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import product\n",
        "\n",
        "eps_values = np.arange(8,12.75,0.25) # eps values to be investigated\n",
        "min_samples = np.arange(3,10) # min_samples values to be investigated\n",
        "\n",
        "DBSCAN_params = list(product(eps_values, min_samples))"
      ],
      "metadata": {
        "id": "yYvPuqaf-fBf"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because DBSCAN creates clusters itself based on those two parameters let's check the number of generated clusters."
      ],
      "metadata": {
        "id": "-h5NIyxr-iJG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "no_of_clusters = []\n",
        "sil_score = []\n",
        "\n",
        "for p in DBSCAN_params:\n",
        "    DBS_clustering = DBSCAN(eps=p[0], min_samples=p[1]).fit(X_numerics)\n",
        "    no_of_clusters.append(len(np.unique(DBS_clustering.labels_)))\n",
        "    sil_score.append(silhouette_score(X_numerics, DBS_clustering.labels_))"
      ],
      "metadata": {
        "id": "vHX9vScE-kBO"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])\n",
        "tmp['No_of_clusters'] = no_of_clusters\n",
        "\n",
        "pivot_1 = pd.pivot_table(tmp, values='No_of_clusters', index='Min_samples', columns='Eps')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,6))\n",
        "sns.heatmap(pivot_1, annot=True,annot_kws={\"size\": 16}, cmap=\"YlGnBu\", ax=ax)\n",
        "ax.set_title('Number of clusters')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "YrXyAmrL-nNI",
        "outputId": "4dcd80a8-1c09-48fd-980f-eda68d03a407"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The heatplot above shows, the number of clusters vary from 17 to 4. However, most of the combinations gives 4-7 clusters. To decide which combination to choose I will use a metric - a silhuette score and I will plot it as a heatmap again."
      ],
      "metadata": {
        "id": "HW2oDs5U-syp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])\n",
        "tmp['Sil_score'] = sil_score\n",
        "\n",
        "pivot_1 = pd.pivot_table(tmp, values='Sil_score', index='Min_samples', columns='Eps')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(18,6))\n",
        "sns.heatmap(pivot_1, annot=True, annot_kws={\"size\": 10}, cmap=\"YlGnBu\", ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "7zNXSjGs-tfW",
        "outputId": "ea30cd6f-94ac-413b-de30-e09331411db3"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Global maximum is 0.26 for eps=12.5 and min_samples=4."
      ],
      "metadata": {
        "id": "Vt93bg_L-wtR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DBS_clustering = DBSCAN(eps=12.5, min_samples=4).fit(X_numerics)\n",
        "\n",
        "DBSCAN_clustered = X_numerics.copy()\n",
        "DBSCAN_clustered.loc[:,'Cluster'] = DBS_clustering.labels_ # append labels to points"
      ],
      "metadata": {
        "id": "wIW5IwnS-xP5"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DBSCAN_clust_sizes = DBSCAN_clustered.groupby('Cluster').size().to_frame()\n",
        "DBSCAN_clust_sizes.columns = [\"DBSCAN_size\"]\n",
        "DBSCAN_clust_sizes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Jxg8zvC1-zn0",
        "outputId": "4640223a-84c3-4360-88f1-c4abc42ddb99"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         DBSCAN_size\n",
              "Cluster             \n",
              "-1                18\n",
              " 0               112\n",
              " 1                 8\n",
              " 2                34\n",
              " 3                24\n",
              " 4                 4"
            ],
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "DBSCAN_clust_sizes",
              "summary": "{\n  \"name\": \"DBSCAN_clust_sizes\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 4,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -1,\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DBSCAN_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40,\n        \"min\": 4,\n        \"max\": 112,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          18,\n          112,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DBSCAN created 5 clusters plus outliers cluster (-1). Sizes of clusters 0-4 vary significantly - some have only 4 or 8 observations. There are 18 outliers."
      ],
      "metadata": {
        "id": "y3bAAl7S-0jW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph above shows that there are some outliers - these points do not meet distance and minimum samples requirements to be recognised as a cluster."
      ],
      "metadata": {
        "id": "f8Scl327-8aV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4.3 Affinity Propagation**"
      ],
      "metadata": {
        "id": "KGuJLWSp-9aD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.3.1 Description"
      ],
      "metadata": {
        "id": "rVEc_7UD_Aff"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.3.2 Use case - customers segmentation**"
      ],
      "metadata": {
        "id": "-B4trMHw_DDN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import AffinityPropagation"
      ],
      "metadata": {
        "id": "OX-Conbz_H-x"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_of_clusters = []\n",
        "preferences = range(-20000,-5000,100) # arbitraty chosen range\n",
        "af_sil_score = [] # silouette scores\n",
        "\n",
        "for p in preferences:\n",
        "    AF = AffinityPropagation(preference=p, max_iter=200).fit(X_numerics)\n",
        "    no_of_clusters.append((len(np.unique(AF.labels_))))\n",
        "    af_sil_score.append(silhouette_score(X_numerics, AF.labels_))\n",
        "\n",
        "af_results = pd.DataFrame([preferences, no_of_clusters, af_sil_score], index=['preference','clusters', 'sil_score']).T\n",
        "af_results.sort_values(by='sil_score', ascending=False).head() # display only 5 best scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "nfw25FJi_Ifa",
        "outputId": "633c7138-ff19-443c-9f9b-f130d906daa1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_affinity_propagation.py:142: ConvergenceWarning:\n",
            "\n",
            "Affinity propagation did not converge, this model may return degenerate cluster centers and labels.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    preference  clusters  sil_score\n",
              "82    -11800.0       6.0   0.451649\n",
              "27    -17300.0       6.0   0.451491\n",
              "51    -14900.0       6.0   0.451491\n",
              "52    -14800.0       6.0   0.451440\n",
              "28    -17200.0       6.0   0.451440"
            ],
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"af_results\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"preference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2248.3327155917113,\n        \"min\": -17300.0,\n        \"max\": -11800.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -17300.0,\n          -17200.0,\n          -14900.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clusters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 6.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sil_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.60616355222455e-05,\n        \"min\": 0.45143978396977485,\n        \"max\": 0.4516490888773576,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.4516490888773576\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AF = AffinityPropagation(preference=-11800).fit(X_numerics)"
      ],
      "metadata": {
        "id": "TCalX-yb_MqJ"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AF_clustered = X_numerics.copy()\n",
        "AF_clustered.loc[:,'Cluster'] = AF.labels_ # append labels to points"
      ],
      "metadata": {
        "id": "IUzZLgnl_N3i"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AF_clust_sizes = AF_clustered.groupby('Cluster').size().to_frame()\n",
        "AF_clust_sizes.columns = [\"AF_size\"]\n",
        "AF_clust_sizes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "9g_crc9l_R6k",
        "outputId": "bbee0129-304e-407a-9d6d-1cc0aaba9701"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         AF_size\n",
              "Cluster         \n",
              "0             22\n",
              "1             22\n",
              "2             44\n",
              "3             39\n",
              "4             34\n",
              "5             39"
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              "\n",
              "  <div id=\"id_4ac82bc5-ee51-43e8-99ce-b4c95519143d\">\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('AF_clust_sizes')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "        document.querySelector('#id_4ac82bc5-ee51-43e8-99ce-b4c95519143d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('AF_clust_sizes');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "AF_clust_sizes",
              "summary": "{\n  \"name\": \"AF_clust_sizes\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AF_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 22,\n        \"max\": 44,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          44,\n          34,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Clusters generated by the Affinity Propagation algorithm created relatively even-sized clusters similar to ones created by K-Means."
      ],
      "metadata": {
        "id": "b5wRTaZ5_W-E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5. Comparison and discussion**"
      ],
      "metadata": {
        "id": "AnE4K9G4_X3_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig2.suptitle('DBSCAN', fontsize=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cgmW2EwAmog",
        "outputId": "781041a5-cb13-47a3-92ef-2f796a08cafc"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'DBSCAN')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "clusters = pd.concat([KM6_clust_sizes, DBSCAN_clust_sizes, AF_clust_sizes],axis=1, sort=False)\n",
        "clusters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "7xru9B0wArmz",
        "outputId": "fb7a6ae5-c7a9-4abb-e9de-d74db3c30b16"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         KM_size  DBSCAN_size  AF_size\n",
              "Cluster                               \n",
              " 0          21.0        112.0     22.0\n",
              " 1          45.0          8.0     22.0\n",
              " 2          22.0         34.0     44.0\n",
              " 3          38.0         24.0     39.0\n",
              " 4          35.0          4.0     34.0\n",
              " 5          39.0          NaN     39.0\n",
              "-1           NaN         18.0      NaN"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>KM_size</th>\n",
              "      <th>DBSCAN_size</th>\n",
              "      <th>AF_size</th>\n",
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              "      <th>Cluster</th>\n",
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              "      <th>1</th>\n",
              "      <td>45.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>39.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15fc34b8-f689-41d7-b554-a805f88d6d8c')\"\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-15fc34b8-f689-41d7-b554-a805f88d6d8c 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-15fc34b8-f689-41d7-b554-a805f88d6d8c');\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-77acbdf8-5122-48c6-a915-a3ab4d0fbf51\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-77acbdf8-5122-48c6-a915-a3ab4d0fbf51')\"\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",
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              "    <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",
              "      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-77acbdf8-5122-48c6-a915-a3ab4d0fbf51 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_84075cab-ef59-415a-9778-96ed2ec57a09\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('clusters')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_84075cab-ef59-415a-9778-96ed2ec57a09 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('clusters');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "clusters",
              "summary": "{\n  \"name\": \"clusters\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": -1,\n        \"max\": 5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.72967967955095,\n        \"min\": 21.0,\n        \"max\": 45.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          21.0,\n          45.0,\n          39.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DBSCAN_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40.033319456006474,\n        \"min\": 4.0,\n        \"max\": 112.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          112.0,\n          8.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AF_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.330952077181978,\n        \"min\": 22.0,\n        \"max\": 44.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          44.0,\n          34.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    }
  ]
}