project_2_level_1.ipynb

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      "source": [
        "# **MEHWISH ZAMAN (DATA ANALYST)**\n",
        "  AS AN INTERN AT OASIS INFOBYTE\n",
        "\n",
        "\n",
        "  **PROJECT PROPOSAL_2**\n",
        "               ( LEVEL_1)\n",
        "\n",
        "\n",
        "#    **Idea: Customer Segmentation Analysis**\n",
        "\n",
        "# **Project Description:**\n",
        "The aim of this data analytics project is to perform customer segmentation analysis for an e-\n",
        "commerce company. By analyzing customer behavior and purchase patterns, the goal is to\n",
        "group customers into distinct segments. This segmentation can inform targeted marketing\n",
        "strategies, improve customer satisfaction, and enhance overall business strategies.\n",
        "\n",
        "\n",
        "# **Key Concepts and Challenges:**\n",
        "1. Data Collection: Obtain a dataset containing customer information, purchase history, and relevant data.\n",
        "2. Data Exploration and Cleaning: Explore the dataset, understand its structure, and handle any missing or inconsistent data.\n",
        "3. Descriptive Statistics: Calculate key metrics such as average purchase value, frequency of purchases, etc.\n",
        "4. Customer Segmentation: Utilize clustering algorithms (e.g., K-means) to segment customers based on behavior and purchase patterns.\n",
        "5. Visualization: Create visualizations (e.g., scatter plots, bar charts) to illustrate customer segments.\n",
        "6. Insights and Recommendations: Analyze characteristics of each segment and provide insights."
      ],
      "metadata": {
        "id": "1mMBOogokmHY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **DATA COLLECTION**"
      ],
      "metadata": {
        "id": "Ga3fbw_dmUJc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sab5ysWvJD3J"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime as dt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import pointbiserialr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/project_2_level_1.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0RRsM5OKL36",
        "outputId": "b26cfa67-c154-4c7a-ef23-298792ae3764"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/project_2_level_1.zip\n",
            "  inflating: dictionary.png          \n",
            "  inflating: ifood_df.csv            \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/ifood_df.csv')"
      ],
      "metadata": {
        "id": "rGFQr3paPdcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EXPLORING DATA**"
      ],
      "metadata": {
        "id": "k5GEBwEwmdSu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Dataset\n",
        "# Dataset First Look\n",
        "df.head()\n",
        "df"
      ],
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          "height": 461
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        "id": "dmaxuZPNPq4p",
        "outputId": "770e9e7d-3ec8-43dd-95dc-ffd7ae1629d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Income  Kidhome  Teenhome  Recency  MntWines  MntFruits  \\\n",
              "0     58138.0        0         0       58       635         88   \n",
              "1     46344.0        1         1       38        11          1   \n",
              "2     71613.0        0         0       26       426         49   \n",
              "3     26646.0        1         0       26        11          4   \n",
              "4     58293.0        1         0       94       173         43   \n",
              "...       ...      ...       ...      ...       ...        ...   \n",
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              "2201  64014.0        2         1       56       406          0   \n",
              "2202  56981.0        0         0       91       908         48   \n",
              "2203  69245.0        0         1        8       428         30   \n",
              "2204  52869.0        1         1       40        84          3   \n",
              "\n",
              "      MntMeatProducts  MntFishProducts  MntSweetProducts  MntGoldProds  ...  \\\n",
              "0                 546              172                88            88  ...   \n",
              "1                   6                2                 1             6  ...   \n",
              "2                 127              111                21            42  ...   \n",
              "3                  20               10                 3             5  ...   \n",
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              "...               ...              ...               ...           ...  ...   \n",
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              "2201               30                0                 0             8  ...   \n",
              "2202              217               32                12            24  ...   \n",
              "2203              214               80                30            61  ...   \n",
              "2204               61                2                 1            21  ...   \n",
              "\n",
              "      marital_Together  marital_Widow  education_2n Cycle  education_Basic  \\\n",
              "0                    0              0                   0                0   \n",
              "1                    0              0                   0                0   \n",
              "2                    1              0                   0                0   \n",
              "3                    1              0                   0                0   \n",
              "4                    0              0                   0                0   \n",
              "...                ...            ...                 ...              ...   \n",
              "2200                 0              0                   0                0   \n",
              "2201                 1              0                   0                0   \n",
              "2202                 0              0                   0                0   \n",
              "2203                 1              0                   0                0   \n",
              "2204                 0              0                   0                0   \n",
              "\n",
              "      education_Graduation  education_Master  education_PhD  MntTotal  \\\n",
              "0                        1                 0              0      1529   \n",
              "1                        1                 0              0        21   \n",
              "2                        1                 0              0       734   \n",
              "3                        1                 0              0        48   \n",
              "4                        0                 0              1       407   \n",
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              "2202                     1                 0              0      1217   \n",
              "2203                     0                 1              0       782   \n",
              "2204                     0                 0              1       151   \n",
              "\n",
              "      MntRegularProds  AcceptedCmpOverall  \n",
              "0                1441                   0  \n",
              "1                  15                   0  \n",
              "2                 692                   0  \n",
              "3                  43                   0  \n",
              "4                 392                   0  \n",
              "...               ...                 ...  \n",
              "2200              847                   0  \n",
              "2201              428                   1  \n",
              "2202             1193                   1  \n",
              "2203              721                   0  \n",
              "2204              130                   0  \n",
              "\n",
              "[2205 rows x 39 columns]"
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              "                                                    [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-a674d7e6-7a7d-4630-822e-cc8bd9b8eb96\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a674d7e6-7a7d-4630-822e-cc8bd9b8eb96')\"\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-a674d7e6-7a7d-4630-822e-cc8bd9b8eb96 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e1dcea16-9aed-4b7d-9462-697d245a942c\">\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('df')\"\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_e1dcea16-9aed-4b7d-9462-697d245a942c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Columns.\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xteebse2Px2A",
        "outputId": "cf274e94-f87b-42c3-85fa-631d83687d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Income', 'Kidhome', 'Teenhome', 'Recency', 'MntWines', 'MntFruits',\n",
              "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
              "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
              "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
              "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
              "       'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response',\n",
              "       'Age', 'Customer_Days', 'marital_Divorced', 'marital_Married',\n",
              "       'marital_Single', 'marital_Together', 'marital_Widow',\n",
              "       'education_2n Cycle', 'education_Basic', 'education_Graduation',\n",
              "       'education_Master', 'education_PhD', 'MntTotal', 'MntRegularProds',\n",
              "       'AcceptedCmpOverall'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Describe.\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "hFxyaJGsSU7S",
        "outputId": "16d70271-178d-4d7a-ca96-8e1603158a50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Income      Kidhome     Teenhome      Recency     MntWines  \\\n",
              "count    2205.000000  2205.000000  2205.000000  2205.000000  2205.000000   \n",
              "mean    51622.094785     0.442177     0.506576    49.009070   306.164626   \n",
              "std     20713.063826     0.537132     0.544380    28.932111   337.493839   \n",
              "min      1730.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%     35196.000000     0.000000     0.000000    24.000000    24.000000   \n",
              "50%     51287.000000     0.000000     0.000000    49.000000   178.000000   \n",
              "75%     68281.000000     1.000000     1.000000    74.000000   507.000000   \n",
              "max    113734.000000     2.000000     2.000000    99.000000  1493.000000   \n",
              "\n",
              "         MntFruits  MntMeatProducts  MntFishProducts  MntSweetProducts  \\\n",
              "count  2205.000000      2205.000000      2205.000000       2205.000000   \n",
              "mean     26.403175       165.312018        37.756463         27.128345   \n",
              "std      39.784484       217.784507        54.824635         41.130468   \n",
              "min       0.000000         0.000000         0.000000          0.000000   \n",
              "25%       2.000000        16.000000         3.000000          1.000000   \n",
              "50%       8.000000        68.000000        12.000000          8.000000   \n",
              "75%      33.000000       232.000000        50.000000         34.000000   \n",
              "max     199.000000      1725.000000       259.000000        262.000000   \n",
              "\n",
              "       MntGoldProds  ...  marital_Together  marital_Widow  education_2n Cycle  \\\n",
              "count   2205.000000  ...       2205.000000    2205.000000         2205.000000   \n",
              "mean      44.057143  ...          0.257596       0.034467            0.089796   \n",
              "std       51.736211  ...          0.437410       0.182467            0.285954   \n",
              "min        0.000000  ...          0.000000       0.000000            0.000000   \n",
              "25%        9.000000  ...          0.000000       0.000000            0.000000   \n",
              "50%       25.000000  ...          0.000000       0.000000            0.000000   \n",
              "75%       56.000000  ...          1.000000       0.000000            0.000000   \n",
              "max      321.000000  ...          1.000000       1.000000            1.000000   \n",
              "\n",
              "       education_Basic  education_Graduation  education_Master  education_PhD  \\\n",
              "count      2205.000000           2205.000000       2205.000000    2205.000000   \n",
              "mean          0.024490              0.504762          0.165079       0.215873   \n",
              "std           0.154599              0.500091          0.371336       0.411520   \n",
              "min           0.000000              0.000000          0.000000       0.000000   \n",
              "25%           0.000000              0.000000          0.000000       0.000000   \n",
              "50%           0.000000              1.000000          0.000000       0.000000   \n",
              "75%           0.000000              1.000000          0.000000       0.000000   \n",
              "max           1.000000              1.000000          1.000000       1.000000   \n",
              "\n",
              "          MntTotal  MntRegularProds  AcceptedCmpOverall  \n",
              "count  2205.000000      2205.000000          2205.00000  \n",
              "mean    562.764626       518.707483             0.29932  \n",
              "std     575.936911       553.847248             0.68044  \n",
              "min       4.000000      -283.000000             0.00000  \n",
              "25%      56.000000        42.000000             0.00000  \n",
              "50%     343.000000       288.000000             0.00000  \n",
              "75%     964.000000       884.000000             0.00000  \n",
              "max    2491.000000      2458.000000             4.00000  \n",
              "\n",
              "[8 rows x 39 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e184feba-a817-453c-a255-bf7c0d47e9d6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Income</th>\n",
              "      <th>Kidhome</th>\n",
              "      <th>Teenhome</th>\n",
              "      <th>Recency</th>\n",
              "      <th>MntWines</th>\n",
              "      <th>MntFruits</th>\n",
              "      <th>MntMeatProducts</th>\n",
              "      <th>MntFishProducts</th>\n",
              "      <th>MntSweetProducts</th>\n",
              "      <th>MntGoldProds</th>\n",
              "      <th>...</th>\n",
              "      <th>marital_Together</th>\n",
              "      <th>marital_Widow</th>\n",
              "      <th>education_2n Cycle</th>\n",
              "      <th>education_Basic</th>\n",
              "      <th>education_Graduation</th>\n",
              "      <th>education_Master</th>\n",
              "      <th>education_PhD</th>\n",
              "      <th>MntTotal</th>\n",
              "      <th>MntRegularProds</th>\n",
              "      <th>AcceptedCmpOverall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.000000</td>\n",
              "      <td>2205.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>51622.094785</td>\n",
              "      <td>0.442177</td>\n",
              "      <td>0.506576</td>\n",
              "      <td>49.009070</td>\n",
              "      <td>306.164626</td>\n",
              "      <td>26.403175</td>\n",
              "      <td>165.312018</td>\n",
              "      <td>37.756463</td>\n",
              "      <td>27.128345</td>\n",
              "      <td>44.057143</td>\n",
              "      <td>...</td>\n",
              "      <td>0.257596</td>\n",
              "      <td>0.034467</td>\n",
              "      <td>0.089796</td>\n",
              "      <td>0.024490</td>\n",
              "      <td>0.504762</td>\n",
              "      <td>0.165079</td>\n",
              "      <td>0.215873</td>\n",
              "      <td>562.764626</td>\n",
              "      <td>518.707483</td>\n",
              "      <td>0.29932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20713.063826</td>\n",
              "      <td>0.537132</td>\n",
              "      <td>0.544380</td>\n",
              "      <td>28.932111</td>\n",
              "      <td>337.493839</td>\n",
              "      <td>39.784484</td>\n",
              "      <td>217.784507</td>\n",
              "      <td>54.824635</td>\n",
              "      <td>41.130468</td>\n",
              "      <td>51.736211</td>\n",
              "      <td>...</td>\n",
              "      <td>0.437410</td>\n",
              "      <td>0.182467</td>\n",
              "      <td>0.285954</td>\n",
              "      <td>0.154599</td>\n",
              "      <td>0.500091</td>\n",
              "      <td>0.371336</td>\n",
              "      <td>0.411520</td>\n",
              "      <td>575.936911</td>\n",
              "      <td>553.847248</td>\n",
              "      <td>0.68044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1730.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>-283.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35196.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51287.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>343.000000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>68281.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>507.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>964.000000</td>\n",
              "      <td>884.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>113734.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>1493.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>259.000000</td>\n",
              "      <td>262.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2491.000000</td>\n",
              "      <td>2458.000000</td>\n",
              "      <td>4.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 39 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e184feba-a817-453c-a255-bf7c0d47e9d6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-e184feba-a817-453c-a255-bf7c0d47e9d6 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-e184feba-a817-453c-a255-bf7c0d47e9d6');\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-7b509606-cd58-460c-828e-de19136c4245\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7b509606-cd58-460c-828e-de19136c4245')\"\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-7b509606-cd58-460c-828e-de19136c4245 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Info.\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_PBDE8kXSZOs",
        "outputId": "462fdcde-e6e3-48ff-d288-bd791f5d8934"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2205 entries, 0 to 2204\n",
            "Data columns (total 39 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   Income                2205 non-null   float64\n",
            " 1   Kidhome               2205 non-null   int64  \n",
            " 2   Teenhome              2205 non-null   int64  \n",
            " 3   Recency               2205 non-null   int64  \n",
            " 4   MntWines              2205 non-null   int64  \n",
            " 5   MntFruits             2205 non-null   int64  \n",
            " 6   MntMeatProducts       2205 non-null   int64  \n",
            " 7   MntFishProducts       2205 non-null   int64  \n",
            " 8   MntSweetProducts      2205 non-null   int64  \n",
            " 9   MntGoldProds          2205 non-null   int64  \n",
            " 10  NumDealsPurchases     2205 non-null   int64  \n",
            " 11  NumWebPurchases       2205 non-null   int64  \n",
            " 12  NumCatalogPurchases   2205 non-null   int64  \n",
            " 13  NumStorePurchases     2205 non-null   int64  \n",
            " 14  NumWebVisitsMonth     2205 non-null   int64  \n",
            " 15  AcceptedCmp3          2205 non-null   int64  \n",
            " 16  AcceptedCmp4          2205 non-null   int64  \n",
            " 17  AcceptedCmp5          2205 non-null   int64  \n",
            " 18  AcceptedCmp1          2205 non-null   int64  \n",
            " 19  AcceptedCmp2          2205 non-null   int64  \n",
            " 20  Complain              2205 non-null   int64  \n",
            " 21  Z_CostContact         2205 non-null   int64  \n",
            " 22  Z_Revenue             2205 non-null   int64  \n",
            " 23  Response              2205 non-null   int64  \n",
            " 24  Age                   2205 non-null   int64  \n",
            " 25  Customer_Days         2205 non-null   int64  \n",
            " 26  marital_Divorced      2205 non-null   int64  \n",
            " 27  marital_Married       2205 non-null   int64  \n",
            " 28  marital_Single        2205 non-null   int64  \n",
            " 29  marital_Together      2205 non-null   int64  \n",
            " 30  marital_Widow         2205 non-null   int64  \n",
            " 31  education_2n Cycle    2205 non-null   int64  \n",
            " 32  education_Basic       2205 non-null   int64  \n",
            " 33  education_Graduation  2205 non-null   int64  \n",
            " 34  education_Master      2205 non-null   int64  \n",
            " 35  education_PhD         2205 non-null   int64  \n",
            " 36  MntTotal              2205 non-null   int64  \n",
            " 37  MntRegularProds       2205 non-null   int64  \n",
            " 38  AcceptedCmpOverall    2205 non-null   int64  \n",
            "dtypes: float64(1), int64(38)\n",
            "memory usage: 672.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking Unique Values for each variable.\n",
        "unique_values = df.nunique()\n",
        "unique_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Liva3GZISc66",
        "outputId": "4bbbe8de-802b-4782-fddd-64a1fc3a8980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Income                  1963\n",
              "Kidhome                    3\n",
              "Teenhome                   3\n",
              "Recency                  100\n",
              "MntWines                 775\n",
              "MntFruits                158\n",
              "MntMeatProducts          551\n",
              "MntFishProducts          182\n",
              "MntSweetProducts         176\n",
              "MntGoldProds             212\n",
              "NumDealsPurchases         15\n",
              "NumWebPurchases           15\n",
              "NumCatalogPurchases       13\n",
              "NumStorePurchases         14\n",
              "NumWebVisitsMonth         16\n",
              "AcceptedCmp3               2\n",
              "AcceptedCmp4               2\n",
              "AcceptedCmp5               2\n",
              "AcceptedCmp1               2\n",
              "AcceptedCmp2               2\n",
              "Complain                   2\n",
              "Z_CostContact              1\n",
              "Z_Revenue                  1\n",
              "Response                   2\n",
              "Age                       56\n",
              "Customer_Days            662\n",
              "marital_Divorced           2\n",
              "marital_Married            2\n",
              "marital_Single             2\n",
              "marital_Together           2\n",
              "marital_Widow              2\n",
              "education_2n Cycle         2\n",
              "education_Basic            2\n",
              "education_Graduation       2\n",
              "education_Master           2\n",
              "education_PhD              2\n",
              "MntTotal                 897\n",
              "MntRegularProds          974\n",
              "AcceptedCmpOverall         5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates\n",
        "print(f\"Number of duplicates:\\n{df.duplicated().sum()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0ZYl3GdSgmD",
        "outputId": "3e922775-0734-4028-fdcc-4d24fe7eb1c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicates:\n",
            "184\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values/Null Values Count.\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DScZ4eOzSr40",
        "outputId": "3943e8c2-cec1-4ffe-a40b-2635df62a20d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Income                  0\n",
              "Kidhome                 0\n",
              "Teenhome                0\n",
              "Recency                 0\n",
              "MntWines                0\n",
              "MntFruits               0\n",
              "MntMeatProducts         0\n",
              "MntFishProducts         0\n",
              "MntSweetProducts        0\n",
              "MntGoldProds            0\n",
              "NumDealsPurchases       0\n",
              "NumWebPurchases         0\n",
              "NumCatalogPurchases     0\n",
              "NumStorePurchases       0\n",
              "NumWebVisitsMonth       0\n",
              "AcceptedCmp3            0\n",
              "AcceptedCmp4            0\n",
              "AcceptedCmp5            0\n",
              "AcceptedCmp1            0\n",
              "AcceptedCmp2            0\n",
              "Complain                0\n",
              "Z_CostContact           0\n",
              "Z_Revenue               0\n",
              "Response                0\n",
              "Age                     0\n",
              "Customer_Days           0\n",
              "marital_Divorced        0\n",
              "marital_Married         0\n",
              "marital_Single          0\n",
              "marital_Together        0\n",
              "marital_Widow           0\n",
              "education_2n Cycle      0\n",
              "education_Basic         0\n",
              "education_Graduation    0\n",
              "education_Master        0\n",
              "education_PhD           0\n",
              "MntTotal                0\n",
              "MntRegularProds         0\n",
              "AcceptedCmpOverall      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dropping irrelvant columns\n",
        "df.drop(columns=['Z_CostContact','Z_Revenue'],inplace=True)"
      ],
      "metadata": {
        "id": "V0ckGhGBSvFw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **DESCRIPTIVE STATISTICS**"
      ],
      "metadata": {
        "id": "KClyEf8igXzF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "sns.boxplot(data=df, y='MntTotal')\n",
        "plt.title('Box Plot for MntTotal')\n",
        "plt.ylabel('MntTotal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "C0OX25mHVyf_",
        "outputId": "f0a148dd-746f-44b7-83b0-175c484ce0c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LOOKING FOR OUTLIERS & REMOVING THEM**"
      ],
      "metadata": {
        "id": "SELUj03bgjG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df['MntTotal'].quantile(0.25)\n",
        "Q3 = df['MntTotal'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "outliers = df[(df['MntTotal'] < lower_bound) | (df['MntTotal'] > upper_bound)]\n",
        "outliers.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "ImxoJDfDW3pA",
        "outputId": "8d5f7fbd-42fb-4d45-a350-cfb131dc9f74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Income  Kidhome  Teenhome  Recency  MntWines  MntFruits  \\\n",
              "1159  90638.0        0         0       29      1156        120   \n",
              "1467  87679.0        0         0       62      1259        172   \n",
              "1547  90638.0        0         0       29      1156        120   \n",
              "\n",
              "      MntMeatProducts  MntFishProducts  MntSweetProducts  MntGoldProds  ...  \\\n",
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              "[3 rows x 37 columns]"
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          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df[(df['MntTotal'] > lower_bound) & (df['MntTotal'] < upper_bound)]\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "OKUwSZTPYkXI",
        "outputId": "1392edfd-cc88-481f-df25-b863fd19198e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Income      Kidhome     Teenhome      Recency     MntWines  \\\n",
              "count    2202.000000  2202.000000  2202.000000  2202.000000  2202.000000   \n",
              "mean    51570.283379     0.442779     0.507266    49.021344   304.960036   \n",
              "std     20679.438848     0.537250     0.544429    28.944211   336.135586   \n",
              "min      1730.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%     35182.500000     0.000000     0.000000    24.000000    24.000000   \n",
              "50%     51258.500000     0.000000     0.000000    49.000000   176.500000   \n",
              "75%     68146.500000     1.000000     1.000000    74.000000   505.000000   \n",
              "max    113734.000000     2.000000     2.000000    99.000000  1493.000000   \n",
              "\n",
              "         MntFruits  MntMeatProducts  MntFishProducts  MntSweetProducts  \\\n",
              "count  2202.000000      2202.000000      2202.000000       2202.000000   \n",
              "mean     26.252044       164.336058        37.678474         26.967302   \n",
              "std      39.589747       216.312982        54.821185         40.926101   \n",
              "min       0.000000         0.000000         0.000000          0.000000   \n",
              "25%       2.000000        16.000000         3.000000          1.000000   \n",
              "50%       8.000000        68.000000        12.000000          8.000000   \n",
              "75%      33.000000       230.750000        50.000000         33.000000   \n",
              "max     199.000000      1725.000000       259.000000        262.000000   \n",
              "\n",
              "       MntGoldProds  ...  marital_Together  marital_Widow  education_2n Cycle  \\\n",
              "count   2202.000000  ...       2202.000000    2202.000000         2202.000000   \n",
              "mean      44.014986  ...          0.257493       0.034514            0.089918   \n",
              "std       51.747221  ...          0.437353       0.182587            0.286130   \n",
              "min        0.000000  ...          0.000000       0.000000            0.000000   \n",
              "25%        9.000000  ...          0.000000       0.000000            0.000000   \n",
              "50%       25.000000  ...          0.000000       0.000000            0.000000   \n",
              "75%       56.000000  ...          1.000000       0.000000            0.000000   \n",
              "max      321.000000  ...          1.000000       1.000000            1.000000   \n",
              "\n",
              "       education_Basic  education_Graduation  education_Master  education_PhD  \\\n",
              "count      2202.000000           2202.000000       2202.000000    2202.000000   \n",
              "mean          0.024523              0.504995          0.164396       0.216167   \n",
              "std           0.154702              0.500089          0.370719       0.411723   \n",
              "min           0.000000              0.000000          0.000000       0.000000   \n",
              "25%           0.000000              0.000000          0.000000       0.000000   \n",
              "50%           0.000000              1.000000          0.000000       0.000000   \n",
              "75%           0.000000              1.000000          0.000000       0.000000   \n",
              "max           1.000000              1.000000          1.000000       1.000000   \n",
              "\n",
              "          MntTotal  MntRegularProds  AcceptedCmpOverall  \n",
              "count  2202.000000      2202.000000         2202.000000  \n",
              "mean    560.193915       516.178928            0.297457  \n",
              "std     572.096830       549.962471            0.678134  \n",
              "min       4.000000      -283.000000            0.000000  \n",
              "25%      56.000000        42.000000            0.000000  \n",
              "50%     342.500000       288.000000            0.000000  \n",
              "75%     962.000000       883.000000            0.000000  \n",
              "max    2304.000000      2259.000000            4.000000  \n",
              "\n",
              "[8 rows x 37 columns]"
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>51570.283379</td>\n",
              "      <td>0.442779</td>\n",
              "      <td>0.507266</td>\n",
              "      <td>49.021344</td>\n",
              "      <td>304.960036</td>\n",
              "      <td>26.252044</td>\n",
              "      <td>164.336058</td>\n",
              "      <td>37.678474</td>\n",
              "      <td>26.967302</td>\n",
              "      <td>44.014986</td>\n",
              "      <td>...</td>\n",
              "      <td>0.257493</td>\n",
              "      <td>0.034514</td>\n",
              "      <td>0.089918</td>\n",
              "      <td>0.024523</td>\n",
              "      <td>0.504995</td>\n",
              "      <td>0.164396</td>\n",
              "      <td>0.216167</td>\n",
              "      <td>560.193915</td>\n",
              "      <td>516.178928</td>\n",
              "      <td>0.297457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20679.438848</td>\n",
              "      <td>0.537250</td>\n",
              "      <td>0.544429</td>\n",
              "      <td>28.944211</td>\n",
              "      <td>336.135586</td>\n",
              "      <td>39.589747</td>\n",
              "      <td>216.312982</td>\n",
              "      <td>54.821185</td>\n",
              "      <td>40.926101</td>\n",
              "      <td>51.747221</td>\n",
              "      <td>...</td>\n",
              "      <td>0.437353</td>\n",
              "      <td>0.182587</td>\n",
              "      <td>0.286130</td>\n",
              "      <td>0.154702</td>\n",
              "      <td>0.500089</td>\n",
              "      <td>0.370719</td>\n",
              "      <td>0.411723</td>\n",
              "      <td>572.096830</td>\n",
              "      <td>549.962471</td>\n",
              "      <td>0.678134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1730.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>-283.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35182.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51258.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>176.500000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>342.500000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>68146.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>505.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>230.750000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>962.000000</td>\n",
              "      <td>883.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>113734.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>1493.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>259.000000</td>\n",
              "      <td>262.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2304.000000</td>\n",
              "      <td>2259.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 37 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-0a05548b-8bd6-4359-a926-381a16c59e09 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-0a05548b-8bd6-4359-a926-381a16c59e09');\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",
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              "    </script>\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-75631f2c-3d2a-4de4-9000-6f68c0301eab')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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-75631f2c-3d2a-4de4-9000-6f68c0301eab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "sns.boxplot(data=df, y='Income', palette='viridis')\n",
        "plt.title('Box Plot for Income')\n",
        "plt.ylabel('Income')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "Ckww1KfRYvxr",
        "outputId": "1af58ced-db84-4fd5-9c5e-e10a76c072d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.histplot(data=data, x='Income', bins=30, kde=True)\n",
        "plt.title('Histogram for Income')\n",
        "plt.xlabel('Income')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OFXCnFlhY5FT",
        "outputId": "7816451e-6a80-478f-f667-e937363ad247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.histplot(data=data, x='Age', bins=30, kde=True)\n",
        "plt.title('Histogram for Age')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-Rpl0uG_Y-QU",
        "outputId": "c1046bb6-f04c-473f-d61e-e7d8be65ca37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **CORRELATIONAL MATRIX**"
      ],
      "metadata": {
        "id": "z1hDMiW0g48q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols_demographics = ['Income','Age']\n",
        "cols_children = ['Kidhome', 'Teenhome']\n",
        "cols_marital = ['marital_Divorced', 'marital_Married','marital_Single', 'marital_Together', 'marital_Widow']\n",
        "cols_mnt = ['MntTotal', 'MntRegularProds','MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']\n",
        "cols_communication = ['Complain', 'Response', 'Customer_Days']\n",
        "cols_campaigns = ['AcceptedCmpOverall', 'AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5']\n",
        "cols_source_of_purchase = ['NumDealsPurchases', 'NumWebPurchases','NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth']\n",
        "cols_education = ['education_2n Cycle', 'education_Basic', 'education_Graduation', 'education_Master', 'education_PhD']"
      ],
      "metadata": {
        "id": "wK0KZ5AWZafv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#HEAT MAP.\n",
        "corr_matrix = data[['MntTotal']+cols_demographics+cols_children].corr()\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title('Correlation Matrix Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "WQyhNveSZzWu",
        "outputId": "0da09102-c7df-422a-8407-499ed6660d78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cols_marital:\n",
        "    correlation, p_value = pointbiserialr(data[col], data['MntTotal'])\n",
        "    print(f'{correlation:.4f}: Point-Biserial Correlation for {col} with p-value {p_value:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFbFIRJZZ3JF",
        "outputId": "6949de1d-50ee-4e19-85f3-325a34db9eea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0053: Point-Biserial Correlation for marital_Divorced with p-value 0.8041\n",
            "-0.0188: Point-Biserial Correlation for marital_Married with p-value 0.3767\n",
            "0.0011: Point-Biserial Correlation for marital_Single with p-value 0.9571\n",
            "0.0008: Point-Biserial Correlation for marital_Together with p-value 0.9708\n",
            "0.0370: Point-Biserial Correlation for marital_Widow with p-value 0.0826\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cols_education:\n",
        "    correlation, p_value = pointbiserialr(data[col], data['MntTotal'])\n",
        "    print(f'{correlation:.4f}: Point-Biserial Correlation for {col} with p-value {p_value:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8DzA8FzaHPW",
        "outputId": "cd565acc-00db-49d9-c806-db5dff1d8b38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.0593: Point-Biserial Correlation for education_2n Cycle with p-value 0.0054\n",
            "-0.1389: Point-Biserial Correlation for education_Basic with p-value 0.0000\n",
            "0.0159: Point-Biserial Correlation for education_Graduation with p-value 0.4551\n",
            "0.0004: Point-Biserial Correlation for education_Master with p-value 0.9842\n",
            "0.0737: Point-Biserial Correlation for education_PhD with p-value 0.0005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_marital_status(row):\n",
        "    if row['marital_Divorced'] == 1:\n",
        "        return 'Divorced'\n",
        "    elif row['marital_Married'] == 1:\n",
        "        return 'Married'\n",
        "    elif row['marital_Single'] == 1:\n",
        "        return 'Single'\n",
        "    elif row['marital_Together'] == 1:\n",
        "        return 'Together'\n",
        "    elif row['marital_Widow'] == 1:\n",
        "        return 'Widow'\n",
        "    else:\n",
        "        return 'Unknown'\n",
        "data['Marital'] = data.apply(get_marital_status, axis=1)"
      ],
      "metadata": {
        "id": "mr4LKFbraNVB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x='Marital', y='MntTotal', data=data, palette='viridis')\n",
        "plt.title('MntTotal by marital status')\n",
        "plt.xlabel('Marital status')\n",
        "plt.ylabel('MntTotal')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "4VEHWsYeaUkO",
        "outputId": "d2d79110-7da3-43ba-b408-10f923c5bc6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'MntTotal')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_relationship(row):\n",
        "    if row['marital_Married'] ==1:\n",
        "        return 1\n",
        "    elif row['marital_Together'] == 1:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "data['In_relationship'] = data.apply(get_relationship, axis=1)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "CswOF23aaXjq",
        "outputId": "7dc406db-28e7-47ed-b2da-1a6ee0603ae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Income  Kidhome  Teenhome  Recency  MntWines  MntFruits  MntMeatProducts  \\\n",
              "0  58138.0        0         0       58       635         88              546   \n",
              "1  46344.0        1         1       38        11          1                6   \n",
              "2  71613.0        0         0       26       426         49              127   \n",
              "3  26646.0        1         0       26        11          4               20   \n",
              "4  58293.0        1         0       94       173         43              118   \n",
              "\n",
              "   MntFishProducts  MntSweetProducts  MntGoldProds  ...  education_2n Cycle  \\\n",
              "0              172                88            88  ...                   0   \n",
              "1                2                 1             6  ...                   0   \n",
              "2              111                21            42  ...                   0   \n",
              "3               10                 3             5  ...                   0   \n",
              "4               46                27            15  ...                   0   \n",
              "\n",
              "   education_Basic  education_Graduation  education_Master  education_PhD  \\\n",
              "0                0                     1                 0              0   \n",
              "1                0                     1                 0              0   \n",
              "2                0                     1                 0              0   \n",
              "3                0                     1                 0              0   \n",
              "4                0                     0                 0              1   \n",
              "\n",
              "   MntTotal  MntRegularProds  AcceptedCmpOverall   Marital  In_relationship  \n",
              "0      1529             1441                   0    Single                0  \n",
              "1        21               15                   0    Single                0  \n",
              "2       734              692                   0  Together                1  \n",
              "3        48               43                   0  Together                1  \n",
              "4       407              392                   0   Married                1  \n",
              "\n",
              "[5 rows x 39 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2f1a9d94-6cd2-4e9e-83f9-c1cf83e5e422\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Income</th>\n",
              "      <th>Kidhome</th>\n",
              "      <th>Teenhome</th>\n",
              "      <th>Recency</th>\n",
              "      <th>MntWines</th>\n",
              "      <th>MntFruits</th>\n",
              "      <th>MntMeatProducts</th>\n",
              "      <th>MntFishProducts</th>\n",
              "      <th>MntSweetProducts</th>\n",
              "      <th>MntGoldProds</th>\n",
              "      <th>...</th>\n",
              "      <th>education_2n Cycle</th>\n",
              "      <th>education_Basic</th>\n",
              "      <th>education_Graduation</th>\n",
              "      <th>education_Master</th>\n",
              "      <th>education_PhD</th>\n",
              "      <th>MntTotal</th>\n",
              "      <th>MntRegularProds</th>\n",
              "      <th>AcceptedCmpOverall</th>\n",
              "      <th>Marital</th>\n",
              "      <th>In_relationship</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58138.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>635</td>\n",
              "      <td>88</td>\n",
              "      <td>546</td>\n",
              "      <td>172</td>\n",
              "      <td>88</td>\n",
              "      <td>88</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1529</td>\n",
              "      <td>1441</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>46344.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>71613.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>426</td>\n",
              "      <td>49</td>\n",
              "      <td>127</td>\n",
              "      <td>111</td>\n",
              "      <td>21</td>\n",
              "      <td>42</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>734</td>\n",
              "      <td>692</td>\n",
              "      <td>0</td>\n",
              "      <td>Together</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26646.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>Together</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>58293.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>94</td>\n",
              "      <td>173</td>\n",
              "      <td>43</td>\n",
              "      <td>118</td>\n",
              "      <td>46</td>\n",
              "      <td>27</td>\n",
              "      <td>15</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>407</td>\n",
              "      <td>392</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 39 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-2f1a9d94-6cd2-4e9e-83f9-c1cf83e5e422 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-2f1a9d94-6cd2-4e9e-83f9-c1cf83e5e422');\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",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dc307fd1-e1e2-4933-abad-d89f4d09a8bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc307fd1-e1e2-4933-abad-d89f4d09a8bc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-dc307fd1-e1e2-4933-abad-d89f4d09a8bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "cols_for_clustering = ['Income', 'MntTotal', 'In_relationship']\n",
        "data_scaled = data.copy()\n",
        "data_scaled[cols_for_clustering] = scaler.fit_transform(data[cols_for_clustering])\n",
        "data_scaled[cols_for_clustering].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cAbqklsIacHT",
        "outputId": "2cd694e3-b2c8-4e2d-f41c-db43fd9fc176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Income      MntTotal  In_relationship\n",
              "count  2.202000e+03  2.202000e+03     2.202000e+03\n",
              "mean   2.742785e-17 -8.873717e-17    -4.678869e-17\n",
              "std    1.000227e+00  1.000227e+00     1.000227e+00\n",
              "min   -2.410685e+00 -9.724232e-01    -1.348874e+00\n",
              "25%   -7.926475e-01 -8.815089e-01    -1.348874e+00\n",
              "50%   -1.508040e-02 -3.806058e-01     7.413589e-01\n",
              "75%    8.017617e-01  7.024988e-01     7.413589e-01\n",
              "max    3.006747e+00  3.048788e+00     7.413589e-01"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe0972d1-723e-48e2-b93e-0fec7c14bead\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Income</th>\n",
              "      <th>MntTotal</th>\n",
              "      <th>In_relationship</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.202000e+03</td>\n",
              "      <td>2.202000e+03</td>\n",
              "      <td>2.202000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.742785e-17</td>\n",
              "      <td>-8.873717e-17</td>\n",
              "      <td>-4.678869e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000227e+00</td>\n",
              "      <td>1.000227e+00</td>\n",
              "      <td>1.000227e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.410685e+00</td>\n",
              "      <td>-9.724232e-01</td>\n",
              "      <td>-1.348874e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.926475e-01</td>\n",
              "      <td>-8.815089e-01</td>\n",
              "      <td>-1.348874e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-1.508040e-02</td>\n",
              "      <td>-3.806058e-01</td>\n",
              "      <td>7.413589e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.017617e-01</td>\n",
              "      <td>7.024988e-01</td>\n",
              "      <td>7.413589e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.006747e+00</td>\n",
              "      <td>3.048788e+00</td>\n",
              "      <td>7.413589e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fe0972d1-723e-48e2-b93e-0fec7c14bead');\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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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0560e391-46b0-4833-827d-abea7eff2a4d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0560e391-46b0-4833-827d-abea7eff2a4d')\"\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-0560e391-46b0-4833-827d-abea7eff2a4d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_scaled[cols_for_clustering]\n",
        "inertia_list = []\n",
        "for K in range(2,10):\n",
        "    inertia = KMeans(n_clusters=K, random_state=7).fit(X).inertia_\n",
        "    inertia_list.append(inertia)"
      ],
      "metadata": {
        "id": "wtY1Lwc-ebpH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **K-MEAN CLUSTERING**"
      ],
      "metadata": {
        "id": "iMB_rrjOhZOC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "bXSXfRy6cpWL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import decomposition\n",
        "pca = decomposition.PCA(n_components = 2)\n",
        "pca_res = pca.fit_transform(data_scaled[cols_for_clustering])\n",
        "data_scaled['pc1'] = pca_res[:,0]\n",
        "data_scaled['pc2'] = pca_res[:,1]"
      ],
      "metadata": {
        "id": "HeCWu-S1eSTm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "PshOt9vgkKfg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[7,5])\n",
        "plt.plot(range(2,10), inertia_list, color=(54 / 255, 113 / 255, 130 / 255))\n",
        "plt.title(\"Inertia vs. Number of Clusters\")\n",
        "plt.xlabel(\"Number of Clusters (K)\")\n",
        "plt.ylabel(\"Inertia\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "5VRsDrY7efdP",
        "outputId": "b0b0a571-64bd-4b97-cdbc-a5dd9ae69b9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "silhouette_list = []\n",
        "for K in range(2,10):\n",
        "    model = KMeans(n_clusters = K, random_state=7)\n",
        "    clusters = model.fit_predict(X)\n",
        "    s_avg = silhouette_score(X, clusters)\n",
        "    silhouette_list.append(s_avg)\n",
        "\n",
        "plt.figure(figsize=[7,5])\n",
        "plt.plot(range(2,10), silhouette_list, color=(54 / 255, 113 / 255, 130 / 255))\n",
        "plt.title(\"Silhouette Score vs. Number of Clusters\")\n",
        "plt.xlabel(\"Number of Clusters (K)\")\n",
        "plt.ylabel(\"Silhouette Score\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "UOCarqwvek0-",
        "outputId": "bf566ba5-f5be-45a8-dce8-20364a1912ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EXPLORATION OF CLUSTERING**"
      ],
      "metadata": {
        "id": "LaovnvifhlcK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = KMeans(n_clusters=4, random_state = 7)\n",
        "model.fit(data_scaled[cols_for_clustering])\n",
        "data_scaled['Cluster'] = model.predict(data_scaled[cols_for_clustering])"
      ],
      "metadata": {
        "id": "ylqEJ52Meq6S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.scatterplot(x='pc1', y='pc2', data=data_scaled, hue='Cluster', palette='viridis')\n",
        "plt.title('Clustered Data Visualization')\n",
        "plt.xlabel('Principal Component 1 (pc1)')\n",
        "plt.ylabel('Principal Component 2 (pc2)')\n",
        "plt.legend(title='Clusters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "SHB5YOFofDSL",
        "outputId": "e142b6d8-e7fa-48bc-8624-b7cd4f4c587c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ec73130a260>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Cluster'] = data_scaled.Cluster\n",
        "data.groupby('Cluster')[cols_for_clustering].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tCWWbLyqfRVh",
        "outputId": "6ebc82aa-2856-4f28-d288-2ec670842d8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Income     MntTotal  In_relationship\n",
              "Cluster                                            \n",
              "0        71818.929329  1147.372792              1.0\n",
              "1        37332.339956   150.761589              0.0\n",
              "2        71946.155488  1159.612805              0.0\n",
              "3        37892.819883   158.463158              1.0"
            ],
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cde4b9ef-f2e6-4e1d-a7d0-a2cbd57fff29');\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-ecb7a66e-e0d1-43de-8823-b275b4879590\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ecb7a66e-e0d1-43de-8823-b275b4879590')\"\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-ecb7a66e-e0d1-43de-8823-b275b4879590 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mnt_data = data.groupby('Cluster')[cols_mnt].mean().reset_index()\n",
        "mnt_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "VljMizDCe7dA",
        "outputId": "526c779a-c34d-49ba-9aba-722e8f2dc4f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cluster     MntTotal  MntRegularProds    MntWines  MntFruits  \\\n",
              "0        0  1147.372792      1076.279152  623.261484  52.489399   \n",
              "1        1   150.761589       125.662252   85.450331   7.832230   \n",
              "2        2  1159.612805      1085.332317  613.862805  54.929878   \n",
              "3        3   158.463158       133.962573   92.046784   7.640936   \n",
              "\n",
              "   MntMeatProducts  MntFishProducts  MntSweetProducts  MntGoldProds  \n",
              "0       341.326855        75.577739         54.717314     71.093640  \n",
              "1        38.774834        10.971302          7.732892     25.099338  \n",
              "2       357.902439        77.603659         55.314024     74.280488  \n",
              "3        39.438596        11.423392          7.913450     24.500585  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-725b8229-44da-4ec8-bd9f-20627fe30fac\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cluster</th>\n",
              "      <th>MntTotal</th>\n",
              "      <th>MntRegularProds</th>\n",
              "      <th>MntWines</th>\n",
              "      <th>MntFruits</th>\n",
              "      <th>MntMeatProducts</th>\n",
              "      <th>MntFishProducts</th>\n",
              "      <th>MntSweetProducts</th>\n",
              "      <th>MntGoldProds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1147.372792</td>\n",
              "      <td>1076.279152</td>\n",
              "      <td>623.261484</td>\n",
              "      <td>52.489399</td>\n",
              "      <td>341.326855</td>\n",
              "      <td>75.577739</td>\n",
              "      <td>54.717314</td>\n",
              "      <td>71.093640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>150.761589</td>\n",
              "      <td>125.662252</td>\n",
              "      <td>85.450331</td>\n",
              "      <td>7.832230</td>\n",
              "      <td>38.774834</td>\n",
              "      <td>10.971302</td>\n",
              "      <td>7.732892</td>\n",
              "      <td>25.099338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1159.612805</td>\n",
              "      <td>1085.332317</td>\n",
              "      <td>613.862805</td>\n",
              "      <td>54.929878</td>\n",
              "      <td>357.902439</td>\n",
              "      <td>77.603659</td>\n",
              "      <td>55.314024</td>\n",
              "      <td>74.280488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>158.463158</td>\n",
              "      <td>133.962573</td>\n",
              "      <td>92.046784</td>\n",
              "      <td>7.640936</td>\n",
              "      <td>39.438596</td>\n",
              "      <td>11.423392</td>\n",
              "      <td>7.913450</td>\n",
              "      <td>24.500585</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-725b8229-44da-4ec8-bd9f-20627fe30fac')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-725b8229-44da-4ec8-bd9f-20627fe30fac 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-725b8229-44da-4ec8-bd9f-20627fe30fac');\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-0a8e81c3-27f9-46fc-b39b-bd75189dc916\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a8e81c3-27f9-46fc-b39b-bd75189dc916')\"\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-0a8e81c3-27f9-46fc-b39b-bd75189dc916 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_sizes = data.groupby('Cluster')[['MntTotal']].count().reset_index()\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.barplot(x='Cluster', y='MntTotal', data=cluster_sizes, palette = 'viridis')\n",
        "plt.title('Cluster sizes')\n",
        "plt.xlabel('Cluster')\n",
        "plt.ylabel('MntTotal')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "vtXGo2W2eyYU",
        "outputId": "3c8bf9db-9dae-4d31-d6d0-030fa8a742df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'MntTotal')"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_rows = len(data)\n",
        "cluster_sizes['Share%'] = round(cluster_sizes['MntTotal'] / total_rows*100,0)\n",
        "cluster_sizes.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "lot0tGU6e5Nl",
        "outputId": "4481bbff-b96d-4dbc-b1a2-9ede43191946"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cluster  MntTotal  Share%\n",
              "0        0       566    26.0\n",
              "1        1       453    21.0\n",
              "2        2       328    15.0\n",
              "3        3       855    39.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-42346af9-7bd4-4ee1-b794-c10e2aa8f717\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cluster</th>\n",
              "      <th>MntTotal</th>\n",
              "      <th>Share%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>566</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>453</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>328</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>855</td>\n",
              "      <td>39.0</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-42346af9-7bd4-4ee1-b794-c10e2aa8f717')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-42346af9-7bd4-4ee1-b794-c10e2aa8f717 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-42346af9-7bd4-4ee1-b794-c10e2aa8f717');\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-7bbd4a78-88ed-4191-9265-77030758476c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7bbd4a78-88ed-4191-9265-77030758476c')\"\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-7bbd4a78-88ed-4191-9265-77030758476c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#BOX PLOT\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(x='Cluster', y='Income', data=data, palette='viridis')\n",
        "plt.title('Income by cluster')\n",
        "plt.xlabel('Cluster')\n",
        "plt.ylabel('Income')\n",
        "plt.legend(title='Clusters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "hRsu_pKlfk8m",
        "outputId": "747a16f8-e7a5-4603-d4f9-405fb511d339"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ec7313418a0>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SCATTER PLOT\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.scatterplot(x='Income', y='MntTotal', data=data, hue = 'Cluster', palette='viridis')\n",
        "plt.title('Income by cluster')\n",
        "plt.xlabel('Income')\n",
        "plt.ylabel('MntTotal')\n",
        "plt.legend(title='Clusters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "h4AN4Q2ifqNK",
        "outputId": "8b6a5f0b-43a6-4b90-cede-2fa908e31ccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ec736027f10>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **RECOMMENDATION:**\n",
        "The K-means clustering analysis, considering customer spending (\"MntTotal\"), income, and relationship status, successfully identified four distinct customer segments!\n",
        "\n",
        "Here's how we determined the ideal number of clusters (k):\n",
        "\n",
        "    Elbow Method: Plotting the distortion within each cluster size (k) revealed an \"elbow\" at k=4 or 5, suggesting these segments minimize within-cluster variation.\n",
        "    Silhouette Analysis: Assessing the silhouette scores for different k values showed a peak score for k=4, confirming it as the optimal choice for distinct clusters.\n",
        "\n",
        "Therefore, this analysis effectively segmented your customers into four meaningful groups based on their spending behaviour, income, and relationship status. Now, you can delve deeper into each segment's characteristics and tailor your marketing, product offerings, and customer engagement strategies accordingly.\n",
        "\n",
        "# **strategies based on customer clusters**\n",
        "\n",
        "1. Emphasis on personalization:\n",
        "\n",
        "    Instead of: Customers from these segments will have different interests and product preferences.\n",
        "    Try: To maximize engagement and sales, personalize your marketing strategies for each segment based on their unique interests and product preferences.\n",
        "\n",
        "2. Highlight specific actions:\n",
        "\n",
        "    Instead of: Customers from these segments will have different interests and product preferences.\n",
        "    Try: Leverage the insights from each segment to tailor your marketing tactics. Offer segment-specific product recommendations, craft targeted messaging that resonates with their interests, and consider exclusive promotions or loyalty programs to further engage them.\n",
        "\n",
        "3. Focus on the benefits:\n",
        "\n",
        "    Instead of: Customers from these segments will have different interests and product preferences.\n",
        "    Try: By understanding the distinct needs and motivations of each cluster, you can develop hyper-personalized marketing campaigns that lead to increased customer satisfaction, loyalty, and ultimately, higher conversion rates."
      ],
      "metadata": {
        "id": "bjwNMFIiiLw4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oAGLO2mWfuLj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}