AI_Phase3.ipynb

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      "source": [
        "\n",
        "**IBM Naan Mudhalvan - Artificial Intelligence**\n",
        "\n",
        "**Market Basket Analysis**"
      ],
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      "source": [
        "#packages import\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import r2_score, mean_absolute_error,mean_squared_error\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.svm import SVR\n",
        "from mlxtend.frequent_patterns import apriori"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Read the Data\n",
        "dataset = pd.read_excel(\"/content/drive/MyDrive/Market_basket_analysis.xlsx\")\n",
        "dataset.head()"
      ],
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              "   BillNo                             Itemname  Quantity                Date  \\\n",
              "0  536365   WHITE HANGING HEART T-LIGHT HOLDER         6 2010-12-01 08:26:00   \n",
              "1  536365                  WHITE METAL LANTERN         6 2010-12-01 08:26:00   \n",
              "2  536365       CREAM CUPID HEARTS COAT HANGER         8 2010-12-01 08:26:00   \n",
              "3  536365  KNITTED UNION FLAG HOT WATER BOTTLE         6 2010-12-01 08:26:00   \n",
              "4  536365       RED WOOLLY HOTTIE WHITE HEART.         6 2010-12-01 08:26:00   \n",
              "\n",
              "   Price  CustomerID         Country  \n",
              "0   2.55     17850.0  United Kingdom  \n",
              "1   3.39     17850.0  United Kingdom  \n",
              "2   2.75     17850.0  United Kingdom  \n",
              "3   3.39     17850.0  United Kingdom  \n",
              "4   3.39     17850.0  United Kingdom  "
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      "source": [
        "**Data Preparation**"
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        "#Data Cleaning\n",
        "dataset['Description'] = dataset['Itemname'].str.strip()\n",
        "dataset.dropna(axis=0, subset=['BillNo'], inplace=True)\n",
        "dataset['BillNo'] = dataset['BillNo'].astype('str')\n",
        "dataset=dataset[~dataset['BillNo'].str.contains('C')]\n",
        "dataset.head()"
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              "   BillNo                             Itemname  Quantity                Date  \\\n",
              "0  536365   WHITE HANGING HEART T-LIGHT HOLDER         6 2010-12-01 08:26:00   \n",
              "1  536365                  WHITE METAL LANTERN         6 2010-12-01 08:26:00   \n",
              "2  536365       CREAM CUPID HEARTS COAT HANGER         8 2010-12-01 08:26:00   \n",
              "3  536365  KNITTED UNION FLAG HOT WATER BOTTLE         6 2010-12-01 08:26:00   \n",
              "4  536365       RED WOOLLY HOTTIE WHITE HEART.         6 2010-12-01 08:26:00   \n",
              "\n",
              "   Price  CustomerID         Country                          Description  \n",
              "0   2.55     17850.0  United Kingdom   WHITE HANGING HEART T-LIGHT HOLDER  \n",
              "1   3.39     17850.0  United Kingdom                  WHITE METAL LANTERN  \n",
              "2   2.75     17850.0  United Kingdom       CREAM CUPID HEARTS COAT HANGER  \n",
              "3   3.39     17850.0  United Kingdom  KNITTED UNION FLAG HOT WATER BOTTLE  \n",
              "4   3.39     17850.0  United Kingdom       RED WOOLLY HOTTIE WHITE HEART.  "
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              "      <td>6</td>\n",
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              "      <td>3.39</td>\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da8cd9ae-3934-4429-b11d-54cc9bb3631a 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-da8cd9ae-3934-4429-b11d-54cc9bb3631a');\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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              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Country'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "He6PIwvO7wJU",
        "outputId": "5681988b-6d87-4452-948b-16334844e3af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "United Kingdom          487622\n",
              "Germany                   9042\n",
              "France                    8408\n",
              "Spain                     2485\n",
              "Netherlands               2363\n",
              "Belgium                   2031\n",
              "Switzerland               1967\n",
              "Portugal                  1501\n",
              "Australia                 1185\n",
              "Norway                    1072\n",
              "Italy                      758\n",
              "Sweden                     451\n",
              "Unspecified                446\n",
              "Austria                    398\n",
              "Poland                     330\n",
              "Japan                      321\n",
              "Israel                     295\n",
              "Hong Kong                  284\n",
              "Singapore                  222\n",
              "Iceland                    182\n",
              "USA                        179\n",
              "Greece                     145\n",
              "Malta                      112\n",
              "United Arab Emirates        68\n",
              "RSA                         58\n",
              "Lebanon                     45\n",
              "Lithuania                   35\n",
              "Brazil                      32\n",
              "Bahrain                     18\n",
              "Saudi Arabia                 9\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjofGEAo8TnJ",
        "outputId": "e0d467dc-3805-4746-932f-90712d1a5e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(522064, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Viewing transaction basket\n",
        "mybasket = (dataset[dataset['Country'] == \"Germany\"]).groupby(['BillNo', 'Description'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('BillNo')"
      ],
      "metadata": {
        "id": "UzN5bFzI8bu9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mybasket.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "QseihFYT-TEU",
        "outputId": "3db95e0a-8684-4f72-d7d5-11650e56bc1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Description  10 COLOUR SPACEBOY PEN  12 COLOURED PARTY BALLOONS  \\\n",
              "BillNo                                                            \n",
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              "536840                          0.0                         0.0   \n",
              "536861                          0.0                         0.0   \n",
              "536967                          0.0                         0.0   \n",
              "536983                          0.0                         0.0   \n",
              "\n",
              "Description  12 IVORY ROSE PEG PLACE SETTINGS  \\\n",
              "BillNo                                          \n",
              "536527                                    0.0   \n",
              "536840                                    0.0   \n",
              "536861                                    0.0   \n",
              "536967                                    0.0   \n",
              "536983                                    0.0   \n",
              "\n",
              "Description  12 MESSAGE CARDS WITH ENVELOPES  12 PENCIL SMALL TUBE WOODLAND  \\\n",
              "BillNo                                                                        \n",
              "536527                                   0.0                            0.0   \n",
              "536840                                   0.0                            0.0   \n",
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              "536967                                   0.0                            0.0   \n",
              "536983                                   0.0                            0.0   \n",
              "\n",
              "Description  12 PENCILS SMALL TUBE RED RETROSPOT  12 PENCILS SMALL TUBE SKULL  \\\n",
              "BillNo                                                                          \n",
              "536527                                       0.0                          0.0   \n",
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              "536967                                       0.0                          0.0   \n",
              "536983                                       0.0                          0.0   \n",
              "\n",
              "Description  12 PENCILS TALL TUBE POSY  12 PENCILS TALL TUBE RED RETROSPOT  \\\n",
              "BillNo                                                                       \n",
              "536527                             0.0                                 0.0   \n",
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              "Description  12 PENCILS TALL TUBE SKULLS  ...  YULETIDE IMAGES GIFT WRAP SET  \\\n",
              "BillNo                                    ...                                  \n",
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              "\n",
              "Description  ZINC  HEART T-LIGHT HOLDER  ZINC  STAR T-LIGHT HOLDER  \\\n",
              "BillNo                                                               \n",
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              "536861                              0.0                        0.0   \n",
              "536967                              0.0                        0.0   \n",
              "536983                              0.0                        0.0   \n",
              "\n",
              "Description  ZINC BOX SIGN HOME  ZINC FOLKART SLEIGH BELLS  \\\n",
              "BillNo                                                       \n",
              "536527                      0.0                        0.0   \n",
              "536840                      0.0                        0.0   \n",
              "536861                      0.0                        0.0   \n",
              "536967                      0.0                        0.0   \n",
              "536983                      0.0                        0.0   \n",
              "\n",
              "Description  ZINC HEART LATTICE T-LIGHT HOLDER  ZINC METAL HEART DECORATION  \\\n",
              "BillNo                                                                        \n",
              "536527                                     0.0                          0.0   \n",
              "536840                                     0.0                          0.0   \n",
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              "536983                                     0.0                          0.0   \n",
              "\n",
              "Description  ZINC T-LIGHT HOLDER STAR LARGE  ZINC T-LIGHT HOLDER STARS SMALL  \\\n",
              "BillNo                                                                         \n",
              "536527                                  0.0                              0.0   \n",
              "536840                                  0.0                              0.0   \n",
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              "BillNo                                         \n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1695 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-560eeb3a-e4b0-4df8-80f2-08765aaac537')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-560eeb3a-e4b0-4df8-80f2-08765aaac537 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-560eeb3a-e4b0-4df8-80f2-08765aaac537');\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-393ea06f-87d3-4f7c-8986-8e44c392883f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-393ea06f-87d3-4f7c-8986-8e44c392883f')\"\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-393ea06f-87d3-4f7c-8986-8e44c392883f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting all +ve values to 1 & everything else to 0\n",
        "def my_encode_units(x):\n",
        "  if x <=0:\n",
        "    return 0\n",
        "  if x >=1:\n",
        "    return 1\n",
        "\n",
        "mybasket_sets = mybasket.applymap(my_encode_units)\n",
        "mybasket_sets.drop('POSTAGE', inplace=True, axis=1)"
      ],
      "metadata": {
        "id": "8_gEkvAo-oco"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training Model**"
      ],
      "metadata": {
        "id": "HkiLB0qAGEfT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Frequent itemsets\n",
        "my_frequent_items = apriori(my_basket_sets, min_support=0.07, use_colnames=True)"
      ],
      "metadata": {
        "id": "iJpX0-60_mIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#generating rules\n",
        "from mlxtend.frequent_patterns.association_rules import association_rules\n",
        "my_rules = association_rules(my_frequent_items, metric='lift', min_threshold=1)"
      ],
      "metadata": {
        "id": "H2cCc8sAAMmq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#viewing top 100 rules\n",
        "my_rules.head(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4eA290pZAkbI",
        "outputId": "119fdd73-8cf6-48ca-bcf9-d5e2820206ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          antecedents  \\\n",
              "0                            (6 RIBBONS RUSTIC CHARM)   \n",
              "1                                           (POSTAGE)   \n",
              "2                        (JUMBO BAG WOODLAND ANIMALS)   \n",
              "3                                           (POSTAGE)   \n",
              "4                     (PLASTERS IN TIN CIRCUS PARADE)   \n",
              "5                                           (POSTAGE)   \n",
              "6                          (PLASTERS IN TIN SPACEBOY)   \n",
              "7                                           (POSTAGE)   \n",
              "8                  (PLASTERS IN TIN WOODLAND ANIMALS)   \n",
              "9                                           (POSTAGE)   \n",
              "10                 (PLASTERS IN TIN WOODLAND ANIMALS)   \n",
              "11               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "12                    (RED TOADSTOOL LED NIGHT LIGHT)   \n",
              "13                                          (POSTAGE)   \n",
              "14                         (REGENCY CAKESTAND 3 TIER)   \n",
              "15                                          (POSTAGE)   \n",
              "16                (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
              "17                                          (POSTAGE)   \n",
              "18               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "19                                          (POSTAGE)   \n",
              "20                               (SPACEBOY LUNCH BOX)   \n",
              "21                                          (POSTAGE)   \n",
              "22                           (WOODLAND CHARLOTTE BAG)   \n",
              "23                                          (POSTAGE)   \n",
              "24               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "25                (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
              "26               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "27                               (SPACEBOY LUNCH BOX)   \n",
              "28  (ROUND SNACK BOXES SET OF4 WOODLAND, ROUND SNA...   \n",
              "29      (ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)   \n",
              "30       (ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)   \n",
              "31               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "32                (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
              "33                                          (POSTAGE)   \n",
              "\n",
              "                                          consequents  antecedent support  \\\n",
              "0                                           (POSTAGE)            0.102845   \n",
              "1                            (6 RIBBONS RUSTIC CHARM)            0.818381   \n",
              "2                                           (POSTAGE)            0.100656   \n",
              "3                        (JUMBO BAG WOODLAND ANIMALS)            0.818381   \n",
              "4                                           (POSTAGE)            0.115974   \n",
              "5                     (PLASTERS IN TIN CIRCUS PARADE)            0.818381   \n",
              "6                                           (POSTAGE)            0.107221   \n",
              "7                          (PLASTERS IN TIN SPACEBOY)            0.818381   \n",
              "8                                           (POSTAGE)            0.137856   \n",
              "9                  (PLASTERS IN TIN WOODLAND ANIMALS)            0.818381   \n",
              "10               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.137856   \n",
              "11                 (PLASTERS IN TIN WOODLAND ANIMALS)            0.245077   \n",
              "12                                          (POSTAGE)            0.096280   \n",
              "13                    (RED TOADSTOOL LED NIGHT LIGHT)            0.818381   \n",
              "14                                          (POSTAGE)            0.137856   \n",
              "15                         (REGENCY CAKESTAND 3 TIER)            0.818381   \n",
              "16                                          (POSTAGE)            0.157549   \n",
              "17                (ROUND SNACK BOXES SET OF 4 FRUITS)            0.818381   \n",
              "18                                          (POSTAGE)            0.245077   \n",
              "19               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.818381   \n",
              "20                                          (POSTAGE)            0.102845   \n",
              "21                               (SPACEBOY LUNCH BOX)            0.818381   \n",
              "22                                          (POSTAGE)            0.126915   \n",
              "23                           (WOODLAND CHARLOTTE BAG)            0.818381   \n",
              "24                (ROUND SNACK BOXES SET OF 4 FRUITS)            0.245077   \n",
              "25               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.157549   \n",
              "26                               (SPACEBOY LUNCH BOX)            0.245077   \n",
              "27               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.102845   \n",
              "28                                          (POSTAGE)            0.131291   \n",
              "29                (ROUND SNACK BOXES SET OF 4 FRUITS)            0.225383   \n",
              "30               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.150985   \n",
              "31       (ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)            0.245077   \n",
              "32      (ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)            0.157549   \n",
              "33  (ROUND SNACK BOXES SET OF4 WOODLAND, ROUND SNA...            0.818381   \n",
              "\n",
              "    consequent support   support  confidence      lift  leverage  conviction  \\\n",
              "0             0.818381  0.091904    0.893617  1.091933  0.007738    1.707221   \n",
              "1             0.102845  0.091904    0.112299  1.091933  0.007738    1.010651   \n",
              "2             0.818381  0.087527    0.869565  1.062544  0.005152    1.392414   \n",
              "3             0.100656  0.087527    0.106952  1.062544  0.005152    1.007049   \n",
              "4             0.818381  0.100656    0.867925  1.060539  0.005746    1.375117   \n",
              "5             0.115974  0.100656    0.122995  1.060539  0.005746    1.008006   \n",
              "6             0.818381  0.100656    0.938776  1.147113  0.012909    2.966448   \n",
              "7             0.107221  0.100656    0.122995  1.147113  0.012909    1.017986   \n",
              "8             0.818381  0.118162    0.857143  1.047364  0.005344    1.271335   \n",
              "9             0.137856  0.118162    0.144385  1.047364  0.005344    1.007631   \n",
              "10            0.245077  0.074398    0.539683  2.202098  0.040613    1.640006   \n",
              "11            0.137856  0.074398    0.303571  2.202098  0.040613    1.237951   \n",
              "12            0.818381  0.080963    0.840909  1.027528  0.002169    1.141607   \n",
              "13            0.096280  0.080963    0.098930  1.027528  0.002169    1.002941   \n",
              "14            0.818381  0.120350    0.873016  1.066760  0.007532    1.430252   \n",
              "15            0.137856  0.120350    0.147059  1.066760  0.007532    1.010790   \n",
              "16            0.818381  0.150985    0.958333  1.171012  0.022049    4.358862   \n",
              "17            0.157549  0.150985    0.184492  1.171012  0.022049    1.033038   \n",
              "18            0.818381  0.225383    0.919643  1.123735  0.024817    2.260151   \n",
              "19            0.245077  0.225383    0.275401  1.123735  0.024817    1.041850   \n",
              "20            0.818381  0.091904    0.893617  1.091933  0.007738    1.707221   \n",
              "21            0.102845  0.091904    0.112299  1.091933  0.007738    1.010651   \n",
              "22            0.818381  0.115974    0.913793  1.116587  0.012109    2.106783   \n",
              "23            0.126915  0.115974    0.141711  1.116587  0.012109    1.017240   \n",
              "24            0.157549  0.131291    0.535714  3.400298  0.092679    1.814509   \n",
              "25            0.245077  0.131291    0.833333  3.400298  0.092679    4.529540   \n",
              "26            0.102845  0.070022    0.285714  2.778116  0.044817    1.256018   \n",
              "27            0.245077  0.070022    0.680851  2.778116  0.044817    2.365427   \n",
              "28            0.818381  0.124726    0.950000  1.160829  0.017280    3.632385   \n",
              "29            0.157549  0.124726    0.553398  3.512540  0.089218    1.886357   \n",
              "30            0.245077  0.124726    0.826087  3.370730  0.087724    4.340810   \n",
              "31            0.150985  0.124726    0.508929  3.370730  0.087724    1.728904   \n",
              "32            0.225383  0.124726    0.791667  3.512540  0.089218    3.718162   \n",
              "33            0.131291  0.124726    0.152406  1.160829  0.017280    1.024912   \n",
              "\n",
              "    zhangs_metric  \n",
              "0        0.093844  \n",
              "1        0.463569  \n",
              "2        0.065450  \n",
              "3        0.324096  \n",
              "4        0.064572  \n",
              "5        0.314301  \n",
              "6        0.143649  \n",
              "7        0.706129  \n",
              "8        0.052453  \n",
              "9        0.248996  \n",
              "10       0.633174  \n",
              "11       0.723103  \n",
              "12       0.029645  \n",
              "13       0.147509  \n",
              "14       0.072589  \n",
              "15       0.344578  \n",
              "16       0.173348  \n",
              "17       0.804086  \n",
              "18       0.145856  \n",
              "19       0.606270  \n",
              "20       0.093844  \n",
              "21       0.463569  \n",
              "22       0.119591  \n",
              "23       0.574903  \n",
              "24       0.935072  \n",
              "25       0.837922  \n",
              "26       0.847826  \n",
              "27       0.713415  \n",
              "28       0.159486  \n",
              "29       0.923431  \n",
              "30       0.828405  \n",
              "31       0.931655  \n",
              "32       0.849077  \n",
              "33       0.762841  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-04259645-abaa-42bb-aec9-1a2f09c17b07\" 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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "      <th>zhangs_metric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(6 RIBBONS RUSTIC CHARM)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.091904</td>\n",
              "      <td>0.893617</td>\n",
              "      <td>1.091933</td>\n",
              "      <td>0.007738</td>\n",
              "      <td>1.707221</td>\n",
              "      <td>0.093844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(6 RIBBONS RUSTIC CHARM)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.091904</td>\n",
              "      <td>0.112299</td>\n",
              "      <td>1.091933</td>\n",
              "      <td>0.007738</td>\n",
              "      <td>1.010651</td>\n",
              "      <td>0.463569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(JUMBO BAG WOODLAND ANIMALS)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.087527</td>\n",
              "      <td>0.869565</td>\n",
              "      <td>1.062544</td>\n",
              "      <td>0.005152</td>\n",
              "      <td>1.392414</td>\n",
              "      <td>0.065450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(JUMBO BAG WOODLAND ANIMALS)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.087527</td>\n",
              "      <td>0.106952</td>\n",
              "      <td>1.062544</td>\n",
              "      <td>0.005152</td>\n",
              "      <td>1.007049</td>\n",
              "      <td>0.324096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(PLASTERS IN TIN CIRCUS PARADE)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.115974</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.867925</td>\n",
              "      <td>1.060539</td>\n",
              "      <td>0.005746</td>\n",
              "      <td>1.375117</td>\n",
              "      <td>0.064572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(PLASTERS IN TIN CIRCUS PARADE)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.115974</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.122995</td>\n",
              "      <td>1.060539</td>\n",
              "      <td>0.005746</td>\n",
              "      <td>1.008006</td>\n",
              "      <td>0.314301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(PLASTERS IN TIN SPACEBOY)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.938776</td>\n",
              "      <td>1.147113</td>\n",
              "      <td>0.012909</td>\n",
              "      <td>2.966448</td>\n",
              "      <td>0.143649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(PLASTERS IN TIN SPACEBOY)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.122995</td>\n",
              "      <td>1.147113</td>\n",
              "      <td>0.012909</td>\n",
              "      <td>1.017986</td>\n",
              "      <td>0.706129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.118162</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>1.047364</td>\n",
              "      <td>0.005344</td>\n",
              "      <td>1.271335</td>\n",
              "      <td>0.052453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.118162</td>\n",
              "      <td>0.144385</td>\n",
              "      <td>1.047364</td>\n",
              "      <td>0.005344</td>\n",
              "      <td>1.007631</td>\n",
              "      <td>0.248996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.539683</td>\n",
              "      <td>2.202098</td>\n",
              "      <td>0.040613</td>\n",
              "      <td>1.640006</td>\n",
              "      <td>0.633174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.303571</td>\n",
              "      <td>2.202098</td>\n",
              "      <td>0.040613</td>\n",
              "      <td>1.237951</td>\n",
              "      <td>0.723103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(RED TOADSTOOL LED NIGHT LIGHT)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.096280</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.080963</td>\n",
              "      <td>0.840909</td>\n",
              "      <td>1.027528</td>\n",
              "      <td>0.002169</td>\n",
              "      <td>1.141607</td>\n",
              "      <td>0.029645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(RED TOADSTOOL LED NIGHT LIGHT)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.096280</td>\n",
              "      <td>0.080963</td>\n",
              "      <td>0.098930</td>\n",
              "      <td>1.027528</td>\n",
              "      <td>0.002169</td>\n",
              "      <td>1.002941</td>\n",
              "      <td>0.147509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.120350</td>\n",
              "      <td>0.873016</td>\n",
              "      <td>1.066760</td>\n",
              "      <td>0.007532</td>\n",
              "      <td>1.430252</td>\n",
              "      <td>0.072589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.120350</td>\n",
              "      <td>0.147059</td>\n",
              "      <td>1.066760</td>\n",
              "      <td>0.007532</td>\n",
              "      <td>1.010790</td>\n",
              "      <td>0.344578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>1.171012</td>\n",
              "      <td>0.022049</td>\n",
              "      <td>4.358862</td>\n",
              "      <td>0.173348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.184492</td>\n",
              "      <td>1.171012</td>\n",
              "      <td>0.022049</td>\n",
              "      <td>1.033038</td>\n",
              "      <td>0.804086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.225383</td>\n",
              "      <td>0.919643</td>\n",
              "      <td>1.123735</td>\n",
              "      <td>0.024817</td>\n",
              "      <td>2.260151</td>\n",
              "      <td>0.145856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.225383</td>\n",
              "      <td>0.275401</td>\n",
              "      <td>1.123735</td>\n",
              "      <td>0.024817</td>\n",
              "      <td>1.041850</td>\n",
              "      <td>0.606270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>(SPACEBOY LUNCH BOX)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.091904</td>\n",
              "      <td>0.893617</td>\n",
              "      <td>1.091933</td>\n",
              "      <td>0.007738</td>\n",
              "      <td>1.707221</td>\n",
              "      <td>0.093844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(SPACEBOY LUNCH BOX)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.091904</td>\n",
              "      <td>0.112299</td>\n",
              "      <td>1.091933</td>\n",
              "      <td>0.007738</td>\n",
              "      <td>1.010651</td>\n",
              "      <td>0.463569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>(WOODLAND CHARLOTTE BAG)</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.126915</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.115974</td>\n",
              "      <td>0.913793</td>\n",
              "      <td>1.116587</td>\n",
              "      <td>0.012109</td>\n",
              "      <td>2.106783</td>\n",
              "      <td>0.119591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(WOODLAND CHARLOTTE BAG)</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.126915</td>\n",
              "      <td>0.115974</td>\n",
              "      <td>0.141711</td>\n",
              "      <td>1.116587</td>\n",
              "      <td>0.012109</td>\n",
              "      <td>1.017240</td>\n",
              "      <td>0.574903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>1.814509</td>\n",
              "      <td>0.935072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>4.529540</td>\n",
              "      <td>0.837922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(SPACEBOY LUNCH BOX)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.070022</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>2.778116</td>\n",
              "      <td>0.044817</td>\n",
              "      <td>1.256018</td>\n",
              "      <td>0.847826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>(SPACEBOY LUNCH BOX)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.070022</td>\n",
              "      <td>0.680851</td>\n",
              "      <td>2.778116</td>\n",
              "      <td>0.044817</td>\n",
              "      <td>2.365427</td>\n",
              "      <td>0.713415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND, ROUND SNA...</td>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.950000</td>\n",
              "      <td>1.160829</td>\n",
              "      <td>0.017280</td>\n",
              "      <td>3.632385</td>\n",
              "      <td>0.159486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>0.225383</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.553398</td>\n",
              "      <td>3.512540</td>\n",
              "      <td>0.089218</td>\n",
              "      <td>1.886357</td>\n",
              "      <td>0.923431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.826087</td>\n",
              "      <td>3.370730</td>\n",
              "      <td>0.087724</td>\n",
              "      <td>4.340810</td>\n",
              "      <td>0.828405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.508929</td>\n",
              "      <td>3.370730</td>\n",
              "      <td>0.087724</td>\n",
              "      <td>1.728904</td>\n",
              "      <td>0.931655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.225383</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>3.512540</td>\n",
              "      <td>0.089218</td>\n",
              "      <td>3.718162</td>\n",
              "      <td>0.849077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>(POSTAGE)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND, ROUND SNA...</td>\n",
              "      <td>0.818381</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.152406</td>\n",
              "      <td>1.160829</td>\n",
              "      <td>0.017280</td>\n",
              "      <td>1.024912</td>\n",
              "      <td>0.762841</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-04259645-abaa-42bb-aec9-1a2f09c17b07')\"\n",
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              "    </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",
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              "      border-radius: 50%;\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",
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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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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-04259645-abaa-42bb-aec9-1a2f09c17b07 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-04259645-abaa-42bb-aec9-1a2f09c17b07');\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-f3ff36da-43ae-49af-a104-5070ac8faed5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3ff36da-43ae-49af-a104-5070ac8faed5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      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-f3ff36da-43ae-49af-a104-5070ac8faed5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Marketing Recommendations**"
      ],
      "metadata": {
        "id": "QwlNjH_IGicO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_basket_sets['ROUND SNACK BOXES SET OF4 WOODLAND'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dic9JxrwA4Ag",
        "outputId": "9fea3bc1-f885-42d5-f9d0-af1803af31bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "112"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_basket_sets['SPACEBOY LUNCH BOX'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgvlU7ZrBna4",
        "outputId": "c100acc6-8fd0-43c8-b973-89bf53dc300d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#condition based rules filteration\n",
        "my_rules[(my_rules['lift'] >= 3) & (my_rules['confidence'] >= 0.3)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "Xfez7scUBx_R",
        "outputId": "cf7f53d1-2ae5-4bf4-f990-aaeb9289d5aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      antecedents  \\\n",
              "24           (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "25            (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
              "29  (ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)   \n",
              "30   (ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)   \n",
              "31           (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "32            (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
              "\n",
              "                                      consequents  antecedent support  \\\n",
              "24            (ROUND SNACK BOXES SET OF 4 FRUITS)            0.245077   \n",
              "25           (ROUND SNACK BOXES SET OF4 WOODLAND)            0.157549   \n",
              "29            (ROUND SNACK BOXES SET OF 4 FRUITS)            0.225383   \n",
              "30           (ROUND SNACK BOXES SET OF4 WOODLAND)            0.150985   \n",
              "31   (ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)            0.245077   \n",
              "32  (ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)            0.157549   \n",
              "\n",
              "    consequent support   support  confidence      lift  leverage  conviction  \\\n",
              "24            0.157549  0.131291    0.535714  3.400298  0.092679    1.814509   \n",
              "25            0.245077  0.131291    0.833333  3.400298  0.092679    4.529540   \n",
              "29            0.157549  0.124726    0.553398  3.512540  0.089218    1.886357   \n",
              "30            0.245077  0.124726    0.826087  3.370730  0.087724    4.340810   \n",
              "31            0.150985  0.124726    0.508929  3.370730  0.087724    1.728904   \n",
              "32            0.225383  0.124726    0.791667  3.512540  0.089218    3.718162   \n",
              "\n",
              "    zhangs_metric  \n",
              "24       0.935072  \n",
              "25       0.837922  \n",
              "29       0.923431  \n",
              "30       0.828405  \n",
              "31       0.931655  \n",
              "32       0.849077  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-78a40f25-b41c-4a39-bc4e-5ef158d8d8c3\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "      <th>zhangs_metric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>1.814509</td>\n",
              "      <td>0.935072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>4.529540</td>\n",
              "      <td>0.837922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>0.225383</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.553398</td>\n",
              "      <td>3.512540</td>\n",
              "      <td>0.089218</td>\n",
              "      <td>1.886357</td>\n",
              "      <td>0.923431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.826087</td>\n",
              "      <td>3.370730</td>\n",
              "      <td>0.087724</td>\n",
              "      <td>4.340810</td>\n",
              "      <td>0.828405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS, POSTAGE)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.508929</td>\n",
              "      <td>3.370730</td>\n",
              "      <td>0.087724</td>\n",
              "      <td>1.728904</td>\n",
              "      <td>0.931655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND, POSTAGE)</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.225383</td>\n",
              "      <td>0.124726</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>3.512540</td>\n",
              "      <td>0.089218</td>\n",
              "      <td>3.718162</td>\n",
              "      <td>0.849077</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
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              "\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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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-78a40f25-b41c-4a39-bc4e-5ef158d8d8c3 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-78a40f25-b41c-4a39-bc4e-5ef158d8d8c3');\n",
              "        const dataTable =\n",
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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-ea9bf3f1-533c-49a5-a491-cb333d3d08a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea9bf3f1-533c-49a5-a491-cb333d3d08a0')\"\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-ea9bf3f1-533c-49a5-a491-cb333d3d08a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualization of Dataset**"
      ],
      "metadata": {
        "id": "gDg_5dWEG4ML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(dataset, x='Price', y='Quantity', bins=50, color='y')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ihs-b-JhHATx",
        "outputId": "6ca2bd60-4c7c-4922-cdfa-02c861472a4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price', ylabel='Quantity'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(dataset, x='Price',  palette='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "varDtftMH7eS",
        "outputId": "bd0c184a-3ea9-4c03-f6fe-fd89c389f6e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAGwCAYAAADMjZ3mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZiElEQVR4nO3de5CVdf3A8c9ZcJdF9kJeuCheSAFTSS0jnNSaKFS0mzMxjmVUU5KiNTpWTCXZaNhl7GKmNY34hzM6Opk2gTaE2g0yY0BDyPstBbQUdmkMxP3+/mg4P46fXViXhb34es2c8XCe73Oe7/N4nmffnD27VEopJQAAtlPX1xMAAPofgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACAZGhPV+zo6Ijnn38+mpqaolKp9OacAIDdpJQS7e3tMXbs2Kir6/p9gh4HwvPPPx/jxo3r6eoAQB969tln48ADD+xyeY8DoampqbqB5ubmnj4NALAHtbW1xbhx46pfx7vS40DY9m2F5uZmgQAAA8zOPh7gQ4oAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgGdrXEwDeuFJKbNmyJSIi6uvro1Kp9PGMgMHGOwgwAG3ZsiVmz54ds2fProYCQG8SCDAAbd68udP7AL1FIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQYADq6Ojo9D5AbxEIMAD95z//6fQ+QG8RCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACRD+3oCr3fbbbfFwoULY8aMGfGxj32sr6cD/d7Xv/713fr8Z5xxRowfPz5+/vOfxyuvvBIRESNHjoytW7dGRMSkSZNi+fLlMWPGjFi3bl3cf//9UVdXF6WUGDZsWEybNi2WLl0aJ5xwQvW/9957b0REvPe9742lS5fGJz7xiTjmmGM63f7KlSvjxhtv3OGYrmy/7hNPPFG9towfP77mOXdlG4PJYD0OA3G/+sOcK6WU0pMV29raoqWlJTZu3BjNzc29Mpn29vb44he/GKWUqFQq8aMf/Siampp65blhMPn0pz+9R7fX1NQU7e3tOxxTqVRiR5eTbcu3H7ft/siRI2P+/PnR0NBQs87mzZtj7ty58fLLL3c5pivbr9vS0hJtbW3V7Tc3N8fGjRtj5MiRcdlll8W8efN6tI3BZFeOdX82EPdrd8+5u1+/+9W3GK6++urqhaOUEj/5yU/6eEZAROw0DiJih3Gw/fLtx227v2HDhli0aFFaZ+HChbFhw4YdjunK9utu3LixZvsbN26sPudPfvKTHm9jMNmVY92fDcT96i9z7jeB8NBDD8Wjjz5a89gjjzwSDz30UB/NCPqnPf3uwZ5QSomFCxfG+vXrq4+tX78+Fi1aVPOF/fVjuvL6dXe03UceeaRH2xhMduVY92cDcb/605y7HQibN2+Otra2mltv6ejoiOuuu67TZdddd110dHT02rZgIBuMcbBNKSVuvPHGKKXU3O9qTHeeZ1fn8WawK8e6PxuI+9Xf5tztQJg/f360tLRUb+PGjeu1STz44IOxadOmTpdt2rQpHnzwwV7bFtA/dXR0xKpVq2Lt2rWxdu3aWLVqVfrLwfZjutLVuj2Zx5vBrhzr/mwg7ld/m3O3A2Hu3LmxcePG6u3ZZ5/ttUlMnjw5RowY0emyESNGxOTJk3ttW0D/VFdXF0cddVSMGTMmxowZE0cddVTU1dV1OaYrXa3bk3m8GezKse7PBuJ+9bc5d/sMamhoiObm5ppbr02iri5mz57d6bLzzjuvxyc6DDYLFizo6ynsNpVKJT75yU9GpVKJSqUSn/jEJ6JSqXQ5ZkfP09m6PZnHm8GuHOv+bCDuV3+bc7/5ynvkkUfG4YcfXvPYhAkT4ogjjuijGUH/NBgjoVKpxIwZM2L//fevPjZq1Kg47bTTqhfFzsZ05fXr7mi7EyZM6NE2BpNdOdb92UDcr/40534TCBERF1xwQc1BmTNnTh/PCIiIbv0+ku58MX79uG33W1tb47TTTkvrzJgxI1pbW3c4pivbr9vS0lKz/ZaWlupzzpkzp8fbGEx25Vj3ZwNxv/rLnPtVIDQ1NcXpp58edXV1cfrpp/slSdCFyy+/fI9t64wzzojPfOYz0djYWH1s5MiR0dTUFE1NTXH88cdXz9njjz8+Iv73bcNKpRKNjY1xxhlnxD777BOnn3569b/b1t322DnnnNPpL4JpaGiIc845Z4djurL9urNmzaq5tsyaNav6nE1NTT3exmCyK8e6PxuI+9Vf5tyvfpMi0D3PPfdc9VcsX3755XHAAQf08YyAgWJA/iZFAKB/EAgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQCIQAIBEIAAAiUCAAWjvvffu9D5AbxEIMADV1dV1eh+gt7iyAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkAgEACARCABAIhBgAGpoaOj0PkBvGdrXEwDeuPr6+rjuuuuq9wF6m0CAAahSqXjnANitfIsBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEiG9nTFUkpERLS1tfXaZACA3Wvb1+1tX8e70uNAaG9vj4iIcePG9fQpAIA+0t7eHi0tLV0ur5SdJUQXOjo64vnnn4+mpqaoVCo9nuDu1tbWFuPGjYtnn302mpub+3o6g57jvec41nuW473nONa7Vykl2tvbY+zYsVFX1/UnDXr8DkJdXV0ceOCBPV19j2tubvZC24Mc7z3Hsd6zHO89x7HefXb0zsE2PqQIACQCAQBIBn0gNDQ0xLx586KhoaGvp/Km4HjvOY71nuV47zmOdf/Q4w8pAgCD16B/BwEAeOMEAgCQCAQAIBEIAEAyYALhiiuuiBNOOCGGDx8era2tnY555plnYsaMGTF8+PDYf//945JLLomtW7fWjLn33nvjuOOOi4aGhjjssMPihhtuSM9zzTXXxCGHHBLDhg2LKVOmxF//+tea5f/973/j/PPPj3322SdGjBgRZ555Zqxfv763drVfOuSQQ6JSqdTcrrzyypoxDz74YJx44okxbNiwGDduXHz3u99Nz3PrrbfGpEmTYtiwYXH00UfHokWLapaXUuLSSy+NMWPGRGNjY0ybNi0effTR3bpvA9XOXqfU+uY3v5lew5MmTaou78553VvXmMHoD3/4Q5xxxhkxduzYqFQqcfvtt9cs7865/dJLL8XZZ58dzc3N0draGp/97Gdj06ZNNWN64zpDN5UB4tJLLy1XXXVVueiii0pLS0tavnXr1nLUUUeVadOmlRUrVpRFixaVfffdt8ydO7c65oknnijDhw8vF110UVm9enW5+uqry5AhQ8pdd91VHXPzzTeX+vr6cv3115eHHnqofO5znyutra1l/fr11TGzZ88u48aNK0uWLCl/+9vfyrvf/e5ywgkn7Nb972sHH3xw+da3vlXWrl1bvW3atKm6fOPGjWXUqFHl7LPPLqtWrSo33XRTaWxsLD/72c+qY/785z+XIUOGlO9+97tl9erV5etf/3rZa6+9yt///vfqmCuvvLK0tLSU22+/vTzwwAPlQx/6UDn00EPLK6+8skf3t7/rzuuUWvPmzStHHnlkzWv4xRdfrC7f2XndW9eYwWrRokXla1/7WrnttttKRJRf/epXNcu7c26fcsop5e1vf3v5y1/+Uv74xz+Www47rJx11lnV5b11naF7BkwgbLNgwYJOA2HRokWlrq6urFu3rvrYtddeW5qbm8vmzZtLKaV8+ctfLkceeWTNejNnzizTp0+v/vld73pXOf/886t/fu2118rYsWPL/PnzSymlbNiwoey1117l1ltvrY5Zs2ZNiYiybNmyXtnH/ujggw8uP/jBD7pc/tOf/rSMHDmyeqxLKeUrX/lKmThxYvXPH//4x8uMGTNq1psyZUo599xzSymldHR0lNGjR5fvfe971eUbNmwoDQ0N5aabbuqlPRkcdvY6JZs3b155+9vf3umy7pzXvXWNeTN4fSB059xevXp1iYhy//33V8fceeedpVKplOeee66U0jvXGbpvwHyLYWeWLVsWRx99dIwaNar62PTp06OtrS0eeuih6php06bVrDd9+vRYtmxZRERs2bIlli9fXjOmrq4upk2bVh2zfPnyePXVV2vGTJo0KQ466KDqmMHqyiuvjH322SeOPfbY+N73vlfz1uqyZcvipJNOivr6+upj06dPj4cffjhefvnl6pgdHf8nn3wy1q1bVzOmpaUlpkyZMuiP7RvRndcpnXv00Udj7NixMX78+Dj77LPjmWeeiYjunde9cY15s+rOub1s2bJobW2Nd77zndUx06ZNi7q6urjvvvuqY3b1OkP39fgfa+pv1q1bV3PiRkT1z+vWrdvhmLa2tnjllVfi5Zdfjtdee63TMf/4xz+qz1FfX58+BzFq1KjqdgajCy+8MI477rh4y1veEkuXLo25c+fG2rVr46qrroqI/x2XQw89tGad7Y//yJEjuzz+2///2X69zsYQ8a9//Wunr1OyKVOmxA033BATJ06MtWvXxmWXXRYnnnhirFq1qlvndW9cYxobG3fT3vVv3Tm3161bF/vvv3/N8qFDh8Zb3vKWmjG7ep2h+/o0EL761a/Gd77znR2OWbNmTc0Hieg9b+T4X3TRRdXHJk+eHPX19XHuuefG/Pnz/TpUBoRTTz21en/y5MkxZcqUOPjgg+OWW255037hhh3p00C4+OKLY9asWTscM378+G491+jRo9OnuLd9Ann06NHV/77+U8nr16+P5ubmaGxsjCFDhsSQIUM6HbP9c2zZsiU2bNhQ87eN7ccMFLty/KdMmRJbt26Np556KiZOnNjlsY3Y+fHffvm2x8aMGVMz5phjjun2fg12++67705fp+xca2trTJgwIR577LH4wAc+sNPzujeuMW9W3Tm3R48eHS+88ELNelu3bo2XXnppp8d3+23s7DpD9/XpZxD222+/mDRp0g5v23+vaUemTp0af//732teYIsXL47m5uZ429veVh2zZMmSmvUWL14cU6dOjYiI+vr6eMc73lEzpqOjI5YsWVId8453vCP22muvmjEPP/xwPPPMM9UxA8WuHP+VK1dGXV1d9S3BqVOnxh/+8Id49dVXq2MWL14cEydOjJEjR1bH7Oj4H3rooTF69OiaMW1tbXHfffcNuGO7O3XndcrObdq0KR5//PEYM2ZMt87r3rjGvFl159yeOnVqbNiwIZYvX14dc/fdd0dHR0dMmTKlOmZXrzO8AX39Kcnuevrpp8uKFSvKZZddVkaMGFFWrFhRVqxYUdrb20sp//8jSB/84AfLypUry1133VX222+/Tn8E6ZJLLilr1qwp11xzTac/5tjQ0FBuuOGGsnr16vL5z3++tLa21nxyefbs2eWggw4qd999d/nb3/5Wpk6dWqZOnbrnDsYetnTp0vKDH/ygrFy5sjz++OPlxhtvLPvtt18555xzqmM2bNhQRo0aVT75yU+WVatWlZtvvrkMHz48/fjR0KFDy/e///2yZs2aMm/evE5/zLG1tbXccccd5cEHHywf/vCH/ZhjJ7rzOqXWxRdfXO69997y5JNPlj//+c9l2rRpZd999y0vvPBCKWXn53VvXWMGq/b29up1OSLKVVddVVasWFGefvrpUkr3zu1TTjmlHHvsseW+++4rf/rTn8rhhx9e82OOvXWdoXsGTCB86lOfKhGRbvfcc091zFNPPVVOPfXU0tjYWPbdd99y8cUXl1dffbXmee65555yzDHHlPr6+jJ+/PiyYMGCtK2rr766HHTQQaW+vr68613vKn/5y19qlr/yyivlvPPOKyNHjizDhw8vH/3oR8vatWt3x273C8uXLy9TpkwpLS0tZdiwYeWII44o3/72t8t///vfmnEPPPBAec973lMaGhrKAQccUK688sr0XLfcckuZMGFCqa+vL0ceeWRZuHBhzfKOjo7yjW98o4waNao0NDSU97///eXhhx/erfs3UO3sdUqtmTNnljFjxpT6+vpywAEHlJkzZ5bHHnusurw753VvXWMGo3vuuafTa/SnPvWpUkr3zu1///vf5ayzziojRowozc3N5dOf/nT1L4Hb9MZ1hu7xzz0DAMmg+T0IAEDvEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBKDqkEMOiR/+8Id9PQ2gHxAIMEjNmjUrKpVKVCqVqK+vj8MOOyy+9a1vxdatW7tc5/7774/Pf/7ze3CWQH/Vp//cM7B7nXLKKbFgwYLYvHlzLFq0KM4///zYa6+9Yu7cuTXjtmzZEvX19bHffvv10UyB/sY7CDCINTQ0xOjRo+Pggw+OL3zhCzFt2rT49a9/HbNmzYqPfOQjccUVV8TYsWNj4sSJEZG/xbBhw4Y499xzY9SoUTFs2LA46qij4je/+U11+Z/+9Kc48cQTo7GxMcaNGxcXXnhh/Oc//9nTuwnsBt5BgDeRxsbG+Pe//x0REUuWLInm5uZYvHhxp2M7Ojri1FNPjfb29rjxxhvjrW99a6xevTqGDBkSERGPP/54nHLKKXH55ZfH9ddfHy+++GLMmTMn5syZEwsWLNhj+wTsHgIB3gRKKbFkyZL47W9/GxdccEG8+OKLsffee8cvfvGLqK+v73Sd3/3ud/HXv/411qxZExMmTIiIiPHjx1eXz58/P84+++z40pe+FBERhx9+ePz4xz+Ok08+Oa699toYNmzYbt8vYPfxLQYYxH7zm9/EiBEjYtiwYXHqqafGzJkz45vf/GZERBx99NFdxkFExMqVK+PAAw+sxsHrPfDAA3HDDTfEiBEjqrfp06dHR0dHPPnkk7tjd4A9yDsIMIi9733vi2uvvTbq6+tj7NixMXTo/5/ye++99w7XbWxs3OHyTZs2xbnnnhsXXnhhWnbQQQf1bMJAvyEQYBDbe++947DDDuvRupMnT45//vOf8cgjj3T6LsJxxx0Xq1ev7vHzA/2bbzEAnTr55JPjpJNOijPPPDMWL14cTz75ZNx5551x1113RUTEV77ylVi6dGnMmTMnVq5cGY8++mjccccdMWfOnD6eOdAbBALQpV/+8pdx/PHHx1lnnRVve9vb4stf/nK89tprEfG/dxh+//vfxyOPPBInnnhiHHvssXHppZfG2LFj+3jWQG+olFJKX08CAOhfvIMAACQCAQBIBAIAkAgEACARCABAIhAAgEQgAACJQAAAEoEAACQCAQBIBAIAkPwfaPtfspmQPyoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.pairplot(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "bi8BTkrwIA1K",
        "outputId": "e92f2eeb-62d9-4cf4-d70b-14ecf10c844e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x78e81ecf8c40>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 750x750 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.hist(figsize=(10,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "id": "bSZ_uj9kJ2k0",
        "outputId": "8b069842-d3f9-4c1b-d5dd-8fae030b0734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Quantity'}>,\n",
              "        <Axes: title={'center': 'Date'}>],\n",
              "       [<Axes: title={'center': 'Price'}>,\n",
              "        <Axes: title={'center': 'CustomerID'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizing Correlation**"
      ],
      "metadata": {
        "id": "bjjJ6F8mNSKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "Xfa3GOT7NdPa",
        "outputId": "b6c92345-1a39-4c70-a701-616f3790cb9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-48-c187c74d1e71>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  dataset.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity     Price  CustomerID\n",
              "Quantity    1.000000 -0.002803   -0.005990\n",
              "Price      -0.002803  1.000000   -0.010245\n",
              "CustomerID -0.005990 -0.010245    1.000000"
            ],
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              "      <th>CustomerID</th>\n",
              "      <td>-0.005990</td>\n",
              "      <td>-0.010245</td>\n",
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              "        const element = document.querySelector('#df-23f60701-7a66-458c-b71d-f98aeeee53e7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\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",
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              "        element.innerHTML = '';\n",
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              "    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-7e784df8-8f42-4a44-b67d-676d81c943a9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(dataset.corr(), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "OIFzaMj8Nolk",
        "outputId": "a1ac69cd-d359-48b1-f02a-362c1f23c2ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-1afac6b5cf53>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(dataset.corr(), annot=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}