Market_Basket_Analysis.ipynb

{
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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9NaoXO4zk65o"
      },
      "outputs": [],
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "dYifFJk6BaQE",
        "outputId": "bef33cd1-8202-4073-85d6-ea111728b693"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Month of the year</th>\n",
              "      <th>Transaction ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Product Category (Enhanced Ecommerce)</th>\n",
              "      <th>Product Brand</th>\n",
              "      <th>Product Revenue</th>\n",
              "      <th>Quantity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>466349</td>\n",
              "      <td>Benamôr - Benamôr - Creme de Rosto 50ml</td>\n",
              "      <td>Hidratantes</td>\n",
              "      <td>Benamôr</td>\n",
              "      <td>11.93</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>466349</td>\n",
              "      <td>Lancôme - La Vie Est Belle - Eau de Parfum 1.2...</td>\n",
              "      <td>Amostras</td>\n",
              "      <td>Lancôme</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>466349</td>\n",
              "      <td>Lancôme - La Vie Est Belle - Eau de Parfum 30ml</td>\n",
              "      <td>Perfumes</td>\n",
              "      <td>Lancôme</td>\n",
              "      <td>48.11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>466351</td>\n",
              "      <td>Benamôr - Benamôr - Creme Chantilly Hidratante...</td>\n",
              "      <td>Hidratantes</td>\n",
              "      <td>Benamôr</td>\n",
              "      <td>19.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>466351</td>\n",
              "      <td>Benamôr - Benamôr - Sabonete original 100g</td>\n",
              "      <td>Banho</td>\n",
              "      <td>Benamôr</td>\n",
              "      <td>4.43</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Month of the year  Transaction ID  \\\n",
              "0  2022                  1          466349   \n",
              "1  2022                  1          466349   \n",
              "2  2022                  1          466349   \n",
              "3  2022                  1          466351   \n",
              "4  2022                  1          466351   \n",
              "\n",
              "                                             Product  \\\n",
              "0            Benamôr - Benamôr - Creme de Rosto 50ml   \n",
              "1  Lancôme - La Vie Est Belle - Eau de Parfum 1.2...   \n",
              "2    Lancôme - La Vie Est Belle - Eau de Parfum 30ml   \n",
              "3  Benamôr - Benamôr - Creme Chantilly Hidratante...   \n",
              "4         Benamôr - Benamôr - Sabonete original 100g   \n",
              "\n",
              "  Product Category (Enhanced Ecommerce) Product Brand  Product Revenue  \\\n",
              "0                           Hidratantes       Benamôr            11.93   \n",
              "1                              Amostras       Lancôme             0.00   \n",
              "2                              Perfumes       Lancôme            48.11   \n",
              "3                           Hidratantes       Benamôr            19.50   \n",
              "4                                 Banho       Benamôr             4.43   \n",
              "\n",
              "   Quantity  \n",
              "0         1  \n",
              "1         1  \n",
              "2         1  \n",
              "3         1  \n",
              "4         1  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_excel('perfumes.xlsx')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "SQ1YfZcbCJp4",
        "outputId": "b82e65a7-3cbe-418c-d4a6-995af4ac9395"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Month of the year</th>\n",
              "      <th>Transaction ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Product Category (Enhanced Ecommerce)</th>\n",
              "      <th>Product Brand</th>\n",
              "      <th>Product Revenue</th>\n",
              "      <th>Quantity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>72909</th>\n",
              "      <td>2022</td>\n",
              "      <td>10</td>\n",
              "      <td>531748</td>\n",
              "      <td>Dior - Miss Dior Blooming B - Eau de Toilette ...</td>\n",
              "      <td>Perfumes</td>\n",
              "      <td>Dior</td>\n",
              "      <td>96.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72910</th>\n",
              "      <td>2022</td>\n",
              "      <td>10</td>\n",
              "      <td>531749</td>\n",
              "      <td>Anny - Anny - 168.50 - BIKINI BASH 15ml</td>\n",
              "      <td>Vernizes</td>\n",
              "      <td>Anny</td>\n",
              "      <td>7.92</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72911</th>\n",
              "      <td>2022</td>\n",
              "      <td>10</td>\n",
              "      <td>531749</td>\n",
              "      <td>Anny - Anny - 305 - COOL ATTITUDE 15ml</td>\n",
              "      <td>Vernizes</td>\n",
              "      <td>Anny</td>\n",
              "      <td>7.92</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72912</th>\n",
              "      <td>2022</td>\n",
              "      <td>10</td>\n",
              "      <td>531749</td>\n",
              "      <td>Anny - Anny - 314.30 - HOT CHOCOLATE WEATHER 15ml</td>\n",
              "      <td>Vernizes</td>\n",
              "      <td>Anny</td>\n",
              "      <td>7.92</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72913</th>\n",
              "      <td>2022</td>\n",
              "      <td>10</td>\n",
              "      <td>531749</td>\n",
              "      <td>Anny - Hiking In La - 371.40 - SAVE THE GREEN ...</td>\n",
              "      <td>Vernizes</td>\n",
              "      <td>Anny</td>\n",
              "      <td>7.92</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Year  Month of the year  Transaction ID  \\\n",
              "72909  2022                 10          531748   \n",
              "72910  2022                 10          531749   \n",
              "72911  2022                 10          531749   \n",
              "72912  2022                 10          531749   \n",
              "72913  2022                 10          531749   \n",
              "\n",
              "                                                 Product  \\\n",
              "72909  Dior - Miss Dior Blooming B - Eau de Toilette ...   \n",
              "72910            Anny - Anny - 168.50 - BIKINI BASH 15ml   \n",
              "72911             Anny - Anny - 305 - COOL ATTITUDE 15ml   \n",
              "72912  Anny - Anny - 314.30 - HOT CHOCOLATE WEATHER 15ml   \n",
              "72913  Anny - Hiking In La - 371.40 - SAVE THE GREEN ...   \n",
              "\n",
              "      Product Category (Enhanced Ecommerce) Product Brand  Product Revenue  \\\n",
              "72909                              Perfumes          Dior            96.75   \n",
              "72910                              Vernizes          Anny             7.92   \n",
              "72911                              Vernizes          Anny             7.92   \n",
              "72912                              Vernizes          Anny             7.92   \n",
              "72913                              Vernizes          Anny             7.92   \n",
              "\n",
              "       Quantity  \n",
              "72909         1  \n",
              "72910         1  \n",
              "72911         1  \n",
              "72912         1  \n",
              "72913         1  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ls0hqGiNCKWy",
        "outputId": "8d636b38-5f2e-4c59-8cc1-948798878d68"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Year                                         1\n",
              "Month of the year                           10\n",
              "Transaction ID                           46875\n",
              "Product                                  11360\n",
              "Product Category (Enhanced Ecommerce)       88\n",
              "Product Brand                              178\n",
              "Product Revenue                           6493\n",
              "Quantity                                     9\n",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#counting unique values in dataframe\n",
        "count = df.nunique()\n",
        "count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-A-NWS70CPc2",
        "outputId": "d3ec3e95-7bbe-476b-d1e7-dfc5bf47309b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 72914 entries, 0 to 72913\n",
            "Data columns (total 8 columns):\n",
            " #   Column                                 Non-Null Count  Dtype  \n",
            "---  ------                                 --------------  -----  \n",
            " 0   Year                                   72914 non-null  int64  \n",
            " 1   Month of the year                      72914 non-null  int64  \n",
            " 2   Transaction ID                         72914 non-null  int64  \n",
            " 3   Product                                72914 non-null  object \n",
            " 4   Product Category (Enhanced Ecommerce)  72914 non-null  object \n",
            " 5   Product Brand                          72914 non-null  object \n",
            " 6   Product Revenue                        72914 non-null  float64\n",
            " 7   Quantity                               72914 non-null  int64  \n",
            "dtypes: float64(1), int64(4), object(3)\n",
            "memory usage: 4.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKYRGUoxj5cu",
        "outputId": "90190401-61f9-464e-eda4-a765e7d67b0c"
      },
      "outputs": [],
      "source": [
        "df['Transaction ID'] = df['Transaction ID'].astype('str')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2AgXLoVNnrOf",
        "outputId": "935b7937-11ad-4ec8-9c8e-70ca1713aeeb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 72914 entries, 0 to 72913\n",
            "Data columns (total 8 columns):\n",
            " #   Column                                 Non-Null Count  Dtype  \n",
            "---  ------                                 --------------  -----  \n",
            " 0   Year                                   72914 non-null  int64  \n",
            " 1   Month of the year                      72914 non-null  int64  \n",
            " 2   Transaction ID                         72914 non-null  object \n",
            " 3   Product                                72914 non-null  object \n",
            " 4   Product Category (Enhanced Ecommerce)  72914 non-null  object \n",
            " 5   Product Brand                          72914 non-null  object \n",
            " 6   Product Revenue                        72914 non-null  float64\n",
            " 7   Quantity                               72914 non-null  int64  \n",
            "dtypes: float64(1), int64(3), object(4)\n",
            "memory usage: 4.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "oCj3Yl7Xk2Q0",
        "outputId": "c66717e9-069f-41d7-954e-2e0e2a80c95a"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product Category (Enhanced Ecommerce)</th>\n",
              "      <th>Quantity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>466349</th>\n",
              "      <td>Hidratantes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466349</th>\n",
              "      <td>Perfumes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466351</th>\n",
              "      <td>Banho</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>466351</th>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531743</th>\n",
              "      <td>Perfumes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531745</th>\n",
              "      <td>Perfumes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531747</th>\n",
              "      <td>Perfumes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531748</th>\n",
              "      <td>Perfumes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531749</th>\n",
              "      <td>Vernizes</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63569 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Product Category (Enhanced Ecommerce)  Quantity\n",
              "Transaction ID                                                \n",
              "466349                                      Amostras         1\n",
              "466349                                   Hidratantes         1\n",
              "466349                                      Perfumes         1\n",
              "466351                                         Banho         1\n",
              "466351                                   Hidratantes         1\n",
              "...                                              ...       ...\n",
              "531743                                      Perfumes         1\n",
              "531745                                      Perfumes         1\n",
              "531747                                      Perfumes         1\n",
              "531748                                      Perfumes         1\n",
              "531749                                      Vernizes         4\n",
              "\n",
              "[63569 rows x 2 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Basket = df.groupby(['Transaction ID', 'Product Category (Enhanced Ecommerce)'])['Quantity'].sum().reset_index().fillna(0).set_index('Transaction ID')\n",
        "Basket"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "6q99glcFoCWY",
        "outputId": "bb528f96-5bb8-43c0-fce5-6ea7d904d1c9"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Product Category (Enhanced Ecommerce)</th>\n",
              "      <th>Acessórios de Moda</th>\n",
              "      <th>Acessórios manicure</th>\n",
              "      <th>After Sun Corpo</th>\n",
              "      <th>After Sun Rosto</th>\n",
              "      <th>Aftershave</th>\n",
              "      <th>Amostras</th>\n",
              "      <th>Anti-idade</th>\n",
              "      <th>Auto-bronzeadores Corpo</th>\n",
              "      <th>Auto-bronzeadores Rosto</th>\n",
              "      <th>Avolumadores</th>\n",
              "      <th>...</th>\n",
              "      <th>Soluções específicas</th>\n",
              "      <th>Sombras</th>\n",
              "      <th>Styling</th>\n",
              "      <th>Suplementos Cabelo &amp; Unhas</th>\n",
              "      <th>Suplementos Pele</th>\n",
              "      <th>Séruns</th>\n",
              "      <th>Tez</th>\n",
              "      <th>Tratamentos</th>\n",
              "      <th>Tónicos</th>\n",
              "      <th>Vernizes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transaction ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>46875 rows × 88 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Product Category (Enhanced Ecommerce)  Acessórios de Moda  \\\n",
              "Transaction ID                                              \n",
              "466349                                                0.0   \n",
              "466351                                                0.0   \n",
              "466353                                                0.0   \n",
              "466354                                                0.0   \n",
              "466355                                                0.0   \n",
              "...                                                   ...   \n",
              "531743                                                0.0   \n",
              "531745                                                0.0   \n",
              "531747                                                0.0   \n",
              "531748                                                0.0   \n",
              "531749                                                0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  Acessórios manicure  After Sun Corpo  \\\n",
              "Transaction ID                                                                \n",
              "466349                                                 0.0              0.0   \n",
              "466351                                                 0.0              0.0   \n",
              "466353                                                 0.0              0.0   \n",
              "466354                                                 0.0              0.0   \n",
              "466355                                                 0.0              0.0   \n",
              "...                                                    ...              ...   \n",
              "531743                                                 0.0              0.0   \n",
              "531745                                                 0.0              0.0   \n",
              "531747                                                 0.0              0.0   \n",
              "531748                                                 0.0              0.0   \n",
              "531749                                                 0.0              0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  After Sun Rosto  Aftershave  Amostras  \\\n",
              "Transaction ID                                                                 \n",
              "466349                                             0.0         0.0       1.0   \n",
              "466351                                             0.0         0.0       0.0   \n",
              "466353                                             0.0         0.0       0.0   \n",
              "466354                                             0.0         0.0       0.0   \n",
              "466355                                             0.0         0.0       0.0   \n",
              "...                                                ...         ...       ...   \n",
              "531743                                             0.0         0.0       0.0   \n",
              "531745                                             0.0         0.0       0.0   \n",
              "531747                                             0.0         0.0       0.0   \n",
              "531748                                             0.0         0.0       0.0   \n",
              "531749                                             0.0         0.0       0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  Anti-idade  Auto-bronzeadores Corpo  \\\n",
              "Transaction ID                                                               \n",
              "466349                                        0.0                      0.0   \n",
              "466351                                        0.0                      0.0   \n",
              "466353                                        0.0                      0.0   \n",
              "466354                                        0.0                      0.0   \n",
              "466355                                        0.0                      0.0   \n",
              "...                                           ...                      ...   \n",
              "531743                                        0.0                      0.0   \n",
              "531745                                        0.0                      0.0   \n",
              "531747                                        0.0                      0.0   \n",
              "531748                                        0.0                      0.0   \n",
              "531749                                        0.0                      0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  Auto-bronzeadores Rosto  Avolumadores  \\\n",
              "Transaction ID                                                                 \n",
              "466349                                                     0.0           0.0   \n",
              "466351                                                     0.0           0.0   \n",
              "466353                                                     0.0           0.0   \n",
              "466354                                                     0.0           0.0   \n",
              "466355                                                     0.0           0.0   \n",
              "...                                                        ...           ...   \n",
              "531743                                                     0.0           0.0   \n",
              "531745                                                     0.0           0.0   \n",
              "531747                                                     0.0           0.0   \n",
              "531748                                                     0.0           0.0   \n",
              "531749                                                     0.0           0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  ...  Soluções específicas  Sombras  \\\n",
              "Transaction ID                         ...                                  \n",
              "466349                                 ...                   0.0      0.0   \n",
              "466351                                 ...                   0.0      0.0   \n",
              "466353                                 ...                   0.0      0.0   \n",
              "466354                                 ...                   0.0      0.0   \n",
              "466355                                 ...                   0.0      0.0   \n",
              "...                                    ...                   ...      ...   \n",
              "531743                                 ...                   0.0      0.0   \n",
              "531745                                 ...                   0.0      0.0   \n",
              "531747                                 ...                   0.0      0.0   \n",
              "531748                                 ...                   0.0      0.0   \n",
              "531749                                 ...                   0.0      0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  Styling  Suplementos Cabelo & Unhas  \\\n",
              "Transaction ID                                                               \n",
              "466349                                     0.0                         0.0   \n",
              "466351                                     0.0                         0.0   \n",
              "466353                                     0.0                         0.0   \n",
              "466354                                     0.0                         0.0   \n",
              "466355                                     0.0                         0.0   \n",
              "...                                        ...                         ...   \n",
              "531743                                     0.0                         0.0   \n",
              "531745                                     0.0                         0.0   \n",
              "531747                                     0.0                         0.0   \n",
              "531748                                     0.0                         0.0   \n",
              "531749                                     0.0                         0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  Suplementos Pele  Séruns  Tez  \\\n",
              "Transaction ID                                                         \n",
              "466349                                              0.0     0.0  0.0   \n",
              "466351                                              0.0     0.0  0.0   \n",
              "466353                                              0.0     0.0  0.0   \n",
              "466354                                              0.0     0.0  0.0   \n",
              "466355                                              0.0     0.0  0.0   \n",
              "...                                                 ...     ...  ...   \n",
              "531743                                              0.0     0.0  0.0   \n",
              "531745                                              0.0     0.0  0.0   \n",
              "531747                                              0.0     0.0  0.0   \n",
              "531748                                              0.0     0.0  0.0   \n",
              "531749                                              0.0     0.0  0.0   \n",
              "\n",
              "Product Category (Enhanced Ecommerce)  Tratamentos  Tónicos  Vernizes  \n",
              "Transaction ID                                                         \n",
              "466349                                         0.0      0.0       0.0  \n",
              "466351                                         0.0      0.0       0.0  \n",
              "466353                                         0.0      0.0       0.0  \n",
              "466354                                         0.0      0.0       0.0  \n",
              "466355                                         0.0      0.0       0.0  \n",
              "...                                            ...      ...       ...  \n",
              "531743                                         0.0      0.0       0.0  \n",
              "531745                                         0.0      0.0       0.0  \n",
              "531747                                         0.0      0.0       0.0  \n",
              "531748                                         0.0      0.0       0.0  \n",
              "531749                                         0.0      0.0       4.0  \n",
              "\n",
              "[46875 rows x 88 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#number of rows decrease as unstack changed the shape of datafram\n",
        "#zero indicates that no product was bought by this transaction id\n",
        "\n",
        "Basket = df.groupby(['Transaction ID', 'Product Category (Enhanced Ecommerce)'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('Transaction ID')\n",
        "Basket\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "EC_hWNBct25F"
      },
      "outputs": [],
      "source": [
        "#converting to 1 and 0 as it is required by our algorithm\n",
        "#0 will mean present in transaction\n",
        "#1 will mean item was not bought\n",
        "def convert(x):\n",
        "  if x<= 0:\n",
        "    return False\n",
        "  if x>= 1:\n",
        "    return True\n",
        "basket = Basket.applymap(convert)\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "FH7uV6l5z4od"
      },
      "source": [
        "**Metrics of Association Analysis**\n",
        "\n",
        "**Support**--> if we have item A in 10 transactions out of 100 support is 10/100\n",
        "\n",
        "**Confidence**--> We consider two items .If item B come in 5 transactions out of 10 transactions in which Item A was present the confidence will be  5/10\n",
        "\n",
        "**Lift** --> Confidence of A and B divided by Support of B"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1019
        },
        "id": "z-4T2gSVz-H8",
        "outputId": "d1051119-4a31-46bb-82c4-284e62f257ea"
      },
      "outputs": [
        {
          "data": {
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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",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(After Sun Corpo)</td>\n",
              "      <td>(Protectores solares Corpo)</td>\n",
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              "      <td>0.007189</td>\n",
              "      <td>0.001451</td>\n",
              "      <td>0.453333</td>\n",
              "      <td>63.056380</td>\n",
              "      <td>0.001428</td>\n",
              "      <td>1.816117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Protectores solares Corpo)</td>\n",
              "      <td>(After Sun Corpo)</td>\n",
              "      <td>0.007189</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Séruns)</td>\n",
              "      <td>(Amostras)</td>\n",
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              "      <td>0.001003</td>\n",
              "      <td>0.028485</td>\n",
              "      <td>3.112418</td>\n",
              "      <td>0.000681</td>\n",
              "      <td>1.019900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Amostras)</td>\n",
              "      <td>(Séruns)</td>\n",
              "      <td>0.009152</td>\n",
              "      <td>0.035200</td>\n",
              "      <td>0.001003</td>\n",
              "      <td>0.109557</td>\n",
              "      <td>3.112418</td>\n",
              "      <td>0.000681</td>\n",
              "      <td>1.083506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Coffrets Cosmética)</td>\n",
              "      <td>(Anti-idade)</td>\n",
              "      <td>0.031851</td>\n",
              "      <td>0.050347</td>\n",
              "      <td>0.001621</td>\n",
              "      <td>0.050904</td>\n",
              "      <td>1.011074</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>1.000587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>(Séruns, Olhos &amp; Lábios)</td>\n",
              "      <td>(Hidratantes)</td>\n",
              "      <td>0.005440</td>\n",
              "      <td>0.061888</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>0.258824</td>\n",
              "      <td>4.182128</td>\n",
              "      <td>0.001071</td>\n",
              "      <td>1.265707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>(Hidratantes, Olhos &amp; Lábios)</td>\n",
              "      <td>(Séruns)</td>\n",
              "      <td>0.005781</td>\n",
              "      <td>0.035200</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>0.243542</td>\n",
              "      <td>6.918819</td>\n",
              "      <td>0.001204</td>\n",
              "      <td>1.275419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>(Séruns)</td>\n",
              "      <td>(Hidratantes, Olhos &amp; Lábios)</td>\n",
              "      <td>0.035200</td>\n",
              "      <td>0.005781</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>6.918819</td>\n",
              "      <td>0.001204</td>\n",
              "      <td>1.035644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>(Hidratantes)</td>\n",
              "      <td>(Séruns, Olhos &amp; Lábios)</td>\n",
              "      <td>0.061888</td>\n",
              "      <td>0.005440</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>0.022751</td>\n",
              "      <td>4.182128</td>\n",
              "      <td>0.001071</td>\n",
              "      <td>1.017714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>(Olhos &amp; Lábios)</td>\n",
              "      <td>(Séruns, Hidratantes)</td>\n",
              "      <td>0.033216</td>\n",
              "      <td>0.006379</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>0.042389</td>\n",
              "      <td>6.645466</td>\n",
              "      <td>0.001196</td>\n",
              "      <td>1.037605</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       antecedents                    consequents  \\\n",
              "0                (After Sun Corpo)    (Protectores solares Corpo)   \n",
              "1      (Protectores solares Corpo)              (After Sun Corpo)   \n",
              "2                         (Séruns)                     (Amostras)   \n",
              "3                       (Amostras)                       (Séruns)   \n",
              "4             (Coffrets Cosmética)                   (Anti-idade)   \n",
              "..                             ...                            ...   \n",
              "155       (Séruns, Olhos & Lábios)                  (Hidratantes)   \n",
              "156  (Hidratantes, Olhos & Lábios)                       (Séruns)   \n",
              "157                       (Séruns)  (Hidratantes, Olhos & Lábios)   \n",
              "158                  (Hidratantes)       (Séruns, Olhos & Lábios)   \n",
              "159               (Olhos & Lábios)          (Séruns, Hidratantes)   \n",
              "\n",
              "     antecedent support  consequent support   support  confidence       lift  \\\n",
              "0              0.003200            0.007189  0.001451    0.453333  63.056380   \n",
              "1              0.007189            0.003200  0.001451    0.201780  63.056380   \n",
              "2              0.035200            0.009152  0.001003    0.028485   3.112418   \n",
              "3              0.009152            0.035200  0.001003    0.109557   3.112418   \n",
              "4              0.031851            0.050347  0.001621    0.050904   1.011074   \n",
              "..                  ...                 ...       ...         ...        ...   \n",
              "155            0.005440            0.061888  0.001408    0.258824   4.182128   \n",
              "156            0.005781            0.035200  0.001408    0.243542   6.918819   \n",
              "157            0.035200            0.005781  0.001408    0.040000   6.918819   \n",
              "158            0.061888            0.005440  0.001408    0.022751   4.182128   \n",
              "159            0.033216            0.006379  0.001408    0.042389   6.645466   \n",
              "\n",
              "     leverage  conviction  \n",
              "0    0.001428    1.816117  \n",
              "1    0.001428    1.248779  \n",
              "2    0.000681    1.019900  \n",
              "3    0.000681    1.083506  \n",
              "4    0.000018    1.000587  \n",
              "..        ...         ...  \n",
              "155  0.001071    1.265707  \n",
              "156  0.001204    1.275419  \n",
              "157  0.001204    1.035644  \n",
              "158  0.001071    1.017714  \n",
              "159  0.001196    1.037605  \n",
              "\n",
              "[160 rows x 9 columns]"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#applying apriori algorithm\n",
        "#By default, apriori returns the column indices of the items, which may be useful in downstream operations \n",
        "#such as association rule mining. For better readability, we can set use_colnames=True to convert these integer values into the respective item names:\n",
        "#Lift basically tells us the likelihood of buying a the two items together \n",
        "#min_support  parameter is used to select the items with support values greater than the value specified by the parameter. \n",
        "#min_length parameter specifies the minimum number of items that you want in your rules.\n",
        "itemset = apriori(basket, min_support=0.001,use_colnames = True)\n",
        "frequent = association_rules(itemset, metric=\"lift\", min_threshold=1)\n",
        "frequent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1019
        },
        "id": "BeZpDD6AExIC",
        "outputId": "0b635f05-942e-4cf7-bb32-a174dd7d8c71"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(After Sun Corpo)</td>\n",
              "      <td>(Protectores solares Corpo)</td>\n",
              "      <td>0.003200</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.001451</td>\n",
              "      <td>0.453333</td>\n",
              "      <td>63.056380</td>\n",
              "      <td>0.001428</td>\n",
              "      <td>1.816117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Protectores solares Corpo)</td>\n",
              "      <td>(After Sun Corpo)</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.003200</td>\n",
              "      <td>0.001451</td>\n",
              "      <td>0.201780</td>\n",
              "      <td>63.056380</td>\n",
              "      <td>0.001428</td>\n",
              "      <td>1.248779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>(Champô)</td>\n",
              "      <td>(Condicionador)</td>\n",
              "      <td>0.019371</td>\n",
              "      <td>0.003413</td>\n",
              "      <td>0.002112</td>\n",
              "      <td>0.109031</td>\n",
              "      <td>31.942628</td>\n",
              "      <td>0.002046</td>\n",
              "      <td>1.118542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>(Condicionador)</td>\n",
              "      <td>(Champô)</td>\n",
              "      <td>0.003413</td>\n",
              "      <td>0.019371</td>\n",
              "      <td>0.002112</td>\n",
              "      <td>0.618750</td>\n",
              "      <td>31.942628</td>\n",
              "      <td>0.002046</td>\n",
              "      <td>2.572143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>(Protectores solares Rosto)</td>\n",
              "      <td>(Protectores solares Corpo)</td>\n",
              "      <td>0.008704</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.001536</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>24.546169</td>\n",
              "      <td>0.001473</td>\n",
              "      <td>1.205556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>(Limpeza Desmaquilhantes)</td>\n",
              "      <td>(Bases Liquidas)</td>\n",
              "      <td>0.028181</td>\n",
              "      <td>0.041216</td>\n",
              "      <td>0.001344</td>\n",
              "      <td>0.047691</td>\n",
              "      <td>1.157103</td>\n",
              "      <td>0.000182</td>\n",
              "      <td>1.006799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>(Hidratantes)</td>\n",
              "      <td>(Tratamentos)</td>\n",
              "      <td>0.061888</td>\n",
              "      <td>0.015765</td>\n",
              "      <td>0.001024</td>\n",
              "      <td>0.016546</td>\n",
              "      <td>1.049519</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>1.000794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>(Tratamentos)</td>\n",
              "      <td>(Hidratantes)</td>\n",
              "      <td>0.015765</td>\n",
              "      <td>0.061888</td>\n",
              "      <td>0.001024</td>\n",
              "      <td>0.064953</td>\n",
              "      <td>1.049519</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>1.003278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Coffrets Cosmética)</td>\n",
              "      <td>(Anti-idade)</td>\n",
              "      <td>0.031851</td>\n",
              "      <td>0.050347</td>\n",
              "      <td>0.001621</td>\n",
              "      <td>0.050904</td>\n",
              "      <td>1.011074</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>1.000587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(Anti-idade)</td>\n",
              "      <td>(Coffrets Cosmética)</td>\n",
              "      <td>0.050347</td>\n",
              "      <td>0.031851</td>\n",
              "      <td>0.001621</td>\n",
              "      <td>0.032203</td>\n",
              "      <td>1.011074</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>1.000364</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     antecedents                  consequents  \\\n",
              "0              (After Sun Corpo)  (Protectores solares Corpo)   \n",
              "1    (Protectores solares Corpo)            (After Sun Corpo)   \n",
              "62                      (Champô)              (Condicionador)   \n",
              "63               (Condicionador)                     (Champô)   \n",
              "145  (Protectores solares Rosto)  (Protectores solares Corpo)   \n",
              "..                           ...                          ...   \n",
              "32     (Limpeza Desmaquilhantes)             (Bases Liquidas)   \n",
              "116                (Hidratantes)                (Tratamentos)   \n",
              "117                (Tratamentos)                (Hidratantes)   \n",
              "4           (Coffrets Cosmética)                 (Anti-idade)   \n",
              "5                   (Anti-idade)         (Coffrets Cosmética)   \n",
              "\n",
              "     antecedent support  consequent support   support  confidence       lift  \\\n",
              "0              0.003200            0.007189  0.001451    0.453333  63.056380   \n",
              "1              0.007189            0.003200  0.001451    0.201780  63.056380   \n",
              "62             0.019371            0.003413  0.002112    0.109031  31.942628   \n",
              "63             0.003413            0.019371  0.002112    0.618750  31.942628   \n",
              "145            0.008704            0.007189  0.001536    0.176471  24.546169   \n",
              "..                  ...                 ...       ...         ...        ...   \n",
              "32             0.028181            0.041216  0.001344    0.047691   1.157103   \n",
              "116            0.061888            0.015765  0.001024    0.016546   1.049519   \n",
              "117            0.015765            0.061888  0.001024    0.064953   1.049519   \n",
              "4              0.031851            0.050347  0.001621    0.050904   1.011074   \n",
              "5              0.050347            0.031851  0.001621    0.032203   1.011074   \n",
              "\n",
              "     leverage  conviction  \n",
              "0    0.001428    1.816117  \n",
              "1    0.001428    1.248779  \n",
              "62   0.002046    1.118542  \n",
              "63   0.002046    2.572143  \n",
              "145  0.001473    1.205556  \n",
              "..        ...         ...  \n",
              "32   0.000182    1.006799  \n",
              "116  0.000048    1.000794  \n",
              "117  0.000048    1.003278  \n",
              "4    0.000018    1.000587  \n",
              "5    0.000018    1.000364  \n",
              "\n",
              "[160 rows x 9 columns]"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "frequent.sort_values(by=['lift'],ascending = False)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/18/j3gjk3wn59d8pmqv5vh1f_g40000gn/T/ipykernel_6229/639449669.py:24: MatplotlibDeprecationWarning: Passing the pad parameter of tight_layout() positionally is deprecated since Matplotlib 3.3; the parameter will become keyword-only two minor releases later.\n",
            "  plt.tight_layout(10)  # Adjust the layout for better spacing\n",
            "/var/folders/18/j3gjk3wn59d8pmqv5vh1f_g40000gn/T/ipykernel_6229/639449669.py:24: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. \n",
            "  plt.tight_layout(10)  # Adjust the layout for better spacing\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Sort the dataset by lift in descending order\n",
        "sorted_dataset = frequent.sort_values('lift', ascending=False)\n",
        "\n",
        "# Select the top 10 antecedents, consequents, and lift values\n",
        "top_10_antecedents = frequent['antecedents'].astype(str).head(10)\n",
        "top_10_consequents = frequent['consequents'].astype(str).head(10)\n",
        "top_10_lift = frequent['lift'].head(10)\n",
        "\n",
        "# Create the scatter plot\n",
        "fig, ax = plt.subplots()\n",
        "scatter = ax.scatter(top_10_antecedents, top_10_consequents, c=top_10_lift, cmap='viridis')\n",
        "plt.xlabel('Antecedents')\n",
        "plt.ylabel('Consequents')\n",
        "plt.title('Top 10 Antecedents and Consequents by Lift')\n",
        "plt.xticks(rotation=90)  # Rotate x-axis labels for better readability\n",
        "\n",
        "# Add a colorbar-like legend\n",
        "cbar = plt.colorbar(scatter)\n",
        "cbar.set_label('Lift')\n",
        "\n",
        "plt.tight_layout(10)  # Adjust the layout for better spacing\n",
        "plt.show()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Convert frozenset objects to strings and remove \"frozenset()\" notation\n",
        "frequent['antecedents'] = frequent['antecedents'].astype(str).str.replace('frozenset', '')\n",
        "frequent['consequents'] = frequent['consequents'].astype(str).str.replace('frozenset', '')\n",
        "\n",
        "# Save the frequent itemsets to an Excel file\n",
        "frequent.to_excel('product_categories_correlations.xlsx', index=False)\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ],
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