Copia_de_Prac2Ej1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/javierarteagapuell/deeplearning/blob/main/Copia_de_Prac2Ej1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funcionamiento de las LIBRERIAS mlxtend o apyori"
      ],
      "metadata": {
        "id": "5LlBpXd2yhHC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets\n",
        "\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "#pip install mlxtend  "
      ],
      "metadata": {
        "id": "Rt81YtPQyGrH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('BlackFriday.csv', encoding='UTF-8')\n",
        "data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "ZMwQHeXs3wI8",
        "outputId": "5e46923a-c597-42ed-c06f-dc3831119f97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
              "0  1000004  P00128942      M  46-50           7             B   \n",
              "1  1000009  P00113442      M  26-35          17             C   \n",
              "2  1000010  P00288442      F  36-45           1             B   \n",
              "3  1000010  P00145342      F  36-45           1             B   \n",
              "4  1000011  P00053842      F  26-35           1             C   \n",
              "\n",
              "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
              "0                          2               1                   1   \n",
              "1                          0               0                   3   \n",
              "2                         4+               1                   5   \n",
              "3                         4+               1                   4   \n",
              "4                          1               0                   4   \n",
              "\n",
              "   Product_Category_2  Product_Category_3  \n",
              "0                11.0                 NaN  \n",
              "1                 5.0                 NaN  \n",
              "2                14.0                 NaN  \n",
              "3                 9.0                 NaN  \n",
              "4                 5.0                12.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-598eebd2-34e5-479e-9702-0655d6ddab01\">\n",
              "    <div 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>User_ID</th>\n",
              "      <th>Product_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>City_Category</th>\n",
              "      <th>Stay_In_Current_City_Years</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Product_Category_1</th>\n",
              "      <th>Product_Category_2</th>\n",
              "      <th>Product_Category_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000004</td>\n",
              "      <td>P00128942</td>\n",
              "      <td>M</td>\n",
              "      <td>46-50</td>\n",
              "      <td>7</td>\n",
              "      <td>B</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000009</td>\n",
              "      <td>P00113442</td>\n",
              "      <td>M</td>\n",
              "      <td>26-35</td>\n",
              "      <td>17</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000010</td>\n",
              "      <td>P00288442</td>\n",
              "      <td>F</td>\n",
              "      <td>36-45</td>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "      <td>4+</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000010</td>\n",
              "      <td>P00145342</td>\n",
              "      <td>F</td>\n",
              "      <td>36-45</td>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "      <td>4+</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000011</td>\n",
              "      <td>P00053842</td>\n",
              "      <td>F</td>\n",
              "      <td>26-35</td>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-598eebd2-34e5-479e-9702-0655d6ddab01')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-598eebd2-34e5-479e-9702-0655d6ddab01 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-598eebd2-34e5-479e-9702-0655d6ddab01');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNBThuo14kbd",
        "outputId": "2a3c9293-119f-46a6-d804-1b383e276f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
              "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
              "       'Product_Category_2', 'Product_Category_3'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a ver las ocurrencias de datos en las columnas de nuestro dataframe. Obviaremos product ID y user ID ya que los dos son unicos o hay muchos registros distintos de cada valor.\n"
      ],
      "metadata": {
        "id": "QQ5PPYqx3mVH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols_interes= data.columns[2:]\n",
        "for col in cols_interes:\n",
        "    print(f'Valores de la columa {col}: {data[col].unique()}')"
      ],
      "metadata": {
        "id": "8Vezukm73lnr",
        "outputId": "eee1b7a1-5278-4337-992f-d76f618a9f1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valores de la columa Gender: ['M' 'F']\n",
            "Valores de la columa Age: ['46-50' '26-35' '36-45' '18-25' '51-55' '55+' '0-17']\n",
            "Valores de la columa Occupation: [ 7 17  1 15  3  0  8 16  4 12 13 18 11  9  2 20 14 10  6 19  5]\n",
            "Valores de la columa City_Category: ['B' 'C' 'A']\n",
            "Valores de la columa Stay_In_Current_City_Years: ['2' '0' '4+' '1' '3']\n",
            "Valores de la columa Marital_Status: [1 0]\n",
            "Valores de la columa Product_Category_1: [ 1  3  5  4  2 10 15 18  8 13  6 11 12  7 16 14 17  9]\n",
            "Valores de la columa Product_Category_2: [11.  5. 14.  9.  3.  4. 13.  2. nan  8. 15. 17. 12. 16.  6. 10. 18.  7.]\n",
            "Valores de la columa Product_Category_3: [nan 12. 15.  9. 16. 14.  4.  3.  5.  8. 17. 13. 11. 10.  6. 18.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vemos que las columas de Gender y Maritial Status son binarias. Las demas de pueden pasar a categoricas usando la fucnion .get_dummies. Esto lo haremos mas alanete ya que primero hay que comprobar si hay valores repetidos o nulos."
      ],
      "metadata": {
        "id": "qJ8c6vc55DAy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().values.any() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b24Boari4iUE",
        "outputId": "8dc2028c-8c2f-45b3-a8c6-ac9e9fbe9428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vemos en que columnas hay NA's"
      ],
      "metadata": {
        "id": "zH2WdB3mN-lr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXA6d1dBNkgH",
        "outputId": "d58dd6a1-b0ae-4727-f40b-8da2c1b7ab3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User_ID                            0\n",
              "Product_ID                         0\n",
              "Gender                             0\n",
              "Age                                0\n",
              "Occupation                         0\n",
              "City_Category                      0\n",
              "Stay_In_Current_City_Years         0\n",
              "Marital_Status                     0\n",
              "Product_Category_1                 0\n",
              "Product_Category_2             72344\n",
              "Product_Category_3            162562\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los valores de NaN aparecen en Product Category. Vamos a comprobar el rango de valores para estas dos categorias."
      ],
      "metadata": {
        "id": "KifAX0FySITb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(data['Product_Category_1'].min(), data['Product_Category_1'].max(), data['Product_Category_1'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNF3BI2qQIFB",
        "outputId": "8953af4e-62f8-4dc2-c919-e66bfb9f9800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 18 5.276542279718663\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data['Product_Category_2'].min(), data['Product_Category_2'].max(), data['Product_Category_2'].mean())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZC-dd7r_RjWk",
        "outputId": "caa64c35-ab55-4011-de42-04a41a882032"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0 18.0 9.849586059346997\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data['Product_Category_3'].min(), data['Product_Category_3'].max(), data['Product_Category_3'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bj1yUTAAR3XV",
        "outputId": "79875a74-363a-4620-9633-3ae4dc762428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.0 18.0 12.669453946534905\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observamos que las dos columnas tienen valores minimos de 2 y 3 respectivamente. Por tanto los valores NaN se pueden igualar a 0 para que asi se contemplen en el analisis de los datos pero no influyan al estudio. "
      ],
      "metadata": {
        "id": "D5xJB1FrSYCb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "8DOQZ6VxOh6C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vemos si hay algún dato duplicado"
      ],
      "metadata": {
        "id": "K6y1V4vGSSSL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if any(data.duplicated()):\n",
        "  print(\"Hay datos duplicados\")\n",
        "else:\n",
        "  print(\"No hay datos duplicados\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4M4fwdekzzD7",
        "outputId": "bbd5d9a2-2882-4a4d-c189-45562155be3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No hay datos duplicados\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Prueba al menos tres configuraciones de soporte y frecuencia para cada género, edad y tipo de producto."
      ],
      "metadata": {
        "id": "oPtk1pVpHjde"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Variables categoricas a dummy \n",
        "dummy_df = pd.get_dummies(data,columns=['Product_ID','Gender','Age','Occupation','City_Category','Stay_In_Current_City_Years','Marital_Status','Product_Category_1','Product_Category_2','Product_Category_3'])\n",
        "\n",
        "# Concatenamos las variables dummy con el DataFrame original\n",
        "data4 = pd.concat([data, dummy_df], axis=1)\n",
        "\n",
        "# Drop las columnas originales. Ademas Drop de User_ID ya que es unico para cada cliente\n",
        "data4.drop(['User_ID','Product_ID','Gender','Age','Occupation','City_Category','Stay_In_Current_City_Years','Marital_Status','Product_Category_1','Product_Category_2','Product_Category_3'], axis=1, inplace=True)\n",
        "\n",
        "data4\n",
        "\n"
      ],
      "metadata": {
        "id": "qfz6zFwz2wzA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "da2b2a48-9de7-4c20-acb5-7dbf8cc627bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Product_ID_P00000142  Product_ID_P00000242  Product_ID_P00000342  \\\n",
              "0                          0                     0                     0   \n",
              "1                          0                     0                     0   \n",
              "2                          0                     0                     0   \n",
              "3                          0                     0                     0   \n",
              "4                          0                     0                     0   \n",
              "...                      ...                   ...                   ...   \n",
              "233594                     0                     0                     0   \n",
              "233595                     0                     0                     0   \n",
              "233596                     0                     0                     0   \n",
              "233597                     0                     0                     0   \n",
              "233598                     0                     0                     0   \n",
              "\n",
              "        Product_ID_P00000442  Product_ID_P00000542  Product_ID_P00000642  \\\n",
              "0                          0                     0                     0   \n",
              "1                          0                     0                     0   \n",
              "2                          0                     0                     0   \n",
              "3                          0                     0                     0   \n",
              "4                          0                     0                     0   \n",
              "...                      ...                   ...                   ...   \n",
              "233594                     0                     0                     0   \n",
              "233595                     0                     0                     0   \n",
              "233596                     0                     0                     0   \n",
              "233597                     0                     0                     0   \n",
              "233598                     0                     0                     0   \n",
              "\n",
              "        Product_ID_P00000742  Product_ID_P00000842  Product_ID_P00000942  \\\n",
              "0                          0                     0                     0   \n",
              "1                          0                     0                     0   \n",
              "2                          0                     0                     0   \n",
              "3                          0                     0                     0   \n",
              "4                          0                     0                     0   \n",
              "...                      ...                   ...                   ...   \n",
              "233594                     0                     0                     0   \n",
              "233595                     0                     0                     0   \n",
              "233596                     0                     0                     0   \n",
              "233597                     0                     0                     0   \n",
              "233598                     0                     0                     0   \n",
              "\n",
              "        Product_ID_P00001042  ...  Product_Category_3_9.0  \\\n",
              "0                          0  ...                       0   \n",
              "1                          0  ...                       0   \n",
              "2                          0  ...                       0   \n",
              "3                          0  ...                       0   \n",
              "4                          0  ...                       0   \n",
              "...                      ...  ...                     ...   \n",
              "233594                     0  ...                       0   \n",
              "233595                     0  ...                       0   \n",
              "233596                     0  ...                       0   \n",
              "233597                     0  ...                       0   \n",
              "233598                     0  ...                       0   \n",
              "\n",
              "        Product_Category_3_10.0  Product_Category_3_11.0  \\\n",
              "0                             0                        0   \n",
              "1                             0                        0   \n",
              "2                             0                        0   \n",
              "3                             0                        0   \n",
              "4                             0                        0   \n",
              "...                         ...                      ...   \n",
              "233594                        0                        0   \n",
              "233595                        0                        0   \n",
              "233596                        0                        0   \n",
              "233597                        0                        0   \n",
              "233598                        0                        0   \n",
              "\n",
              "        Product_Category_3_12.0  Product_Category_3_13.0  \\\n",
              "0                             0                        0   \n",
              "1                             0                        0   \n",
              "2                             0                        0   \n",
              "3                             0                        0   \n",
              "4                             1                        0   \n",
              "...                         ...                      ...   \n",
              "233594                        0                        0   \n",
              "233595                        0                        0   \n",
              "233596                        1                        0   \n",
              "233597                        0                        0   \n",
              "233598                        0                        0   \n",
              "\n",
              "        Product_Category_3_14.0  Product_Category_3_15.0  \\\n",
              "0                             0                        0   \n",
              "1                             0                        0   \n",
              "2                             0                        0   \n",
              "3                             0                        0   \n",
              "4                             0                        0   \n",
              "...                         ...                      ...   \n",
              "233594                        0                        0   \n",
              "233595                        0                        0   \n",
              "233596                        0                        0   \n",
              "233597                        0                        0   \n",
              "233598                        0                        0   \n",
              "\n",
              "        Product_Category_3_16.0  Product_Category_3_17.0  \\\n",
              "0                             0                        0   \n",
              "1                             0                        0   \n",
              "2                             0                        0   \n",
              "3                             0                        0   \n",
              "4                             0                        0   \n",
              "...                         ...                      ...   \n",
              "233594                        0                        0   \n",
              "233595                        0                        0   \n",
              "233596                        0                        0   \n",
              "233597                        0                        0   \n",
              "233598                        0                        0   \n",
              "\n",
              "        Product_Category_3_18.0  \n",
              "0                             0  \n",
              "1                             0  \n",
              "2                             0  \n",
              "3                             0  \n",
              "4                             0  \n",
              "...                         ...  \n",
              "233594                        0  \n",
              "233595                        0  \n",
              "233596                        0  \n",
              "233597                        0  \n",
              "233598                        0  \n",
              "\n",
              "[233599 rows x 3583 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-88130b22-cca8-44cc-aa4d-a9aa68d73a73\">\n",
              "    <div 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>Product_ID_P00000142</th>\n",
              "      <th>Product_ID_P00000242</th>\n",
              "      <th>Product_ID_P00000342</th>\n",
              "      <th>Product_ID_P00000442</th>\n",
              "      <th>Product_ID_P00000542</th>\n",
              "      <th>Product_ID_P00000642</th>\n",
              "      <th>Product_ID_P00000742</th>\n",
              "      <th>Product_ID_P00000842</th>\n",
              "      <th>Product_ID_P00000942</th>\n",
              "      <th>Product_ID_P00001042</th>\n",
              "      <th>...</th>\n",
              "      <th>Product_Category_3_9.0</th>\n",
              "      <th>Product_Category_3_10.0</th>\n",
              "      <th>Product_Category_3_11.0</th>\n",
              "      <th>Product_Category_3_12.0</th>\n",
              "      <th>Product_Category_3_13.0</th>\n",
              "      <th>Product_Category_3_14.0</th>\n",
              "      <th>Product_Category_3_15.0</th>\n",
              "      <th>Product_Category_3_16.0</th>\n",
              "      <th>Product_Category_3_17.0</th>\n",
              "      <th>Product_Category_3_18.0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233594</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233595</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233596</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233597</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233598</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>233599 rows × 3583 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88130b22-cca8-44cc-aa4d-a9aa68d73a73')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-88130b22-cca8-44cc-aa4d-a9aa68d73a73 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-88130b22-cca8-44cc-aa4d-a9aa68d73a73');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data4.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVKX_6msaKoV",
        "outputId": "699634b3-4336-41ef-9c51-bfb5a3cf7b0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Product_ID_P00000142', 'Product_ID_P00000242', 'Product_ID_P00000342',\n",
            "       'Product_ID_P00000442', 'Product_ID_P00000542', 'Product_ID_P00000642',\n",
            "       'Product_ID_P00000742', 'Product_ID_P00000842', 'Product_ID_P00000942',\n",
            "       'Product_ID_P00001042',\n",
            "       ...\n",
            "       'Product_Category_3_9.0', 'Product_Category_3_10.0',\n",
            "       'Product_Category_3_11.0', 'Product_Category_3_12.0',\n",
            "       'Product_Category_3_13.0', 'Product_Category_3_14.0',\n",
            "       'Product_Category_3_15.0', 'Product_Category_3_16.0',\n",
            "       'Product_Category_3_17.0', 'Product_Category_3_18.0'],\n",
            "      dtype='object', length=3583)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Colocamos los datos de la forma indicada para poder calcular las frecuencias de estos más adelante"
      ],
      "metadata": {
        "id": "1yTwMGy-Sf6I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1) Itemsets frecuentes para k=1"
      ],
      "metadata": {
        "id": "JzW5LCNebLOx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para hallar esto necesitamos calcular el soporte y las frecuencias de estos itemsets"
      ],
      "metadata": {
        "id": "HMaRpY_mhweb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "IR VARIANDO EL MIN_SUPPORT HASTA QUE SALE UN NÚMERO DE REGLAS RAZONABLE"
      ],
      "metadata": {
        "id": "zmSCb6P4z0NR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "frequent_itemsets = apriori(data4, min_support=0.2, use_colnames=True)\n",
        "frequent_itemsets_k1 =frequent_itemsets[frequent_itemsets['itemsets'].apply(lambda x: len(x) == 1)]\n",
        "frequent_itemsets_k1_sort = frequent_itemsets_k1.sort_values(by=['support'], ascending=False)\n",
        "\n",
        "frequent=frequent_itemsets_k1_sort.support*len(data4)\n",
        "frequent_itemsets_k1_sort['frecuencia'] =frequent\n",
        "frequent_itemsets_k1_sort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "k3-bJvTBw4le",
        "outputId": "7b943a3a-485a-4989-b6f3-4bfe168319f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     support                        itemsets  frecuencia\n",
              "1   0.752452                      (Gender_M)    175772.0\n",
              "13  0.695902        (Product_Category_3_0.0)    162562.0\n",
              "7   0.589930              (Marital_Status_0)    137807.0\n",
              "4   0.421945               (City_Category_B)     98566.0\n",
              "8   0.410070              (Marital_Status_1)     95792.0\n",
              "2   0.399950                     (Age_26-35)     93428.0\n",
              "6   0.353615  (Stay_In_Current_City_Years_1)     82604.0\n",
              "5   0.310399               (City_Category_C)     72509.0\n",
              "12  0.309693        (Product_Category_2_0.0)     72344.0\n",
              "10  0.278327          (Product_Category_1_5)     65017.0\n",
              "3   0.267655               (City_Category_A)     62524.0\n",
              "9   0.258225          (Product_Category_1_1)     60321.0\n",
              "0   0.247548                      (Gender_F)     57827.0\n",
              "11  0.207060          (Product_Category_1_8)     48369.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5020a4c-698a-4c4e-89a5-a6b55860839a\">\n",
              "    <div 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>support</th>\n",
              "      <th>itemsets</th>\n",
              "      <th>frecuencia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.752452</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>175772.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.695902</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>162562.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.589930</td>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>137807.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.421945</td>\n",
              "      <td>(City_Category_B)</td>\n",
              "      <td>98566.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.410070</td>\n",
              "      <td>(Marital_Status_1)</td>\n",
              "      <td>95792.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.399950</td>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>93428.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.353615</td>\n",
              "      <td>(Stay_In_Current_City_Years_1)</td>\n",
              "      <td>82604.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.310399</td>\n",
              "      <td>(City_Category_C)</td>\n",
              "      <td>72509.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.309693</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>72344.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.278327</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>65017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.267655</td>\n",
              "      <td>(City_Category_A)</td>\n",
              "      <td>62524.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.258225</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>60321.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.247548</td>\n",
              "      <td>(Gender_F)</td>\n",
              "      <td>57827.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.207060</td>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>48369.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5020a4c-698a-4c4e-89a5-a6b55860839a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e5020a4c-698a-4c4e-89a5-a6b55860839a 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-e5020a4c-698a-4c4e-89a5-a6b55860839a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "COMPRUEBAMOS QUE LOS VALORES SON LOS CORRESTOS\n",
        "\n",
        "SupportMale=len(Gender)/Male\n",
        "\n"
      ],
      "metadata": {
        "id": "RqbAGAxiZiz5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SupportMale=sum(data4.Gender_M==True)/len(data4.Gender_M+data4.Gender_F)\n",
        "print(SupportMale)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "day_BbcWZUYb",
        "outputId": "93c0fc04-add2-4d6d-e7d6-4b3018dd2b7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7524518512493632\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos que es correcto"
      ],
      "metadata": {
        "id": "B4MO_sVuJ594"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2) Itemsets frecuentes para k≥2."
      ],
      "metadata": {
        "id": "yFmoHTL6atkl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## a. Mostrar los itemset frecuentes candidatos y su soporte."
      ],
      "metadata": {
        "id": "v_FGqWznirUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets_k2 =frequent_itemsets[frequent_itemsets['itemsets'].apply(lambda x: len(x) == 2)]\n",
        "frequent_itemsets_k2_sort = frequent_itemsets_k2.sort_values(by=['support'], ascending=False)\n",
        "frequent=frequent_itemsets_k2_sort.support*len(data4)\n",
        "frequent_itemsets_k2_sort['frecuenciak2'] =frequent\n",
        "frequent_itemsets_k2_sort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "_aW5alB8zedh",
        "outputId": "0386ce81-a915-4f2b-ff6e-a387279af88b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     support                                           itemsets  frecuenciak2\n",
              "24  0.516963                 (Gender_M, Product_Category_3_0.0)      120762.0\n",
              "19  0.445888                       (Gender_M, Marital_Status_0)      104159.0\n",
              "32  0.408080         (Product_Category_3_0.0, Marital_Status_0)       95327.0\n",
              "16  0.316722                        (City_Category_B, Gender_M)       73986.0\n",
              "35  0.309693   (Product_Category_3_0.0, Product_Category_2_0.0)       72344.0\n",
              "14  0.307150                              (Age_26-35, Gender_M)       71750.0\n",
              "20  0.306564                       (Marital_Status_1, Gender_M)       71613.0\n",
              "28  0.296166          (City_Category_B, Product_Category_3_0.0)       69184.0\n",
              "33  0.287822         (Marital_Status_1, Product_Category_3_0.0)       67235.0\n",
              "26  0.278636                (Age_26-35, Product_Category_3_0.0)       65089.0\n",
              "18  0.259642           (Gender_M, Stay_In_Current_City_Years_1)       60652.0\n",
              "27  0.250138                (City_Category_B, Marital_Status_0)       58432.0\n",
              "31  0.246666  (Stay_In_Current_City_Years_1, Product_Categor...       57621.0\n",
              "34  0.243879     (Product_Category_1_5, Product_Category_3_0.0)       56970.0\n",
              "25  0.241885                      (Age_26-35, Marital_Status_0)       56504.0\n",
              "17  0.233640                        (Gender_M, City_Category_C)       54578.0\n",
              "23  0.230343                 (Gender_M, Product_Category_2_0.0)       53808.0\n",
              "21  0.212480                   (Product_Category_1_1, Gender_M)       49635.0\n",
              "29  0.207882          (Product_Category_3_0.0, City_Category_C)       48561.0\n",
              "15  0.202090                        (City_Category_A, Gender_M)       47208.0\n",
              "30  0.201957   (Stay_In_Current_City_Years_1, Marital_Status_0)       47177.0\n",
              "22  0.200707                   (Product_Category_1_5, Gender_M)       46885.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61e19d66-1c6d-49d0-86d2-ba8eca073f34\">\n",
              "    <div 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>support</th>\n",
              "      <th>itemsets</th>\n",
              "      <th>frecuenciak2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.516963</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>120762.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.445888</td>\n",
              "      <td>(Gender_M, Marital_Status_0)</td>\n",
              "      <td>104159.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.408080</td>\n",
              "      <td>(Product_Category_3_0.0, Marital_Status_0)</td>\n",
              "      <td>95327.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.316722</td>\n",
              "      <td>(City_Category_B, Gender_M)</td>\n",
              "      <td>73986.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.309693</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_2_0.0)</td>\n",
              "      <td>72344.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.307150</td>\n",
              "      <td>(Age_26-35, Gender_M)</td>\n",
              "      <td>71750.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.306564</td>\n",
              "      <td>(Marital_Status_1, Gender_M)</td>\n",
              "      <td>71613.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.296166</td>\n",
              "      <td>(City_Category_B, Product_Category_3_0.0)</td>\n",
              "      <td>69184.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.287822</td>\n",
              "      <td>(Marital_Status_1, Product_Category_3_0.0)</td>\n",
              "      <td>67235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.278636</td>\n",
              "      <td>(Age_26-35, Product_Category_3_0.0)</td>\n",
              "      <td>65089.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.259642</td>\n",
              "      <td>(Gender_M, Stay_In_Current_City_Years_1)</td>\n",
              "      <td>60652.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.250138</td>\n",
              "      <td>(City_Category_B, Marital_Status_0)</td>\n",
              "      <td>58432.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.246666</td>\n",
              "      <td>(Stay_In_Current_City_Years_1, Product_Categor...</td>\n",
              "      <td>57621.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.243879</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_3_0.0)</td>\n",
              "      <td>56970.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.241885</td>\n",
              "      <td>(Age_26-35, Marital_Status_0)</td>\n",
              "      <td>56504.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.233640</td>\n",
              "      <td>(Gender_M, City_Category_C)</td>\n",
              "      <td>54578.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.230343</td>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>53808.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.212480</td>\n",
              "      <td>(Product_Category_1_1, Gender_M)</td>\n",
              "      <td>49635.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.207882</td>\n",
              "      <td>(Product_Category_3_0.0, City_Category_C)</td>\n",
              "      <td>48561.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.202090</td>\n",
              "      <td>(City_Category_A, Gender_M)</td>\n",
              "      <td>47208.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.201957</td>\n",
              "      <td>(Stay_In_Current_City_Years_1, Marital_Status_0)</td>\n",
              "      <td>47177.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.200707</td>\n",
              "      <td>(Product_Category_1_5, Gender_M)</td>\n",
              "      <td>46885.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61e19d66-1c6d-49d0-86d2-ba8eca073f34')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-61e19d66-1c6d-49d0-86d2-ba8eca073f34 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-61e19d66-1c6d-49d0-86d2-ba8eca073f34');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3) Repetir el proceso 2 hasta que no se generen nuevos itemsets frecuentes."
      ],
      "metadata": {
        "id": "4MrgtzBYai2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets_k3 = frequent_itemsets[frequent_itemsets['itemsets'].apply(lambda x: len(x) == 3)]\n",
        "frequent_itemsets_k3_sort = frequent_itemsets_k3.sort_values(by=['support'], ascending=False)\n",
        "\n",
        "frequent=frequent_itemsets_k3_sort.support*len(data4)\n",
        "frequent_itemsets_k3_sort['frecuenciak3'] =frequent\n",
        "\n",
        "frequent_itemsets_k3_sort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eTgXS013eVFq",
        "outputId": "9eb084cd-5b90-45a0-924d-3b39d791e8ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     support                                           itemsets  frecuenciak3\n",
              "38  0.304248  (Gender_M, Product_Category_3_0.0, Marital_Sta...       71072.0\n",
              "40  0.230343  (Gender_M, Product_Category_3_0.0, Product_Cat...       53808.0\n",
              "37  0.219389  (City_Category_B, Gender_M, Product_Category_3...       51249.0\n",
              "39  0.212715  (Marital_Status_1, Product_Category_3_0.0, Gen...       49690.0\n",
              "36  0.210527      (Age_26-35, Gender_M, Product_Category_3_0.0)       49179.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-752694cd-de4d-41f0-9e3a-e47f12ba648a\">\n",
              "    <div 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>support</th>\n",
              "      <th>itemsets</th>\n",
              "      <th>frecuenciak3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.304248</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0, Marital_Sta...</td>\n",
              "      <td>71072.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.230343</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0, Product_Cat...</td>\n",
              "      <td>53808.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.219389</td>\n",
              "      <td>(City_Category_B, Gender_M, Product_Category_3...</td>\n",
              "      <td>51249.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.212715</td>\n",
              "      <td>(Marital_Status_1, Product_Category_3_0.0, Gen...</td>\n",
              "      <td>49690.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.210527</td>\n",
              "      <td>(Age_26-35, Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>49179.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-752694cd-de4d-41f0-9e3a-e47f12ba648a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-752694cd-de4d-41f0-9e3a-e47f12ba648a 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-752694cd-de4d-41f0-9e3a-e47f12ba648a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets_k4 = frequent_itemsets[frequent_itemsets['itemsets'].apply(lambda x: len(x) == 4)]\n",
        "frequent_itemsets_k4_sort = frequent_itemsets_k4.sort_values(by=['support'], ascending=False)\n",
        "\n",
        "frequent=frequent_itemsets_k4_sort.support*len(data4)\n",
        "frequent_itemsets_k4_sort['frecuenciak3'] =frequent\n",
        "\n",
        "frequent_itemsets_k4_sort.head()"
      ],
      "metadata": {
        "id": "wKwgjR0HXNCX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "89b4db0e-aa99-48b6-c5c4-1af9fb815428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [support, itemsets, frecuenciak3]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-621e81b9-7ac0-4b01-8e31-e45b08faea5b\">\n",
              "    <div 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>support</th>\n",
              "      <th>itemsets</th>\n",
              "      <th>frecuenciak3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-621e81b9-7ac0-4b01-8e31-e45b08faea5b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-621e81b9-7ac0-4b01-8e31-e45b08faea5b 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-621e81b9-7ac0-4b01-8e31-e45b08faea5b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observamos que para K=4 no hay mas itemsets que cumplan el requisito de tener un soporte minimo del 20%. "
      ],
      "metadata": {
        "id": "VYx37-eGu7vS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4) Mostrar todas las posibles reglas con la confianza de cada una de ellas."
      ],
      "metadata": {
        "id": "DtDT0ePvRMG9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric='confidence',  min_threshold=0.85)\n",
        "rules"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "LrQtwdolqDNa",
        "outputId": "c853c77a-0c01-481e-dbf0-8eedfd871120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          antecedents               consequents  \\\n",
              "0              (Product_Category_1_5)  (Product_Category_3_0.0)   \n",
              "1            (Product_Category_2_0.0)  (Product_Category_3_0.0)   \n",
              "2  (Gender_M, Product_Category_2_0.0)  (Product_Category_3_0.0)   \n",
              "\n",
              "   antecedent support  consequent support   support  confidence      lift  \\\n",
              "0            0.278327            0.695902  0.243879    0.876232  1.259132   \n",
              "1            0.309693            0.695902  0.309693    1.000000  1.436984   \n",
              "2            0.230343            0.695902  0.230343    1.000000  1.436984   \n",
              "\n",
              "   leverage  conviction  \n",
              "0  0.050191    2.457008  \n",
              "1  0.094177         inf  \n",
              "2  0.070047         inf  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27da1dcf-7d0b-4a0a-920c-b6abc3c4b169\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.278327</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.243879</td>\n",
              "      <td>0.876232</td>\n",
              "      <td>1.259132</td>\n",
              "      <td>0.050191</td>\n",
              "      <td>2.457008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27da1dcf-7d0b-4a0a-920c-b6abc3c4b169')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-27da1dcf-7d0b-4a0a-920c-b6abc3c4b169 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-27da1dcf-7d0b-4a0a-920c-b6abc3c4b169');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5) Listar todas las reglas que sean de alta confianza."
      ],
      "metadata": {
        "id": "UTttHoU-RYgW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1- SEGÚN SU CONFIANZA"
      ],
      "metadata": {
        "id": "CVSaVhDjc_cg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules_sort = rules.sort_values(by=['confidence'], ascending=False)\n",
        "rules_sort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "w7sG75iqxjNk",
        "outputId": "f3d2c2df-3c2b-43f8-9f1d-c9a9265fa5a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         antecedents  \\\n",
              "29                (Gender_M, Product_Category_2_0.0)   \n",
              "20                          (Product_Category_2_0.0)   \n",
              "19                            (Product_Category_1_5)   \n",
              "7                             (Product_Category_1_1)   \n",
              "0                                        (Age_26-35)   \n",
              "5                                 (Marital_Status_0)   \n",
              "22               (Age_26-35, Product_Category_3_0.0)   \n",
              "1                                  (City_Category_A)   \n",
              "3                                  (City_Category_C)   \n",
              "2                                  (City_Category_B)   \n",
              "6                                 (Marital_Status_1)   \n",
              "26        (Product_Category_3_0.0, Marital_Status_0)   \n",
              "30  (Product_Category_3_0.0, Product_Category_2_0.0)   \n",
              "31                          (Product_Category_2_0.0)   \n",
              "9                           (Product_Category_2_0.0)   \n",
              "11                          (Product_Category_3_0.0)   \n",
              "24         (City_Category_B, Product_Category_3_0.0)   \n",
              "27        (Marital_Status_1, Product_Category_3_0.0)   \n",
              "4                     (Stay_In_Current_City_Years_1)   \n",
              "8                             (Product_Category_1_5)   \n",
              "14                                 (City_Category_B)   \n",
              "18                                (Marital_Status_1)   \n",
              "16                    (Stay_In_Current_City_Years_1)   \n",
              "13                                       (Age_26-35)   \n",
              "28                      (Marital_Status_1, Gender_M)   \n",
              "23                       (City_Category_B, Gender_M)   \n",
              "17                                (Marital_Status_0)   \n",
              "10                                        (Gender_M)   \n",
              "21                             (Age_26-35, Gender_M)   \n",
              "25                      (Gender_M, Marital_Status_0)   \n",
              "15                                 (City_Category_C)   \n",
              "12                                       (Age_26-35)   \n",
              "\n",
              "                           consequents  antecedent support  \\\n",
              "29            (Product_Category_3_0.0)            0.230343   \n",
              "20            (Product_Category_3_0.0)            0.309693   \n",
              "19            (Product_Category_3_0.0)            0.278327   \n",
              "7                           (Gender_M)            0.258225   \n",
              "0                           (Gender_M)            0.399950   \n",
              "5                           (Gender_M)            0.589930   \n",
              "22                          (Gender_M)            0.278636   \n",
              "1                           (Gender_M)            0.267655   \n",
              "3                           (Gender_M)            0.310399   \n",
              "2                           (Gender_M)            0.421945   \n",
              "6                           (Gender_M)            0.410070   \n",
              "26                          (Gender_M)            0.408080   \n",
              "30                          (Gender_M)            0.309693   \n",
              "31  (Gender_M, Product_Category_3_0.0)            0.309693   \n",
              "9                           (Gender_M)            0.309693   \n",
              "11                          (Gender_M)            0.695902   \n",
              "24                          (Gender_M)            0.296166   \n",
              "27                          (Gender_M)            0.287822   \n",
              "4                           (Gender_M)            0.353615   \n",
              "8                           (Gender_M)            0.278327   \n",
              "14            (Product_Category_3_0.0)            0.421945   \n",
              "18            (Product_Category_3_0.0)            0.410070   \n",
              "16            (Product_Category_3_0.0)            0.353615   \n",
              "13            (Product_Category_3_0.0)            0.399950   \n",
              "28            (Product_Category_3_0.0)            0.306564   \n",
              "23            (Product_Category_3_0.0)            0.316722   \n",
              "17            (Product_Category_3_0.0)            0.589930   \n",
              "10            (Product_Category_3_0.0)            0.752452   \n",
              "21            (Product_Category_3_0.0)            0.307150   \n",
              "25            (Product_Category_3_0.0)            0.445888   \n",
              "15            (Product_Category_3_0.0)            0.310399   \n",
              "12                  (Marital_Status_0)            0.399950   \n",
              "\n",
              "    consequent support   support  confidence      lift  leverage  conviction  \n",
              "29            0.695902  0.230343    1.000000  1.436984  0.070047         inf  \n",
              "20            0.695902  0.309693    1.000000  1.436984  0.094177         inf  \n",
              "19            0.695902  0.243879    0.876232  1.259132  0.050191    2.457008  \n",
              "7             0.752452  0.212480    0.822848  1.093555  0.018178    1.397375  \n",
              "0             0.752452  0.307150    0.767971  1.020625  0.006207    1.066885  \n",
              "5             0.752452  0.445888    0.755832  1.004493  0.001994    1.013845  \n",
              "22            0.752452  0.210527    0.755565  1.004138  0.000868    1.012738  \n",
              "1             0.752452  0.202090    0.755038  1.003437  0.000692    1.010558  \n",
              "3             0.752452  0.233640    0.752707  1.000339  0.000079    1.001030  \n",
              "2             0.752452  0.316722    0.750624  0.997571 -0.000771    0.992670  \n",
              "6             0.752452  0.306564    0.747589  0.993537 -0.001994    0.980733  \n",
              "26            0.752452  0.304248    0.745560  0.990841 -0.002812    0.972914  \n",
              "30            0.752452  0.230343    0.743780  0.988475 -0.002686    0.966154  \n",
              "31            0.516963  0.230343    0.743780  1.438749  0.070244    1.885242  \n",
              "9             0.752452  0.230343    0.743780  0.988475 -0.002686    0.966154  \n",
              "11            0.752452  0.516963    0.742867  0.987262 -0.006670    0.962725  \n",
              "24            0.752452  0.219389    0.740764  0.984467 -0.003462    0.954913  \n",
              "27            0.752452  0.212715    0.739050  0.982189 -0.003857    0.948641  \n",
              "4             0.752452  0.259642    0.734250  0.975810 -0.006436    0.931508  \n",
              "8             0.752452  0.200707    0.721119  0.958359 -0.008721    0.887648  \n",
              "14            0.695902  0.296166    0.701905  1.008627  0.002533    1.020139  \n",
              "18            0.695902  0.287822    0.701885  1.008598  0.002454    1.020071  \n",
              "16            0.695902  0.246666    0.697557  1.002378  0.000585    1.005472  \n",
              "13            0.695902  0.278636    0.696676  1.001112  0.000309    1.002550  \n",
              "28            0.695902  0.212715    0.693868  0.997078 -0.000623    0.993357  \n",
              "23            0.695902  0.219389    0.692685  0.995377 -0.001019    0.989532  \n",
              "17            0.695902  0.408080    0.691743  0.994023 -0.002454    0.986508  \n",
              "10            0.695902  0.516963    0.687038  0.987262 -0.006670    0.971676  \n",
              "21            0.695902  0.210527    0.685422  0.984940 -0.003219    0.966684  \n",
              "25            0.695902  0.304248    0.682341  0.980514 -0.006046    0.957311  \n",
              "15            0.695902  0.207882    0.669724  0.962382 -0.008126    0.920738  \n",
              "12            0.589930  0.241885    0.604787  1.025184  0.005942    1.037592  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3cbfbe41-517b-4aa2-9ab9-b8700c6eb023\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.278327</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.243879</td>\n",
              "      <td>0.876232</td>\n",
              "      <td>1.259132</td>\n",
              "      <td>0.050191</td>\n",
              "      <td>2.457008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.258225</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.212480</td>\n",
              "      <td>0.822848</td>\n",
              "      <td>1.093555</td>\n",
              "      <td>0.018178</td>\n",
              "      <td>1.397375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.767971</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.066885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.589930</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.445888</td>\n",
              "      <td>0.755832</td>\n",
              "      <td>1.004493</td>\n",
              "      <td>0.001994</td>\n",
              "      <td>1.013845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>(Age_26-35, Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.278636</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.210527</td>\n",
              "      <td>0.755565</td>\n",
              "      <td>1.004138</td>\n",
              "      <td>0.000868</td>\n",
              "      <td>1.012738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(City_Category_A)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.267655</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.202090</td>\n",
              "      <td>0.755038</td>\n",
              "      <td>1.003437</td>\n",
              "      <td>0.000692</td>\n",
              "      <td>1.010558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(City_Category_C)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.310399</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.233640</td>\n",
              "      <td>0.752707</td>\n",
              "      <td>1.000339</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>1.001030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(City_Category_B)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.421945</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.316722</td>\n",
              "      <td>0.750624</td>\n",
              "      <td>0.997571</td>\n",
              "      <td>-0.000771</td>\n",
              "      <td>0.992670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(Marital_Status_1)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.410070</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.306564</td>\n",
              "      <td>0.747589</td>\n",
              "      <td>0.993537</td>\n",
              "      <td>-0.001994</td>\n",
              "      <td>0.980733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>(Product_Category_3_0.0, Marital_Status_0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.408080</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.304248</td>\n",
              "      <td>0.745560</td>\n",
              "      <td>0.990841</td>\n",
              "      <td>-0.002812</td>\n",
              "      <td>0.972914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>0.988475</td>\n",
              "      <td>-0.002686</td>\n",
              "      <td>0.966154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>1.438749</td>\n",
              "      <td>0.070244</td>\n",
              "      <td>1.885242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>0.988475</td>\n",
              "      <td>-0.002686</td>\n",
              "      <td>0.966154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.742867</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.962725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(City_Category_B, Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.296166</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.219389</td>\n",
              "      <td>0.740764</td>\n",
              "      <td>0.984467</td>\n",
              "      <td>-0.003462</td>\n",
              "      <td>0.954913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>(Marital_Status_1, Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.287822</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.212715</td>\n",
              "      <td>0.739050</td>\n",
              "      <td>0.982189</td>\n",
              "      <td>-0.003857</td>\n",
              "      <td>0.948641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Stay_In_Current_City_Years_1)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.353615</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.259642</td>\n",
              "      <td>0.734250</td>\n",
              "      <td>0.975810</td>\n",
              "      <td>-0.006436</td>\n",
              "      <td>0.931508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.278327</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.200707</td>\n",
              "      <td>0.721119</td>\n",
              "      <td>0.958359</td>\n",
              "      <td>-0.008721</td>\n",
              "      <td>0.887648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(City_Category_B)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.421945</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.296166</td>\n",
              "      <td>0.701905</td>\n",
              "      <td>1.008627</td>\n",
              "      <td>0.002533</td>\n",
              "      <td>1.020139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(Marital_Status_1)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.410070</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.287822</td>\n",
              "      <td>0.701885</td>\n",
              "      <td>1.008598</td>\n",
              "      <td>0.002454</td>\n",
              "      <td>1.020071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(Stay_In_Current_City_Years_1)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.353615</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.246666</td>\n",
              "      <td>0.697557</td>\n",
              "      <td>1.002378</td>\n",
              "      <td>0.000585</td>\n",
              "      <td>1.005472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.278636</td>\n",
              "      <td>0.696676</td>\n",
              "      <td>1.001112</td>\n",
              "      <td>0.000309</td>\n",
              "      <td>1.002550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>(Marital_Status_1, Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.306564</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.212715</td>\n",
              "      <td>0.693868</td>\n",
              "      <td>0.997078</td>\n",
              "      <td>-0.000623</td>\n",
              "      <td>0.993357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(City_Category_B, Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.316722</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.219389</td>\n",
              "      <td>0.692685</td>\n",
              "      <td>0.995377</td>\n",
              "      <td>-0.001019</td>\n",
              "      <td>0.989532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.589930</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.408080</td>\n",
              "      <td>0.691743</td>\n",
              "      <td>0.994023</td>\n",
              "      <td>-0.002454</td>\n",
              "      <td>0.986508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.687038</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.971676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>(Age_26-35, Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.210527</td>\n",
              "      <td>0.685422</td>\n",
              "      <td>0.984940</td>\n",
              "      <td>-0.003219</td>\n",
              "      <td>0.966684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>(Gender_M, Marital_Status_0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.445888</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.304248</td>\n",
              "      <td>0.682341</td>\n",
              "      <td>0.980514</td>\n",
              "      <td>-0.006046</td>\n",
              "      <td>0.957311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(City_Category_C)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.310399</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.207882</td>\n",
              "      <td>0.669724</td>\n",
              "      <td>0.962382</td>\n",
              "      <td>-0.008126</td>\n",
              "      <td>0.920738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.589930</td>\n",
              "      <td>0.241885</td>\n",
              "      <td>0.604787</td>\n",
              "      <td>1.025184</td>\n",
              "      <td>0.005942</td>\n",
              "      <td>1.037592</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cbfbe41-517b-4aa2-9ab9-b8700c6eb023')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3cbfbe41-517b-4aa2-9ab9-b8700c6eb023 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-3cbfbe41-517b-4aa2-9ab9-b8700c6eb023');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6) Dado un antecesor, devolver todas las reglas que contengan a dicho antecesor. \n"
      ],
      "metadata": {
        "id": "yEfhKS7AaAPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "antereg=rules_sort[rules_sort['antecedents'].apply(lambda x: 'Age_26-35' in x)]\n",
        "antereg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "mhu2sSDAtEwu",
        "outputId": "3dc73e3c-d528-458a-9618-39c6254a76d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            antecedents                         consequents  \\\n",
              "22                          (Age_26-35)                  (Marital_Status_0)   \n",
              "0                           (Age_26-35)                          (Gender_M)   \n",
              "47                          (Age_26-35)  (Gender_M, Product_Category_3_0.0)   \n",
              "45  (Age_26-35, Product_Category_3_0.0)                          (Gender_M)   \n",
              "24                          (Age_26-35)            (Product_Category_3_0.0)   \n",
              "44                (Age_26-35, Gender_M)            (Product_Category_3_0.0)   \n",
              "\n",
              "    antecedent support  consequent support   support  confidence      lift  \\\n",
              "22            0.399950            0.589930  0.241885    0.604787  1.025184   \n",
              "0             0.399950            0.752452  0.307150    0.767971  1.020625   \n",
              "47            0.399950            0.516963  0.210527    0.526384  1.018224   \n",
              "45            0.278636            0.752452  0.210527    0.755565  1.004138   \n",
              "24            0.399950            0.695902  0.278636    0.696676  1.001112   \n",
              "44            0.307150            0.695902  0.210527    0.685422  0.984940   \n",
              "\n",
              "    leverage  conviction  \n",
              "22  0.005942    1.037592  \n",
              "0   0.006207    1.066885  \n",
              "47  0.003768    1.019892  \n",
              "45  0.000868    1.012738  \n",
              "24  0.000309    1.002550  \n",
              "44 -0.003219    0.966684  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cc25ca2-6688-4770-b2d8-c93084cd41ec\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.589930</td>\n",
              "      <td>0.241885</td>\n",
              "      <td>0.604787</td>\n",
              "      <td>1.025184</td>\n",
              "      <td>0.005942</td>\n",
              "      <td>1.037592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.767971</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.066885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.210527</td>\n",
              "      <td>0.526384</td>\n",
              "      <td>1.018224</td>\n",
              "      <td>0.003768</td>\n",
              "      <td>1.019892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>(Age_26-35, Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.278636</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.210527</td>\n",
              "      <td>0.755565</td>\n",
              "      <td>1.004138</td>\n",
              "      <td>0.000868</td>\n",
              "      <td>1.012738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.278636</td>\n",
              "      <td>0.696676</td>\n",
              "      <td>1.001112</td>\n",
              "      <td>0.000309</td>\n",
              "      <td>1.002550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>(Age_26-35, Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.210527</td>\n",
              "      <td>0.685422</td>\n",
              "      <td>0.984940</td>\n",
              "      <td>-0.003219</td>\n",
              "      <td>0.966684</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cc25ca2-6688-4770-b2d8-c93084cd41ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cc25ca2-6688-4770-b2d8-c93084cd41ec 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-2cc25ca2-6688-4770-b2d8-c93084cd41ec');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ESTO PORQUE LO HACES?"
      ],
      "metadata": {
        "id": "ih0_mp9Uh5aN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if any(rules_sort['antecedents']=={'Medio'})>0:\n",
        "  apariciones_antecedente= sum(rules_sort['antecedents'] =={'Medio'})\n",
        "  print(\"Número de veces que está: \", apariciones_antecedente)\n",
        "else:\n",
        "  print(\"NO esta\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRZl7qWgtTwk",
        "outputId": "02363512-61b9-4779-886c-7de5730ad2ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NO esta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(rules_sort)):\n",
        "  if rules_sort['antecedents'][i] == {'Medio'}:\n",
        "    print(rules_sort.iloc[i])"
      ],
      "metadata": {
        "id": "zViAuTVUwMOd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.2) Dado un umbral mínimo devolver todas las reglas que cumplan con dicha confianza."
      ],
      "metadata": {
        "id": "YAZe0oECcI8V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric='confidence', min_threshold=0.6)\n",
        "rules"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "C9RRCW_ydzRC",
        "outputId": "cf6a887c-56df-48f4-f8fd-2b9939629804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         antecedents  \\\n",
              "0                                        (Age_26-35)   \n",
              "1                                  (City_Category_A)   \n",
              "2                                  (City_Category_B)   \n",
              "3                                  (City_Category_C)   \n",
              "4                     (Stay_In_Current_City_Years_1)   \n",
              "5                                 (Marital_Status_0)   \n",
              "6                                 (Marital_Status_1)   \n",
              "7                             (Product_Category_1_1)   \n",
              "8                             (Product_Category_1_5)   \n",
              "9                           (Product_Category_2_0.0)   \n",
              "10                                        (Gender_M)   \n",
              "11                          (Product_Category_3_0.0)   \n",
              "12                                       (Age_26-35)   \n",
              "13                                       (Age_26-35)   \n",
              "14                                 (City_Category_B)   \n",
              "15                                 (City_Category_C)   \n",
              "16                    (Stay_In_Current_City_Years_1)   \n",
              "17                                (Marital_Status_0)   \n",
              "18                                (Marital_Status_1)   \n",
              "19                            (Product_Category_1_5)   \n",
              "20                          (Product_Category_2_0.0)   \n",
              "21                             (Age_26-35, Gender_M)   \n",
              "22               (Age_26-35, Product_Category_3_0.0)   \n",
              "23                       (City_Category_B, Gender_M)   \n",
              "24         (City_Category_B, Product_Category_3_0.0)   \n",
              "25                      (Gender_M, Marital_Status_0)   \n",
              "26        (Product_Category_3_0.0, Marital_Status_0)   \n",
              "27        (Marital_Status_1, Product_Category_3_0.0)   \n",
              "28                      (Marital_Status_1, Gender_M)   \n",
              "29                (Gender_M, Product_Category_2_0.0)   \n",
              "30  (Product_Category_3_0.0, Product_Category_2_0.0)   \n",
              "31                          (Product_Category_2_0.0)   \n",
              "\n",
              "                           consequents  antecedent support  \\\n",
              "0                           (Gender_M)            0.399950   \n",
              "1                           (Gender_M)            0.267655   \n",
              "2                           (Gender_M)            0.421945   \n",
              "3                           (Gender_M)            0.310399   \n",
              "4                           (Gender_M)            0.353615   \n",
              "5                           (Gender_M)            0.589930   \n",
              "6                           (Gender_M)            0.410070   \n",
              "7                           (Gender_M)            0.258225   \n",
              "8                           (Gender_M)            0.278327   \n",
              "9                           (Gender_M)            0.309693   \n",
              "10            (Product_Category_3_0.0)            0.752452   \n",
              "11                          (Gender_M)            0.695902   \n",
              "12                  (Marital_Status_0)            0.399950   \n",
              "13            (Product_Category_3_0.0)            0.399950   \n",
              "14            (Product_Category_3_0.0)            0.421945   \n",
              "15            (Product_Category_3_0.0)            0.310399   \n",
              "16            (Product_Category_3_0.0)            0.353615   \n",
              "17            (Product_Category_3_0.0)            0.589930   \n",
              "18            (Product_Category_3_0.0)            0.410070   \n",
              "19            (Product_Category_3_0.0)            0.278327   \n",
              "20            (Product_Category_3_0.0)            0.309693   \n",
              "21            (Product_Category_3_0.0)            0.307150   \n",
              "22                          (Gender_M)            0.278636   \n",
              "23            (Product_Category_3_0.0)            0.316722   \n",
              "24                          (Gender_M)            0.296166   \n",
              "25            (Product_Category_3_0.0)            0.445888   \n",
              "26                          (Gender_M)            0.408080   \n",
              "27                          (Gender_M)            0.287822   \n",
              "28            (Product_Category_3_0.0)            0.306564   \n",
              "29            (Product_Category_3_0.0)            0.230343   \n",
              "30                          (Gender_M)            0.309693   \n",
              "31  (Gender_M, Product_Category_3_0.0)            0.309693   \n",
              "\n",
              "    consequent support   support  confidence      lift  leverage  conviction  \n",
              "0             0.752452  0.307150    0.767971  1.020625  0.006207    1.066885  \n",
              "1             0.752452  0.202090    0.755038  1.003437  0.000692    1.010558  \n",
              "2             0.752452  0.316722    0.750624  0.997571 -0.000771    0.992670  \n",
              "3             0.752452  0.233640    0.752707  1.000339  0.000079    1.001030  \n",
              "4             0.752452  0.259642    0.734250  0.975810 -0.006436    0.931508  \n",
              "5             0.752452  0.445888    0.755832  1.004493  0.001994    1.013845  \n",
              "6             0.752452  0.306564    0.747589  0.993537 -0.001994    0.980733  \n",
              "7             0.752452  0.212480    0.822848  1.093555  0.018178    1.397375  \n",
              "8             0.752452  0.200707    0.721119  0.958359 -0.008721    0.887648  \n",
              "9             0.752452  0.230343    0.743780  0.988475 -0.002686    0.966154  \n",
              "10            0.695902  0.516963    0.687038  0.987262 -0.006670    0.971676  \n",
              "11            0.752452  0.516963    0.742867  0.987262 -0.006670    0.962725  \n",
              "12            0.589930  0.241885    0.604787  1.025184  0.005942    1.037592  \n",
              "13            0.695902  0.278636    0.696676  1.001112  0.000309    1.002550  \n",
              "14            0.695902  0.296166    0.701905  1.008627  0.002533    1.020139  \n",
              "15            0.695902  0.207882    0.669724  0.962382 -0.008126    0.920738  \n",
              "16            0.695902  0.246666    0.697557  1.002378  0.000585    1.005472  \n",
              "17            0.695902  0.408080    0.691743  0.994023 -0.002454    0.986508  \n",
              "18            0.695902  0.287822    0.701885  1.008598  0.002454    1.020071  \n",
              "19            0.695902  0.243879    0.876232  1.259132  0.050191    2.457008  \n",
              "20            0.695902  0.309693    1.000000  1.436984  0.094177         inf  \n",
              "21            0.695902  0.210527    0.685422  0.984940 -0.003219    0.966684  \n",
              "22            0.752452  0.210527    0.755565  1.004138  0.000868    1.012738  \n",
              "23            0.695902  0.219389    0.692685  0.995377 -0.001019    0.989532  \n",
              "24            0.752452  0.219389    0.740764  0.984467 -0.003462    0.954913  \n",
              "25            0.695902  0.304248    0.682341  0.980514 -0.006046    0.957311  \n",
              "26            0.752452  0.304248    0.745560  0.990841 -0.002812    0.972914  \n",
              "27            0.752452  0.212715    0.739050  0.982189 -0.003857    0.948641  \n",
              "28            0.695902  0.212715    0.693868  0.997078 -0.000623    0.993357  \n",
              "29            0.695902  0.230343    1.000000  1.436984  0.070047         inf  \n",
              "30            0.752452  0.230343    0.743780  0.988475 -0.002686    0.966154  \n",
              "31            0.516963  0.230343    0.743780  1.438749  0.070244    1.885242  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b5a9b86b-b129-40d1-bbea-2dbf87bf3b1a\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.767971</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.066885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(City_Category_A)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.267655</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.202090</td>\n",
              "      <td>0.755038</td>\n",
              "      <td>1.003437</td>\n",
              "      <td>0.000692</td>\n",
              "      <td>1.010558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(City_Category_B)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.421945</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.316722</td>\n",
              "      <td>0.750624</td>\n",
              "      <td>0.997571</td>\n",
              "      <td>-0.000771</td>\n",
              "      <td>0.992670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(City_Category_C)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.310399</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.233640</td>\n",
              "      <td>0.752707</td>\n",
              "      <td>1.000339</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>1.001030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Stay_In_Current_City_Years_1)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.353615</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.259642</td>\n",
              "      <td>0.734250</td>\n",
              "      <td>0.975810</td>\n",
              "      <td>-0.006436</td>\n",
              "      <td>0.931508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.589930</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.445888</td>\n",
              "      <td>0.755832</td>\n",
              "      <td>1.004493</td>\n",
              "      <td>0.001994</td>\n",
              "      <td>1.013845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(Marital_Status_1)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.410070</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.306564</td>\n",
              "      <td>0.747589</td>\n",
              "      <td>0.993537</td>\n",
              "      <td>-0.001994</td>\n",
              "      <td>0.980733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.258225</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.212480</td>\n",
              "      <td>0.822848</td>\n",
              "      <td>1.093555</td>\n",
              "      <td>0.018178</td>\n",
              "      <td>1.397375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.278327</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.200707</td>\n",
              "      <td>0.721119</td>\n",
              "      <td>0.958359</td>\n",
              "      <td>-0.008721</td>\n",
              "      <td>0.887648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>0.988475</td>\n",
              "      <td>-0.002686</td>\n",
              "      <td>0.966154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.687038</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.971676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.742867</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.962725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.589930</td>\n",
              "      <td>0.241885</td>\n",
              "      <td>0.604787</td>\n",
              "      <td>1.025184</td>\n",
              "      <td>0.005942</td>\n",
              "      <td>1.037592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.278636</td>\n",
              "      <td>0.696676</td>\n",
              "      <td>1.001112</td>\n",
              "      <td>0.000309</td>\n",
              "      <td>1.002550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(City_Category_B)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.421945</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.296166</td>\n",
              "      <td>0.701905</td>\n",
              "      <td>1.008627</td>\n",
              "      <td>0.002533</td>\n",
              "      <td>1.020139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(City_Category_C)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.310399</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.207882</td>\n",
              "      <td>0.669724</td>\n",
              "      <td>0.962382</td>\n",
              "      <td>-0.008126</td>\n",
              "      <td>0.920738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(Stay_In_Current_City_Years_1)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.353615</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.246666</td>\n",
              "      <td>0.697557</td>\n",
              "      <td>1.002378</td>\n",
              "      <td>0.000585</td>\n",
              "      <td>1.005472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.589930</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.408080</td>\n",
              "      <td>0.691743</td>\n",
              "      <td>0.994023</td>\n",
              "      <td>-0.002454</td>\n",
              "      <td>0.986508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(Marital_Status_1)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.410070</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.287822</td>\n",
              "      <td>0.701885</td>\n",
              "      <td>1.008598</td>\n",
              "      <td>0.002454</td>\n",
              "      <td>1.020071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.278327</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.243879</td>\n",
              "      <td>0.876232</td>\n",
              "      <td>1.259132</td>\n",
              "      <td>0.050191</td>\n",
              "      <td>2.457008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>(Age_26-35, Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.210527</td>\n",
              "      <td>0.685422</td>\n",
              "      <td>0.984940</td>\n",
              "      <td>-0.003219</td>\n",
              "      <td>0.966684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>(Age_26-35, Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.278636</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.210527</td>\n",
              "      <td>0.755565</td>\n",
              "      <td>1.004138</td>\n",
              "      <td>0.000868</td>\n",
              "      <td>1.012738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(City_Category_B, Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.316722</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.219389</td>\n",
              "      <td>0.692685</td>\n",
              "      <td>0.995377</td>\n",
              "      <td>-0.001019</td>\n",
              "      <td>0.989532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(City_Category_B, Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.296166</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.219389</td>\n",
              "      <td>0.740764</td>\n",
              "      <td>0.984467</td>\n",
              "      <td>-0.003462</td>\n",
              "      <td>0.954913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>(Gender_M, Marital_Status_0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.445888</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.304248</td>\n",
              "      <td>0.682341</td>\n",
              "      <td>0.980514</td>\n",
              "      <td>-0.006046</td>\n",
              "      <td>0.957311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>(Product_Category_3_0.0, Marital_Status_0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.408080</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.304248</td>\n",
              "      <td>0.745560</td>\n",
              "      <td>0.990841</td>\n",
              "      <td>-0.002812</td>\n",
              "      <td>0.972914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>(Marital_Status_1, Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.287822</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.212715</td>\n",
              "      <td>0.739050</td>\n",
              "      <td>0.982189</td>\n",
              "      <td>-0.003857</td>\n",
              "      <td>0.948641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>(Marital_Status_1, Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.306564</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.212715</td>\n",
              "      <td>0.693868</td>\n",
              "      <td>0.997078</td>\n",
              "      <td>-0.000623</td>\n",
              "      <td>0.993357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>0.988475</td>\n",
              "      <td>-0.002686</td>\n",
              "      <td>0.966154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>1.438749</td>\n",
              "      <td>0.070244</td>\n",
              "      <td>1.885242</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5a9b86b-b129-40d1-bbea-2dbf87bf3b1a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5a9b86b-b129-40d1-bbea-2dbf87bf3b1a 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-b5a9b86b-b129-40d1-bbea-2dbf87bf3b1a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7) Representar gráficamente las reglas obtenidas\n"
      ],
      "metadata": {
        "id": "tTEjhbREcSWZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Es interesante analizar las siguientes graficas:\n",
        "\n",
        "Soporte vs. Cantidad de elementos: Con esta grafica podemos representar el soporte de cada conjunto de elementos de diferentes tamaños. Puede ayudar a identificar los conjuntos de elementos más frecuentes y sus tamaños.\n",
        "\n",
        "Confianza vs. soporte: Este gráfico muestra la confianza de las reglas de asociación frente a su soporte. Puede ayudar a identificar las reglas que son de alta calidad y tienen un alto soporte.\n",
        "\n",
        "Lift vs. soporte: Este puede ayudar a identificar las reglas que tienen mayor impacto significativo en la venta de los elementos.\n"
      ],
      "metadata": {
        "id": "UcPECgd-SC-_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(frequent_itemsets.support, frequent_itemsets.itemsets.apply(lambda x: len(x)))\n",
        "plt.xlabel('Soporte')\n",
        "plt.ylabel('Cantidad de elementos')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "5TmDLrZH0ZM1",
        "outputId": "2b591590-dcf5-40c3-e4af-cbb8f9683d0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con esta grafica de Soporte vs Cantidad de elementos vemos la cantidad de reglas para cada uno de los k de los itemsets y sus respectivos valores de soporte. Se ve que el mejor soporte lo da los k=1."
      ],
      "metadata": {
        "id": "P5k1Q0GO1Oce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('rules.support')\n",
        "plt.ylabel('rules.condidence')\n",
        "plt.scatter(rules.support, rules.confidence)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "JouNKaTQYjCV",
        "outputId": "e5b25fc6-2ab8-4c26-a0a1-f3d4d5d29dd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4e986aa880>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('rules.support')\n",
        "plt.ylabel('rules.condidence')\n",
        "plt.plot(rules['lift'],rules['support'], '*')  \n",
        "plt.plot(rules.lift,rules.support,'*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "x-PqBWTJ3lVf",
        "outputId": "7496e142-9746-4156-bc0c-4303fc8cc515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4e983efe50>]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.1) Obtener conclusiones a partir de estas"
      ],
      "metadata": {
        "id": "mtqmFmXKcrW8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Prueba al menos tres configuraciones de soporte y frecuencia para cada género, edad y tipo de producto."
      ],
      "metadata": {
        "id": "u46w4640ZZ5n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lista de variables dummy \n",
        "dummy_vars = list(data4.columns)\n",
        "#Filtramos para genero, edad y tipo de producto\n",
        "age_vars = [var for var in dummy_vars if var.startswith('Age')]\n",
        "age_data = data4[age_vars]\n",
        "gender_vars = [var for var in dummy_vars if var.startswith('Gender')]\n",
        "gender_data = data4[gender_vars]\n",
        "pc1_vars = [var for var in dummy_vars if var.startswith('Product_Category_1')]\n",
        "pc1_data = data4[pc1_vars]\n",
        "pc2_vars = [var for var in dummy_vars if var.startswith('Product_Category_2')]\n",
        "pc2_data = data4[pc2_vars]\n",
        "pc3_vars = [var for var in dummy_vars if var.startswith('Product_Category_3')]\n",
        "pc3_data = data4[pc3_vars]"
      ],
      "metadata": {
        "id": "--VTITrtgE20"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataq1 = pd.concat([age_data, gender_data, pc1_data, pc2_data, pc3_data], axis = 1)\n",
        "frq_items_q1 = apriori(dataq1, min_support = 0.3, use_colnames = True)\n"
      ],
      "metadata": {
        "id": "1WHlIik3ZaGm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frq_items_q1_25 = frq_items_q1[frq_items_q1[\"support\"].apply(lambda x: 0.25 <= x)]\n",
        "frq_items_q1_50 = frq_items_q1[frq_items_q1[\"support\"].apply(lambda x: 0.50 <= x)]\n",
        "frq_items_q1_75 = frq_items_q1[frq_items_q1[\"support\"].apply(lambda x: 0.75 <= x)]"
      ],
      "metadata": {
        "id": "ODHFLkyef8fT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frq_items_q1_25"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "D379FSV8tOja",
        "outputId": "d7b97f46-5406-46ba-fa31-894f81eca949"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    support                                          itemsets\n",
              "0  0.399950                                       (Age_26-35)\n",
              "1  0.752452                                        (Gender_M)\n",
              "2  0.309693                          (Product_Category_2_0.0)\n",
              "3  0.695902                          (Product_Category_3_0.0)\n",
              "4  0.307150                             (Age_26-35, Gender_M)\n",
              "5  0.516963                (Gender_M, Product_Category_3_0.0)\n",
              "6  0.309693  (Product_Category_3_0.0, Product_Category_2_0.0)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a74d161c-7a48-4767-b352-407fa3067c53\">\n",
              "    <div 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>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.399950</td>\n",
              "      <td>(Age_26-35)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.752452</td>\n",
              "      <td>(Gender_M)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.309693</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.695902</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.307150</td>\n",
              "      <td>(Age_26-35, Gender_M)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.516963</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.309693</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_2_0.0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a74d161c-7a48-4767-b352-407fa3067c53')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a74d161c-7a48-4767-b352-407fa3067c53 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-a74d161c-7a48-4767-b352-407fa3067c53');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frq_items_q1_50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "kbhaSvOFtPo3",
        "outputId": "0b52a170-29e9-48d6-9e69-6fde2486e33f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    support                            itemsets\n",
              "1  0.752452                          (Gender_M)\n",
              "3  0.695902            (Product_Category_3_0.0)\n",
              "5  0.516963  (Gender_M, Product_Category_3_0.0)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-471f8a10-8089-4565-b1ba-bf2248e11e2a\">\n",
              "    <div 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>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.752452</td>\n",
              "      <td>(Gender_M)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.695902</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.516963</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-471f8a10-8089-4565-b1ba-bf2248e11e2a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-471f8a10-8089-4565-b1ba-bf2248e11e2a 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-471f8a10-8089-4565-b1ba-bf2248e11e2a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frq_items_q1_75"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "YI0YIg_7tPsS",
        "outputId": "207cf985-b0d1-4fd6-ae2e-fdb95c15912d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    support    itemsets\n",
              "1  0.752452  (Gender_M)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1cfceaeb-35fe-4874-a7e7-f8b2e55431f0\">\n",
              "    <div 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>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.752452</td>\n",
              "      <td>(Gender_M)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1cfceaeb-35fe-4874-a7e7-f8b2e55431f0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1cfceaeb-35fe-4874-a7e7-f8b2e55431f0 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-1cfceaeb-35fe-4874-a7e7-f8b2e55431f0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "¿Qué tipo reglas desaparecen según la configuración y categorías (género, edad y tipo de producto)\n",
        "consideradas? ¿Por qué?"
      ],
      "metadata": {
        "id": "L_x9meTy4s6T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules_25 = association_rules(frq_items_q1_25, metric = 'confidence', min_threshold=0.51)\n",
        "rules_25"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "JmebiAFg4vKO",
        "outputId": "6268c2e4-dfa7-4f72-c511-3664227c564b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                antecedents               consequents  antecedent support  \\\n",
              "0               (Age_26-35)                (Gender_M)            0.399950   \n",
              "1                (Gender_M)  (Product_Category_3_0.0)            0.752452   \n",
              "2  (Product_Category_3_0.0)                (Gender_M)            0.695902   \n",
              "3  (Product_Category_2_0.0)  (Product_Category_3_0.0)            0.309693   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "0            0.752452  0.307150    0.767971  1.020625  0.006207    1.066885  \n",
              "1            0.695902  0.516963    0.687038  0.987262 -0.006670    0.971676  \n",
              "2            0.752452  0.516963    0.742867  0.987262 -0.006670    0.962725  \n",
              "3            0.695902  0.309693    1.000000  1.436984  0.094177         inf  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-747662ef-e418-48a4-938f-ca4a41ee5a0c\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.767971</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.066885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.687038</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.971676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.742867</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.962725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-747662ef-e418-48a4-938f-ca4a41ee5a0c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-747662ef-e418-48a4-938f-ca4a41ee5a0c 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-747662ef-e418-48a4-938f-ca4a41ee5a0c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_50 = association_rules(frq_items_q1_50, metric = 'confidence', min_threshold=0.51)\n",
        "rules_50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "THjZwoSM45m5",
        "outputId": "f336d8fd-4598-486e-a675-3dd1ef1002bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                antecedents               consequents  antecedent support  \\\n",
              "0                (Gender_M)  (Product_Category_3_0.0)            0.752452   \n",
              "1  (Product_Category_3_0.0)                (Gender_M)            0.695902   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "0            0.695902  0.516963    0.687038  0.987262  -0.00667    0.971676  \n",
              "1            0.752452  0.516963    0.742867  0.987262  -0.00667    0.962725  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38924392-8ebb-43fb-9877-ac759e3b97a1\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.687038</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.00667</td>\n",
              "      <td>0.971676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.742867</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.00667</td>\n",
              "      <td>0.962725</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38924392-8ebb-43fb-9877-ac759e3b97a1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-38924392-8ebb-43fb-9877-ac759e3b97a1 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-38924392-8ebb-43fb-9877-ac759e3b97a1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_75 = association_rules(frq_items_q1_75, metric = 'confidence', min_threshold=0.51)\n",
        "rules_75"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "f39t2xq249rv",
        "outputId": "a37bf466-c8ef-4cf3-8b43-6123ee57b06a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09888afd-b10c-45d9-a4d0-a3222698d602\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09888afd-b10c-45d9-a4d0-a3222698d602')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-09888afd-b10c-45d9-a4d0-a3222698d602 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-09888afd-b10c-45d9-a4d0-a3222698d602');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para una de las configuraciones, interpreta algunas de las reglas que te hayan resultado interesantes\n",
        "obtenidas usando la clase association_rules y un par de configuraciones cambiando la variable metric y\n",
        "min_threshold. Justifica los resultados"
      ],
      "metadata": {
        "id": "gYILDGgd5VPv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules_25_lift = association_rules(frq_items_q1_25, metric = 'lift', min_threshold=0.51)\n",
        "rules_25_lift"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "bha3W0fZ5VZc",
        "outputId": "a4642762-9b05-4361-e19b-4ab6729e05b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                antecedents               consequents  antecedent support  \\\n",
              "0               (Age_26-35)                (Gender_M)            0.399950   \n",
              "1                (Gender_M)               (Age_26-35)            0.752452   \n",
              "2                (Gender_M)  (Product_Category_3_0.0)            0.752452   \n",
              "3  (Product_Category_3_0.0)                (Gender_M)            0.695902   \n",
              "4  (Product_Category_3_0.0)  (Product_Category_2_0.0)            0.695902   \n",
              "5  (Product_Category_2_0.0)  (Product_Category_3_0.0)            0.309693   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "0            0.752452  0.307150    0.767971  1.020625  0.006207    1.066885  \n",
              "1            0.399950  0.307150    0.408199  1.020625  0.006207    1.013939  \n",
              "2            0.695902  0.516963    0.687038  0.987262 -0.006670    0.971676  \n",
              "3            0.752452  0.516963    0.742867  0.987262 -0.006670    0.962725  \n",
              "4            0.309693  0.309693    0.445024  1.436984  0.094177    1.243850  \n",
              "5            0.695902  0.309693    1.000000  1.436984  0.094177         inf  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-851675bc-6827-4fe2-975a-3fc7adaa326b\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.767971</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.066885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.408199</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.013939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.687038</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.971676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.742867</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.962725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.445024</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>1.243850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-851675bc-6827-4fe2-975a-3fc7adaa326b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-851675bc-6827-4fe2-975a-3fc7adaa326b 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-851675bc-6827-4fe2-975a-3fc7adaa326b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_25_conviction = association_rules(frq_items_q1_25, metric = 'conviction', min_threshold=0.51)\n",
        "rules_25_conviction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "dRUrdGo25r3l",
        "outputId": "7febbecf-6d35-4c67-d3ce-623cc29ed203"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                antecedents               consequents  antecedent support  \\\n",
              "0               (Age_26-35)                (Gender_M)            0.399950   \n",
              "1                (Gender_M)               (Age_26-35)            0.752452   \n",
              "2                (Gender_M)  (Product_Category_3_0.0)            0.752452   \n",
              "3  (Product_Category_3_0.0)                (Gender_M)            0.695902   \n",
              "4  (Product_Category_3_0.0)  (Product_Category_2_0.0)            0.695902   \n",
              "5  (Product_Category_2_0.0)  (Product_Category_3_0.0)            0.309693   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "0            0.752452  0.307150    0.767971  1.020625  0.006207    1.066885  \n",
              "1            0.399950  0.307150    0.408199  1.020625  0.006207    1.013939  \n",
              "2            0.695902  0.516963    0.687038  0.987262 -0.006670    0.971676  \n",
              "3            0.752452  0.516963    0.742867  0.987262 -0.006670    0.962725  \n",
              "4            0.309693  0.309693    0.445024  1.436984  0.094177    1.243850  \n",
              "5            0.695902  0.309693    1.000000  1.436984  0.094177         inf  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-073ba7a2-5b7b-4bec-b4df-572cb3893d1e\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.767971</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.066885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Age_26-35)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.399950</td>\n",
              "      <td>0.307150</td>\n",
              "      <td>0.408199</td>\n",
              "      <td>1.020625</td>\n",
              "      <td>0.006207</td>\n",
              "      <td>1.013939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.687038</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.971676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.752452</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.742867</td>\n",
              "      <td>0.987262</td>\n",
              "      <td>-0.006670</td>\n",
              "      <td>0.962725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.445024</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>1.243850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-073ba7a2-5b7b-4bec-b4df-572cb3893d1e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-073ba7a2-5b7b-4bec-b4df-572cb3893d1e 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-073ba7a2-5b7b-4bec-b4df-572cb3893d1e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_maximales = rules [max(rules)]\n",
        "rules_maximales.head()"
      ],
      "metadata": {
        "id": "7qgYpf6w4x_9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2- SEGÚN SU LIFT"
      ],
      "metadata": {
        "id": "ROJKcdIXxzFL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric='lift', min_threshold=0.8)\n",
        "rules_sort = rules.sort_values(by=['lift'], ascending=False)\n",
        "rules_sort.head()"
      ],
      "metadata": {
        "id": "DWnzDfagmYVj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "dc06954c-2403-4581-84a1-4a23cd12c890"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           antecedents                         consequents  \\\n",
              "68  (Gender_M, Product_Category_3_0.0)            (Product_Category_2_0.0)   \n",
              "73            (Product_Category_2_0.0)  (Gender_M, Product_Category_3_0.0)   \n",
              "72            (Product_Category_3_0.0)  (Gender_M, Product_Category_2_0.0)   \n",
              "42            (Product_Category_3_0.0)            (Product_Category_2_0.0)   \n",
              "69  (Gender_M, Product_Category_2_0.0)            (Product_Category_3_0.0)   \n",
              "\n",
              "    antecedent support  consequent support   support  confidence      lift  \\\n",
              "68            0.516963            0.309693  0.230343    0.445571  1.438749   \n",
              "73            0.309693            0.516963  0.230343    0.743780  1.438749   \n",
              "72            0.695902            0.230343  0.230343    0.331000  1.436984   \n",
              "42            0.695902            0.309693  0.309693    0.445024  1.436984   \n",
              "69            0.230343            0.695902  0.230343    1.000000  1.436984   \n",
              "\n",
              "    leverage  conviction  \n",
              "68  0.070244    1.245076  \n",
              "73  0.070244    1.885242  \n",
              "72  0.070047    1.150458  \n",
              "42  0.094177    1.243850  \n",
              "69  0.070047         inf  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c452f891-637d-4663-b43b-b8faa19f3b61\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.445571</td>\n",
              "      <td>1.438749</td>\n",
              "      <td>0.070244</td>\n",
              "      <td>1.245076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>1.438749</td>\n",
              "      <td>0.070244</td>\n",
              "      <td>1.885242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.331000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>1.150458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.445024</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>1.243850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c452f891-637d-4663-b43b-b8faa19f3b61')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c452f891-637d-4663-b43b-b8faa19f3b61 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-c452f891-637d-4663-b43b-b8faa19f3b61');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_maximales = rules [max(rules)]\n",
        "rules_maximales.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qHV_s1Y1ndR",
        "outputId": "fa699c54-2189-4bea-e1d9-b1de25806928"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.307150\n",
              "1    0.307150\n",
              "2    0.202090\n",
              "3    0.202090\n",
              "4    0.316722\n",
              "Name: support, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DECIR QUE REGLAS DESAPARECEN DEPENDIENDO DE LAS COMBINACIONES DE CONFIGURACIÓN Y CATEGORÍAS QUE TOMAMOS\n",
        "\n",
        "EJ 1: HOMBRE POSTUNI TIPO PRODUCTO 2 + LIFT \n",
        "\n",
        "EJ 2: MUJER JUBI TIPO PRODUCTO 1 + CONFIANCE"
      ],
      "metadata": {
        "id": "ADMQ8yqLl-uH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-   Para   una   de   las   configuraciones,   interpreta   algunas   de   las   reglas\n",
        "  que   \n",
        "  te   hayan   resultado   interesantes\n",
        "obtenidas   usando   la   clase  association_rules  y   \n",
        "un   par   de   configuraciones   cambiando   la   variable  metric  y\n",
        "min_threshold.  \n",
        "Justifica los resultados. (1 punto)"
      ],
      "metadata": {
        "id": "Ber1eSlG4g3F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric='lift', min_threshold=0.4)\n",
        "rules_sort = rules.sort_values(by=['lift'], ascending=False)\n",
        "rules_sort.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "4NHpj703-y4t",
        "outputId": "29ce2518-e7b8-4de0-cb74-c00fe3e09c95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           antecedents                         consequents  \\\n",
              "68  (Gender_M, Product_Category_3_0.0)            (Product_Category_2_0.0)   \n",
              "73            (Product_Category_2_0.0)  (Gender_M, Product_Category_3_0.0)   \n",
              "72            (Product_Category_3_0.0)  (Gender_M, Product_Category_2_0.0)   \n",
              "42            (Product_Category_3_0.0)            (Product_Category_2_0.0)   \n",
              "69  (Gender_M, Product_Category_2_0.0)            (Product_Category_3_0.0)   \n",
              "\n",
              "    antecedent support  consequent support   support  confidence      lift  \\\n",
              "68            0.516963            0.309693  0.230343    0.445571  1.438749   \n",
              "73            0.309693            0.516963  0.230343    0.743780  1.438749   \n",
              "72            0.695902            0.230343  0.230343    0.331000  1.436984   \n",
              "42            0.695902            0.309693  0.309693    0.445024  1.436984   \n",
              "69            0.230343            0.695902  0.230343    1.000000  1.436984   \n",
              "\n",
              "    leverage  conviction  \n",
              "68  0.070244    1.245076  \n",
              "73  0.070244    1.885242  \n",
              "72  0.070047    1.150458  \n",
              "42  0.094177    1.243850  \n",
              "69  0.070047         inf  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc0d2d00-8b71-4dea-b95e-eb2943b2f6d0\">\n",
              "    <div 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.445571</td>\n",
              "      <td>1.438749</td>\n",
              "      <td>0.070244</td>\n",
              "      <td>1.245076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Gender_M, Product_Category_3_0.0)</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.516963</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.743780</td>\n",
              "      <td>1.438749</td>\n",
              "      <td>0.070244</td>\n",
              "      <td>1.885242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.331000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>1.150458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.309693</td>\n",
              "      <td>0.445024</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.094177</td>\n",
              "      <td>1.243850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>(Gender_M, Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>0.695902</td>\n",
              "      <td>0.230343</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.436984</td>\n",
              "      <td>0.070047</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc0d2d00-8b71-4dea-b95e-eb2943b2f6d0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bc0d2d00-8b71-4dea-b95e-eb2943b2f6d0 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-bc0d2d00-8b71-4dea-b95e-eb2943b2f6d0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "any(rules.duplicated())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgpkvpwP338z",
        "outputId": "46136af7-2cc1-498a-a5bf-fa4a4df926f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Dadas las mejores configuraciones. ¿Existen reglas o patrones que se repitan? ¿Podemos generalizar  de\n",
        "alguna manera como se comportan los clientes? Expón las conclusiones respaldadas con datos (1 punto)"
      ],
      "metadata": {
        "id": "V7L_pEXRR8Yv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('rules.support')\n",
        "plt.ylabel('rules.condidence')\n",
        "plt.plot(rules.support, rules.confidence, '*')  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "pG1_vU163jC2",
        "outputId": "e3e97140-81d9-4e84-f9b6-a25a340bc079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4e9849adc0>]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Se Pide:"
      ],
      "metadata": {
        "id": "zjD1XxHYgwXh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A partir de lo anterior se pide:"
      ],
      "metadata": {
        "id": "JPkUyBrlTfWE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Prueba al menos tres configuraciones de soporte y frecuencia para cada género, edad y tipo de producto.\n",
        "(1 punto)"
      ],
      "metadata": {
        "id": "ZfTZJOw6An9D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- ¿Qué diferencias hay entre usar soporte y frecuencia? Respalda la respuesta con datos"
      ],
      "metadata": {
        "id": "kYqd7ljv09xV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-   ¿Qué   tipo   reglas   desaparecen   según   la   configuración   y   categorías   ( género,   edad   y   tipo   de   producto)\n",
        "consideradas? ¿Por qué? (1 punto)"
      ],
      "metadata": {
        "id": "0UuqR9ggxuoB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2 TIPOS DE CLASIFICAR REGLAS SEGÚN SU CONFIGURACIÓN:\n",
        "\n",
        "1- SEGÚN SU CONFIANZA\n",
        "\n",
        "CONFIZANZA= SOPORTE(1) / SOPORTE(1,2)\n",
        "\n",
        "2- SEGÚN SU LIFT\n",
        "\n",
        "LIFT = CONFIANZA / SOPORTE\n",
        "\n",
        "3 TIPOS DE CLASIFICAR REGLAS SEGÚN SU CATEGORÍA:\n",
        "\n",
        "1- GÉNERO 2- EDAD 3- TIPO DE PRODUCTO"
      ],
      "metadata": {
        "id": "WOlpGerBiZEs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-   Para   una   de   las   configuraciones,   interpreta   algunas   de   las   reglas   que   te   hayan   resultado   interesantes\n",
        "obtenidas   usando   la   clase  association_rules  y   un   par   de   configuraciones   cambiando   la   variable  metric  y\n",
        "min_threshold. Justifica los resultados. (1 punto)"
      ],
      "metadata": {
        "id": "eZWC4cwShgrO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Dadas las mejores configuraciones. ¿Existen reglas o patrones que se repitan? ¿Podemos generalizar  de\n",
        "alguna manera como se comportan los clientes? Expón las conclusiones respaldadas con datos (1 punto)"
      ],
      "metadata": {
        "id": "CTBLBt6-hepk"
      }
    }
  ]
}