Prac2Ej1FINAL.ipynb

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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/javierarteagapuell/deeplearning/blob/main/Prac2Ej1FINAL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "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": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('BlackFriday.csv', encoding='UTF-8')\n",
        "data.head()\n"
      ],
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      "execution_count": 50,
      "outputs": [
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          "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  "
            ],
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "id": "fNBThuo14kbd",
        "outputId": "5eb98471-fd0d-4142-a853-0755af856932",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 51,
      "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": 51
        }
      ]
    },
    {
      "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": "2521fb3f-7221-45d9-95ea-a5905cf98298",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 52,
      "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": {
        "id": "b24Boari4iUE",
        "outputId": "0cc81162-184c-4b45-bfaa-0a5d479d74ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vemos en que columnas hay NA's"
      ],
      "metadata": {
        "id": "zH2WdB3mN-lr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()  "
      ],
      "metadata": {
        "id": "kXA6d1dBNkgH",
        "outputId": "8e687ebb-ed14-479b-88bd-46fa5546058c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 54,
      "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": 54
        }
      ]
    },
    {
      "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": {
        "id": "kNF3BI2qQIFB",
        "outputId": "edaba3da-3dfc-4686-95e9-0ee14d2e1a83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 55,
      "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": {
        "id": "ZC-dd7r_RjWk",
        "outputId": "96179564-483f-48a5-8f02-1985435c58bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 56,
      "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": {
        "id": "bj1yUTAAR3XV",
        "outputId": "98edfe90-915c-49af-d79c-9ec5f8cfb48d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 57,
      "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": 58,
      "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": {
        "id": "4M4fwdekzzD7",
        "outputId": "a405593d-c4e8-4b11-fc53-9604b722733c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 59,
      "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"
      ],
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        "id": "qfz6zFwz2wzA",
        "outputId": "3fcf7af9-e7b1-4ac1-eebb-72c29dd0b765",
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          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "execution_count": 60,
      "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",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#Creamos vectores con todos los nombres de las columnas dummy de cada una de las variables categoricas\n",
        "\n",
        "dummy_df_OC = pd.get_dummies(data,columns=['Occupation'])\n",
        "dummy_df_OC_new = list(dummy_df_OC.filter(like='Occupation_').columns)\n",
        "\n",
        "dummy_df_C = pd.get_dummies(data,columns=['City_Category'])\n",
        "dummy_df_C_new = list(dummy_df_C.filter(like='City_Category_').columns)\n",
        "\n",
        "dummy_df_S = pd.get_dummies(data,columns=['Stay_In_Current_City_Years'])\n",
        "dummy_df_S_new = list(dummy_df_S.filter(like='Stay_In_Current_City_Years_').columns)\n",
        "\n",
        "dummy_df_A = pd.get_dummies(data,columns=['Age'])\n",
        "dummy_df_A_new = list(dummy_df_OC.filter(like='Age_').columns)\n",
        "\n",
        "dummy_df_MS = pd.get_dummies(data,columns=['Marital_Status'])\n",
        "dummy_df_MS_new = list(dummy_df_MS.filter(like='Marital_Status_').columns)\n",
        "\n",
        "dummy_df_PC1 = pd.get_dummies(data,columns=['Product_Category_1'])\n",
        "dummy_df_PC1_new = list(dummy_df_PC1.filter(like='Product_Category_1_').columns)\n",
        "\n",
        "dummy_df_PC2 = pd.get_dummies(data,columns=['Product_Category_2'])\n",
        "dummy_df_PC2_new = list(dummy_df_PC2.filter(like='Product_Category_2_').columns)\n",
        "\n",
        "dummy_df_PC3 = pd.get_dummies(data,columns=['Product_Category_3'])\n",
        "dummy_df_PC3_new = list(dummy_df_PC3.filter(like='Product_Category_3_').columns)"
      ],
      "metadata": {
        "id": "vkZX8NPeIt8z"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creamos un dataframe nuevo para cada categorica agrupando por User_ID y Occupation y sumamos las columnas P_C1,P_C2,P_C3 para cada grupo\n",
        "data_P = data.groupby(['User_ID','Product_ID'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "\n",
        "data_G = data.groupby(['User_ID','Gender'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "\n",
        "data_A = data.groupby(['User_ID','Age'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "\n",
        "data_OC = data.groupby(['User_ID','Occupation'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "data_OC = data_OC.set_axis(dummy_df_OC_new, axis = 1, inplace = False)\n",
        "\n",
        "data_C = data.groupby(['User_ID','City_Category'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "data_C = data_C.set_axis(dummy_df_C_new, axis = 1, inplace = False)\n",
        "\n",
        "data_S = data.groupby(['User_ID','Stay_In_Current_City_Years'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "data_S = data_S.set_axis(dummy_df_S_new, axis = 1, inplace = False)\n",
        "\n",
        "data_MS = data.groupby(['User_ID','Marital_Status'])['Product_Category_2'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "data_MS = data_MS.set_axis(dummy_df_MS_new, axis = 1, inplace = False)\n",
        "\n",
        "data_PC1 = data.groupby(['User_ID','Product_Category_1'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "data_PC1 = data_PC1.set_axis(dummy_df_PC1_new, axis = 1, inplace = False)\n",
        "\n",
        "data_PC2 = data.groupby(['User_ID','Product_Category_2'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "data_PC2 = data_PC2.set_axis(dummy_df_PC2_new, axis = 1, inplace = False)\n",
        "\n",
        "data_PC3= data.groupby(['User_ID','Product_Category_3'])['Product_Category_1'].sum().apply(lambda x: x>0).unstack().fillna(False).reset_index().set_index('User_ID')\n",
        "data_PC3 = data_PC3.set_axis(dummy_df_PC3_new, axis = 1, inplace = False)\n"
      ],
      "metadata": {
        "id": "v1zK4m-EIkBp"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Agrupamos todo en un mismo Dataframe\n",
        "data4 = pd.concat([data_P, data_G,data_A,data_OC,data_C,data_S,data_MS,data_PC1,data_PC2,data_PC3], axis = 1)"
      ],
      "metadata": {
        "id": "G50aUzlFR8Gm"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data4.columns)"
      ],
      "metadata": {
        "id": "LVKX_6msaKoV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51622af3-1ce2-4413-de52-349e5ba1a01a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['P00000142', 'P00000242', 'P00000342', 'P00000442', 'P00000542',\n",
            "       'P00000642', 'P00000742', 'P00000842', 'P00000942', '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=0.5, 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": {
        "id": "k3-bJvTBw4le",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "outputId": "180190c3-59f5-477b-9a5d-02e90e5eb55e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     support                   itemsets  frecuencia\n",
              "17  0.997963   (Product_Category_3_0.0)      5879.0\n",
              "8   0.944152   (Product_Category_2_0.0)      5562.0\n",
              "5   0.913597     (Product_Category_1_5)      5382.0\n",
              "3   0.910711     (Product_Category_1_1)      5365.0\n",
              "7   0.873196     (Product_Category_1_8)      5144.0\n",
              "13  0.830929   (Product_Category_2_8.0)      4895.0\n",
              "14  0.757936  (Product_Category_2_14.0)      4465.0\n",
              "9   0.729588   (Product_Category_2_2.0)      4298.0\n",
              "16  0.723986  (Product_Category_2_16.0)      4265.0\n",
              "0   0.717196                        (M)      4225.0\n",
              "21  0.670175  (Product_Category_3_16.0)      3948.0\n",
              "15  0.656765  (Product_Category_2_15.0)      3869.0\n",
              "11  0.592938   (Product_Category_2_5.0)      3493.0\n",
              "2   0.578170         (Marital_Status_0)      3406.0\n",
              "20  0.565099  (Product_Category_3_15.0)      3329.0\n",
              "22  0.556782  (Product_Category_3_17.0)      3280.0\n",
              "4   0.543711     (Product_Category_1_2)      3203.0\n",
              "19  0.538618  (Product_Category_3_14.0)      3173.0\n",
              "18  0.533865   (Product_Category_3_5.0)      3145.0\n",
              "1   0.532847          (City_Category_C)      3139.0\n",
              "6   0.529961     (Product_Category_1_6)      3122.0\n",
              "10  0.527075   (Product_Category_2_4.0)      3105.0\n",
              "12  0.516890   (Product_Category_2_6.0)      3045.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edc84589-5e8e-4f3b-ad63-e1360b88e47f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>support</th>\n",
              "      <th>itemsets</th>\n",
              "      <th>frecuencia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.997963</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>5879.0</td>\n",
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              "      <th>8</th>\n",
              "      <td>0.944152</td>\n",
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              "      <td>5562.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.913597</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>5382.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.910711</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>5365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.873196</td>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>5144.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.830929</td>\n",
              "      <td>(Product_Category_2_8.0)</td>\n",
              "      <td>4895.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.757936</td>\n",
              "      <td>(Product_Category_2_14.0)</td>\n",
              "      <td>4465.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.729588</td>\n",
              "      <td>(Product_Category_2_2.0)</td>\n",
              "      <td>4298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.723986</td>\n",
              "      <td>(Product_Category_2_16.0)</td>\n",
              "      <td>4265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.717196</td>\n",
              "      <td>(M)</td>\n",
              "      <td>4225.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.670175</td>\n",
              "      <td>(Product_Category_3_16.0)</td>\n",
              "      <td>3948.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.656765</td>\n",
              "      <td>(Product_Category_2_15.0)</td>\n",
              "      <td>3869.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.592938</td>\n",
              "      <td>(Product_Category_2_5.0)</td>\n",
              "      <td>3493.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.578170</td>\n",
              "      <td>(Marital_Status_0)</td>\n",
              "      <td>3406.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.565099</td>\n",
              "      <td>(Product_Category_3_15.0)</td>\n",
              "      <td>3329.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.556782</td>\n",
              "      <td>(Product_Category_3_17.0)</td>\n",
              "      <td>3280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.543711</td>\n",
              "      <td>(Product_Category_1_2)</td>\n",
              "      <td>3203.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.538618</td>\n",
              "      <td>(Product_Category_3_14.0)</td>\n",
              "      <td>3173.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.533865</td>\n",
              "      <td>(Product_Category_3_5.0)</td>\n",
              "      <td>3145.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.532847</td>\n",
              "      <td>(City_Category_C)</td>\n",
              "      <td>3139.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.529961</td>\n",
              "      <td>(Product_Category_1_6)</td>\n",
              "      <td>3122.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.527075</td>\n",
              "      <td>(Product_Category_2_4.0)</td>\n",
              "      <td>3105.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.516890</td>\n",
              "      <td>(Product_Category_2_6.0)</td>\n",
              "      <td>3045.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "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.M==True)/len(data4.M+data4.F)\n",
        "print(SupportMale)"
      ],
      "metadata": {
        "id": "day_BbcWZUYb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5803fc08-fdd5-43bc-d904-d5921415fa47"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7171957222882362\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": {
        "id": "_aW5alB8zedh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "3144d48d-0744-4fe0-becd-4cd68b145a76"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      support                                           itemsets  frecuenciak2\n",
              "102  0.944152   (Product_Category_2_0.0, Product_Category_3_0.0)        5562.0\n",
              "73   0.913258     (Product_Category_1_5, Product_Category_3_0.0)        5380.0\n",
              "53   0.909014     (Product_Category_3_0.0, Product_Category_1_1)        5355.0\n",
              "65   0.881854     (Product_Category_1_5, Product_Category_2_0.0)        5195.0\n",
              "90   0.873196     (Product_Category_3_0.0, Product_Category_1_8)        5144.0\n",
              "..        ...                                                ...           ...\n",
              "97   0.501782   (Product_Category_2_0.0, Product_Category_2_6.0)        2956.0\n",
              "126  0.501103  (Product_Category_2_8.0, Product_Category_3_15.0)        2952.0\n",
              "60   0.500764       (Product_Category_1_2, Product_Category_1_8)        2950.0\n",
              "108  0.500594   (Product_Category_2_5.0, Product_Category_2_2.0)        2949.0\n",
              "118  0.500255  (Product_Category_2_14.0, Product_Category_2_5.0)        2947.0\n",
              "\n",
              "[120 rows x 3 columns]"
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              "      <th>102</th>\n",
              "      <td>0.944152</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_3_0.0)</td>\n",
              "      <td>5562.0</td>\n",
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              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0.913258</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_3_0.0)</td>\n",
              "      <td>5380.0</td>\n",
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              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.909014</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_1_1)</td>\n",
              "      <td>5355.0</td>\n",
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              "      <th>65</th>\n",
              "      <td>0.881854</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0)</td>\n",
              "      <td>5195.0</td>\n",
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              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>0.873196</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_1_8)</td>\n",
              "      <td>5144.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.501782</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_2_6.0)</td>\n",
              "      <td>2956.0</td>\n",
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              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>0.501103</td>\n",
              "      <td>(Product_Category_2_8.0, Product_Category_3_15.0)</td>\n",
              "      <td>2952.0</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.500764</td>\n",
              "      <td>(Product_Category_1_2, Product_Category_1_8)</td>\n",
              "      <td>2950.0</td>\n",
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              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>0.500594</td>\n",
              "      <td>(Product_Category_2_5.0, Product_Category_2_2.0)</td>\n",
              "      <td>2949.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>0.500255</td>\n",
              "      <td>(Product_Category_2_14.0, Product_Category_2_5.0)</td>\n",
              "      <td>2947.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 3 columns</p>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-803f337f-f296-46a2-bf9b-542ac2a6e881');\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": 67
        }
      ]
    },
    {
      "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": {
        "id": "eTgXS013eVFq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "21e738f5-c7a3-4bc8-90f8-8ee05cb730e2"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      support                                           itemsets  frecuenciak3\n",
              "269  0.881854  (Product_Category_1_5, Product_Category_2_0.0,...        5195.0\n",
              "210  0.861653  (Product_Category_2_0.0, Product_Category_3_0....        5076.0\n",
              "312  0.843999  (Product_Category_3_0.0, Product_Category_2_0....        4972.0\n",
              "187  0.835512  (Product_Category_1_5, Product_Category_3_0.0,...        4922.0\n",
              "261  0.814802  (Product_Category_3_0.0, Product_Category_1_8,...        4800.0\n",
              "..        ...                                                ...           ...\n",
              "216  0.500594  (Product_Category_2_5.0, Product_Category_2_2....        2949.0\n",
              "188  0.500594  (Product_Category_3_14.0, Product_Category_1_5...        2949.0\n",
              "382  0.500255  (Product_Category_2_14.0, Product_Category_2_5...        2947.0\n",
              "370  0.500255  (Product_Category_2_5.0, Product_Category_3_0....        2947.0\n",
              "211  0.500085  (Product_Category_3_5.0, Product_Category_2_0....        2946.0\n",
              "\n",
              "[256 rows x 3 columns]"
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              "      <th>269</th>\n",
              "      <td>0.881854</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>5195.0</td>\n",
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              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>0.861653</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_3_0....</td>\n",
              "      <td>5076.0</td>\n",
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              "      <th>312</th>\n",
              "      <td>0.843999</td>\n",
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              "      <td>4922.0</td>\n",
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              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>0.814802</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_1_8,...</td>\n",
              "      <td>4800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>0.500594</td>\n",
              "      <td>(Product_Category_2_5.0, Product_Category_2_2....</td>\n",
              "      <td>2949.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>0.500594</td>\n",
              "      <td>(Product_Category_3_14.0, Product_Category_1_5...</td>\n",
              "      <td>2949.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>0.500255</td>\n",
              "      <td>(Product_Category_2_14.0, Product_Category_2_5...</td>\n",
              "      <td>2947.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370</th>\n",
              "      <td>0.500255</td>\n",
              "      <td>(Product_Category_2_5.0, Product_Category_3_0....</td>\n",
              "      <td>2947.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>0.500085</td>\n",
              "      <td>(Product_Category_3_5.0, Product_Category_2_0....</td>\n",
              "      <td>2946.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>256 rows × 3 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4afecd59-96c1-41ea-9f01-5432e517e6b2 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-4afecd59-96c1-41ea-9f01-5432e517e6b2');\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": [
        "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['frecuenciak4'] =frequent\n",
        "\n",
        "frequent_itemsets_k4_sort.head()"
      ],
      "metadata": {
        "id": "wKwgjR0HXNCX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "653fc3bd-1597-41b8-d4b5-76a595d18c55"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      support                                           itemsets  frecuenciak4\n",
              "448  0.808182  (Product_Category_1_5, Product_Category_2_0.0,...        4761.0\n",
              "570  0.795451  (Product_Category_3_0.0, Product_Category_2_0....        4686.0\n",
              "484  0.777287  (Product_Category_3_0.0, Product_Category_2_0....        4579.0\n",
              "600  0.763028  (Product_Category_1_5, Product_Category_2_0.0,...        4495.0\n",
              "440  0.753183  (Product_Category_1_8, Product_Category_1_5, P...        4437.0"
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              "\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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        "frequent_itemsets_k5 = frequent_itemsets[frequent_itemsets['itemsets'].apply(lambda x: len(x) == 5)]\n",
        "frequent_itemsets_k5_sort = frequent_itemsets_k5.sort_values(by=['support'], ascending=False)\n",
        "\n",
        "frequent=frequent_itemsets_k5_sort.support*len(data4)\n",
        "frequent_itemsets_k5_sort['frecuenciak5'] =frequent\n",
        "\n",
        "frequent_itemsets_k5_sort.head()"
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        "frequent_itemsets_k6_sort = frequent_itemsets_k6.sort_values(by=['support'], ascending=False)\n",
        "\n",
        "frequent=frequent_itemsets_k6_sort.support*len(data4)\n",
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        "\n",
        "frequent_itemsets_k6_sort.head()"
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              "      support                                           itemsets  frecuenciak6\n",
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              "          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": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets_k7 = frequent_itemsets[frequent_itemsets['itemsets'].apply(lambda x: len(x) == 7)]\n",
        "frequent_itemsets_k7_sort = frequent_itemsets_k7.sort_values(by=['support'], ascending=False)\n",
        "\n",
        "frequent=frequent_itemsets_k7_sort.support*len(data4)\n",
        "frequent_itemsets_k7_sort['frecuenciak7'] =frequent\n",
        "\n",
        "frequent_itemsets_k7_sort.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n7oVwnWXQEMC",
        "outputId": "8c09eabc-2f54-4fbd-fbe2-1ec916cbeadb"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       support                                           itemsets  \\\n",
              "1006  0.582074  (Product_Category_1_5, Product_Category_2_0.0,...   \n",
              "1003  0.554575  (Product_Category_1_5, Product_Category_2_0.0,...   \n",
              "1008  0.550671  (Product_Category_1_5, Product_Category_2_0.0,...   \n",
              "1004  0.527415  (Product_Category_1_5, Product_Category_2_0.0,...   \n",
              "1009  0.524699  (Product_Category_1_5, Product_Category_3_16.0...   \n",
              "\n",
              "      frecuenciak7  \n",
              "1006        3429.0  \n",
              "1003        3267.0  \n",
              "1008        3244.0  \n",
              "1004        3107.0  \n",
              "1009        3091.0  "
            ],
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              "      <th>1006</th>\n",
              "      <td>0.582074</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
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              "      <td>3267.0</td>\n",
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              "    <tr>\n",
              "      <th>1008</th>\n",
              "      <td>0.550671</td>\n",
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              "      <td>3244.0</td>\n",
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              "    <tr>\n",
              "      <th>1004</th>\n",
              "      <td>0.527415</td>\n",
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              "      <td>3107.0</td>\n",
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              "    <tr>\n",
              "      <th>1009</th>\n",
              "      <td>0.524699</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-07938582-f835-466d-a3ff-8292f17d980f 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-07938582-f835-466d-a3ff-8292f17d980f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con k=8 no salen más itemsets"
      ],
      "metadata": {
        "id": "LDlKlgMWT2Ww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets_k8 = frequent_itemsets[frequent_itemsets['itemsets'].apply(lambda x: len(x) == 8)]\n",
        "frequent_itemsets_k8_sort = frequent_itemsets_k8.sort_values(by=['support'], ascending=False)\n",
        "\n",
        "frequent=frequent_itemsets_k8_sort.support*len(data4)\n",
        "frequent_itemsets_k8_sort['frecuenciak7'] =frequent\n",
        "\n",
        "frequent_itemsets_k8_sort.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "JqAMPPBqT1yA",
        "outputId": "745f117c-0b39-4224-adf3-704254dc2528"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [support, itemsets, frecuenciak7]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc02b2a6-c462-46ee-9465-644bc432abbe\">\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>frecuenciak7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc02b2a6-c462-46ee-9465-644bc432abbe')\"\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-dc02b2a6-c462-46ee-9465-644bc432abbe 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-dc02b2a6-c462-46ee-9465-644bc432abbe');\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": 73
        }
      ]
    },
    {
      "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)\n",
        "rules"
      ],
      "metadata": {
        "id": "7lRmQhLZgKfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "da901304-e74d-476f-e65f-4f756f5da0dd"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     antecedents  \\\n",
              "0                            (M)   \n",
              "1         (Product_Category_1_1)   \n",
              "2         (Product_Category_1_5)   \n",
              "3                            (M)   \n",
              "4         (Product_Category_1_8)   \n",
              "...                          ...   \n",
              "19479  (Product_Category_2_14.0)   \n",
              "19480   (Product_Category_3_0.0)   \n",
              "19481   (Product_Category_2_8.0)   \n",
              "19482  (Product_Category_2_16.0)   \n",
              "19483     (Product_Category_1_8)   \n",
              "\n",
              "                                             consequents  antecedent support  \\\n",
              "0                                 (Product_Category_1_1)            0.717196   \n",
              "1                                                    (M)            0.910711   \n",
              "2                                                    (M)            0.913597   \n",
              "3                                 (Product_Category_1_5)            0.717196   \n",
              "4                                                    (M)            0.873196   \n",
              "...                                                  ...                 ...   \n",
              "19479  (Product_Category_1_5, Product_Category_2_0.0,...            0.757936   \n",
              "19480  (Product_Category_1_5, Product_Category_2_0.0,...            0.997963   \n",
              "19481  (Product_Category_1_5, Product_Category_2_0.0,...            0.830929   \n",
              "19482  (Product_Category_1_5, Product_Category_2_0.0,...            0.723986   \n",
              "19483  (Product_Category_1_5, Product_Category_2_0.0,...            0.873196   \n",
              "\n",
              "       consequent support   support  confidence      lift  leverage  \\\n",
              "0                0.910711  0.667119    0.930178  1.021375  0.013961   \n",
              "1                0.717196  0.667119    0.732526  1.021375  0.013961   \n",
              "2                0.717196  0.652521    0.714233  0.995868 -0.002707   \n",
              "3                0.913597  0.652521    0.909822  0.995868 -0.002707   \n",
              "4                0.717196  0.623833    0.714425  0.996136 -0.002420   \n",
              "...                   ...       ...         ...       ...       ...   \n",
              "19479            0.566627  0.504159    0.665174  1.173918  0.074692   \n",
              "19480            0.504159  0.504159    0.505188  1.002041  0.001027   \n",
              "19481            0.535563  0.504159    0.606742  1.132905  0.059145   \n",
              "19482            0.608555  0.504159    0.696366  1.144293  0.063573   \n",
              "19483            0.519267  0.504159    0.577372  1.111898  0.050737   \n",
              "\n",
              "       conviction  \n",
              "0        1.278796  \n",
              "1        1.057314  \n",
              "2        0.989631  \n",
              "3        0.958143  \n",
              "4        0.990296  \n",
              "...           ...  \n",
              "19479    1.294321  \n",
              "19480    1.002080  \n",
              "19481    1.180998  \n",
              "19482    1.289198  \n",
              "19483    1.137485  \n",
              "\n",
              "[19484 rows x 9 columns]"
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>0.717196</td>\n",
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              "      <th>2</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(M)</td>\n",
              "      <td>0.913597</td>\n",
              "      <td>0.717196</td>\n",
              "      <td>0.652521</td>\n",
              "      <td>0.714233</td>\n",
              "      <td>0.995868</td>\n",
              "      <td>-0.002707</td>\n",
              "      <td>0.989631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(M)</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>0.717196</td>\n",
              "      <td>0.913597</td>\n",
              "      <td>0.652521</td>\n",
              "      <td>0.909822</td>\n",
              "      <td>0.995868</td>\n",
              "      <td>-0.002707</td>\n",
              "      <td>0.958143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>(M)</td>\n",
              "      <td>0.873196</td>\n",
              "      <td>0.717196</td>\n",
              "      <td>0.623833</td>\n",
              "      <td>0.714425</td>\n",
              "      <td>0.996136</td>\n",
              "      <td>-0.002420</td>\n",
              "      <td>0.990296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19479</th>\n",
              "      <td>(Product_Category_2_14.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.757936</td>\n",
              "      <td>0.566627</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.665174</td>\n",
              "      <td>1.173918</td>\n",
              "      <td>0.074692</td>\n",
              "      <td>1.294321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19480</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.997963</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.505188</td>\n",
              "      <td>1.002041</td>\n",
              "      <td>0.001027</td>\n",
              "      <td>1.002080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19481</th>\n",
              "      <td>(Product_Category_2_8.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.830929</td>\n",
              "      <td>0.535563</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.606742</td>\n",
              "      <td>1.132905</td>\n",
              "      <td>0.059145</td>\n",
              "      <td>1.180998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19482</th>\n",
              "      <td>(Product_Category_2_16.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.723986</td>\n",
              "      <td>0.608555</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.696366</td>\n",
              "      <td>1.144293</td>\n",
              "      <td>0.063573</td>\n",
              "      <td>1.289198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19483</th>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.873196</td>\n",
              "      <td>0.519267</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.577372</td>\n",
              "      <td>1.111898</td>\n",
              "      <td>0.050737</td>\n",
              "      <td>1.137485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19484 rows × 9 columns</p>\n",
              "</div>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5) Listar todas las reglas que sean de alta confianza."
      ],
      "metadata": {
        "id": "UTttHoU-RYgW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Asumimos que las reglas con alta confianza son aquellas que tienen un umbral mínimo por encima del 90%"
      ],
      "metadata": {
        "id": "gGNNR3tsmnLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric='confidence',  min_threshold=0.90)\n",
        "rules"
      ],
      "metadata": {
        "id": "LrQtwdolqDNa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "f597bc0d-01cf-4bf9-aac0-7412cccbc40b"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            antecedents  \\\n",
              "0                                                   (M)   \n",
              "1                                                   (M)   \n",
              "2                                                   (M)   \n",
              "3                                                   (M)   \n",
              "4                                     (City_Category_C)   \n",
              "...                                                 ...   \n",
              "5157  (Product_Category_2_16.0, Product_Category_2_1...   \n",
              "5158  (Product_Category_2_16.0, Product_Category_2_1...   \n",
              "5159  (Product_Category_2_16.0, Product_Category_2_1...   \n",
              "5160  (Product_Category_2_16.0, Product_Category_2_1...   \n",
              "5161  (Product_Category_2_16.0, Product_Category_2_1...   \n",
              "\n",
              "                                            consequents  antecedent support  \\\n",
              "0                                (Product_Category_1_1)            0.717196   \n",
              "1                                (Product_Category_1_5)            0.717196   \n",
              "2                              (Product_Category_2_0.0)            0.717196   \n",
              "3                              (Product_Category_3_0.0)            0.717196   \n",
              "4                              (Product_Category_3_0.0)            0.532847   \n",
              "...                                                 ...                 ...   \n",
              "5157  (Product_Category_1_5, Product_Category_2_0.0,...            0.538109   \n",
              "5158  (Product_Category_1_5, Product_Category_2_0.0,...            0.558649   \n",
              "5159  (Product_Category_1_5, Product_Category_2_0.0,...            0.518757   \n",
              "5160  (Product_Category_1_8, Product_Category_1_5, P...            0.538109   \n",
              "5161  (Product_Category_1_5, Product_Category_2_0.0,...            0.558649   \n",
              "\n",
              "      consequent support   support  confidence      lift  leverage  conviction  \n",
              "0               0.910711  0.667119    0.930178  1.021375  0.013961    1.278796  \n",
              "1               0.913597  0.652521    0.909822  0.995868 -0.002707    0.958143  \n",
              "2               0.944152  0.679341    0.947219  1.003248  0.002200    1.058105  \n",
              "3               0.997963  0.715498    0.997633  0.999669 -0.000237    0.860635  \n",
              "4               0.997963  0.531149    0.996814  0.998849 -0.000612    0.639416  \n",
              "...                  ...       ...         ...       ...       ...         ...  \n",
              "5157            0.795451  0.504159    0.936909  1.177834  0.076120    3.242107  \n",
              "5158            0.763028  0.504159    0.902461  1.182736  0.077894    2.429513  \n",
              "5159            0.881854  0.504159    0.971859  1.102063  0.046691    4.198316  \n",
              "5160            0.795451  0.504159    0.936909  1.177834  0.076120    3.242107  \n",
              "5161            0.763028  0.504159    0.902461  1.182736  0.077894    2.429513  \n",
              "\n",
              "[5162 rows x 9 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
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              "      <th>0</th>\n",
              "      <td>(M)</td>\n",
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              "      <td>0.717196</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.667119</td>\n",
              "      <td>0.930178</td>\n",
              "      <td>1.021375</td>\n",
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              "      <th>1</th>\n",
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              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>0.717196</td>\n",
              "      <td>0.913597</td>\n",
              "      <td>0.652521</td>\n",
              "      <td>0.909822</td>\n",
              "      <td>0.995868</td>\n",
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              "      <th>2</th>\n",
              "      <td>(M)</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.717196</td>\n",
              "      <td>0.944152</td>\n",
              "      <td>0.679341</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(M)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.717196</td>\n",
              "      <td>0.997963</td>\n",
              "      <td>0.715498</td>\n",
              "      <td>0.997633</td>\n",
              "      <td>0.999669</td>\n",
              "      <td>-0.000237</td>\n",
              "      <td>0.860635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(City_Category_C)</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>0.532847</td>\n",
              "      <td>0.997963</td>\n",
              "      <td>0.531149</td>\n",
              "      <td>0.996814</td>\n",
              "      <td>0.998849</td>\n",
              "      <td>-0.000612</td>\n",
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              "      <th>5157</th>\n",
              "      <td>(Product_Category_2_16.0, Product_Category_2_1...</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.538109</td>\n",
              "      <td>0.795451</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.936909</td>\n",
              "      <td>1.177834</td>\n",
              "      <td>0.076120</td>\n",
              "      <td>3.242107</td>\n",
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              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>(Product_Category_2_16.0, Product_Category_2_1...</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.558649</td>\n",
              "      <td>0.763028</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.902461</td>\n",
              "      <td>1.182736</td>\n",
              "      <td>0.077894</td>\n",
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              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>(Product_Category_2_16.0, Product_Category_2_1...</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.518757</td>\n",
              "      <td>0.881854</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.971859</td>\n",
              "      <td>1.102063</td>\n",
              "      <td>0.046691</td>\n",
              "      <td>4.198316</td>\n",
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              "    <tr>\n",
              "      <th>5160</th>\n",
              "      <td>(Product_Category_2_16.0, Product_Category_2_1...</td>\n",
              "      <td>(Product_Category_1_8, Product_Category_1_5, P...</td>\n",
              "      <td>0.538109</td>\n",
              "      <td>0.795451</td>\n",
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              "      <td>0.936909</td>\n",
              "      <td>1.177834</td>\n",
              "      <td>0.076120</td>\n",
              "      <td>3.242107</td>\n",
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              "    <tr>\n",
              "      <th>5161</th>\n",
              "      <td>(Product_Category_2_16.0, Product_Category_2_1...</td>\n",
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              "      <td>0.558649</td>\n",
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              "      <td>0.902461</td>\n",
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              "      <td>0.077894</td>\n",
              "      <td>2.429513</td>\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-2d2b1e7c-f6ee-441a-894c-ba20304880a1 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-2d2b1e7c-f6ee-441a-894c-ba20304880a1');\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": "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": {
        "id": "w7sG75iqxjNk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "fbe72f9c-7974-4ddb-e95a-551fbe74cc6e"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            antecedents  \\\n",
              "995   (Product_Category_1_8, Product_Category_2_8.0,...   \n",
              "2677  (Product_Category_3_16.0, Product_Category_1_8...   \n",
              "716   (Product_Category_2_0.0, Marital_Status_0, Pro...   \n",
              "2691  (Product_Category_1_8, Product_Category_3_0.0,...   \n",
              "4222  (Product_Category_2_14.0, Product_Category_1_5...   \n",
              "...                                                 ...   \n",
              "3043  (Product_Category_2_5.0, Product_Category_3_0....   \n",
              "3047     (Product_Category_2_5.0, Product_Category_1_5)   \n",
              "1335     (Product_Category_2_5.0, Product_Category_1_5)   \n",
              "84                             (Product_Category_2_5.0)   \n",
              "599                            (Product_Category_2_5.0)   \n",
              "\n",
              "                                            consequents  antecedent support  \\\n",
              "995                              (Product_Category_1_1)            0.583093   \n",
              "2677                           (Product_Category_3_0.0)            0.579358   \n",
              "716                            (Product_Category_3_0.0)            0.501273   \n",
              "2691                             (Product_Category_1_1)            0.583093   \n",
              "4222     (Product_Category_3_0.0, Product_Category_1_1)            0.525378   \n",
              "...                                                 ...                 ...   \n",
              "3043     (Product_Category_2_0.0, Product_Category_1_8)            0.564081   \n",
              "3047  (Product_Category_1_8, Product_Category_2_0.0,...            0.564081   \n",
              "1335     (Product_Category_2_0.0, Product_Category_1_8)            0.564081   \n",
              "84                             (Product_Category_2_8.0)            0.592938   \n",
              "599    (Product_Category_3_0.0, Product_Category_2_8.0)            0.592938   \n",
              "\n",
              "      consequent support   support  confidence      lift  leverage  conviction  \n",
              "995             0.910711  0.583093    1.000000  1.098043  0.052064         inf  \n",
              "2677            0.997963  0.579358    1.000000  1.002041  0.001180         inf  \n",
              "716             0.997963  0.501273    1.000000  1.002041  0.001021         inf  \n",
              "2691            0.910711  0.583093    1.000000  1.098043  0.052064         inf  \n",
              "4222            0.909014  0.525378    1.000000  1.100093  0.047802         inf  \n",
              "...                  ...       ...         ...       ...       ...         ...  \n",
              "3043            0.843999  0.507724    0.900090  1.066459  0.031640    1.561416  \n",
              "3047            0.843999  0.507724    0.900090  1.066459  0.031640    1.561416  \n",
              "1335            0.843999  0.507724    0.900090  1.066459  0.031640    1.561416  \n",
              "84              0.830929  0.533695    0.900086  1.083229  0.041006    1.692168  \n",
              "599             0.830080  0.533695    0.900086  1.084337  0.041509    1.700663  \n",
              "\n",
              "[5162 rows x 9 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
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              "      <th>conviction</th>\n",
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              "      <th>995</th>\n",
              "      <td>(Product_Category_1_8, Product_Category_2_8.0,...</td>\n",
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              "      <td>0.910711</td>\n",
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              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.583093</td>\n",
              "      <td>0.910711</td>\n",
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              "      <td>(Product_Category_2_14.0, Product_Category_1_5...</td>\n",
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              "      <td>0.525378</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.047802</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3043</th>\n",
              "      <td>(Product_Category_2_5.0, Product_Category_3_0....</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_1_8)</td>\n",
              "      <td>0.564081</td>\n",
              "      <td>0.843999</td>\n",
              "      <td>0.507724</td>\n",
              "      <td>0.900090</td>\n",
              "      <td>1.066459</td>\n",
              "      <td>0.031640</td>\n",
              "      <td>1.561416</td>\n",
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              "    <tr>\n",
              "      <th>3047</th>\n",
              "      <td>(Product_Category_2_5.0, Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_1_8, Product_Category_2_0.0,...</td>\n",
              "      <td>0.564081</td>\n",
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              "      <td>0.031640</td>\n",
              "      <td>1.561416</td>\n",
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              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>(Product_Category_2_5.0, Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_1_8)</td>\n",
              "      <td>0.564081</td>\n",
              "      <td>0.843999</td>\n",
              "      <td>0.507724</td>\n",
              "      <td>0.900090</td>\n",
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              "      <td>0.031640</td>\n",
              "      <td>1.561416</td>\n",
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              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>(Product_Category_2_5.0)</td>\n",
              "      <td>(Product_Category_2_8.0)</td>\n",
              "      <td>0.592938</td>\n",
              "      <td>0.830929</td>\n",
              "      <td>0.533695</td>\n",
              "      <td>0.900086</td>\n",
              "      <td>1.083229</td>\n",
              "      <td>0.041006</td>\n",
              "      <td>1.692168</td>\n",
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              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>(Product_Category_2_5.0)</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_2_8.0)</td>\n",
              "      <td>0.592938</td>\n",
              "      <td>0.830080</td>\n",
              "      <td>0.533695</td>\n",
              "      <td>0.900086</td>\n",
              "      <td>1.084337</td>\n",
              "      <td>0.041509</td>\n",
              "      <td>1.700663</td>\n",
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              "<p>5162 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3de012e5-a83a-4d98-87e4-55a42b2d2f72')\"\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-3de012e5-a83a-4d98-87e4-55a42b2d2f72 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-3de012e5-a83a-4d98-87e4-55a42b2d2f72');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "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: 'M' in x)]\n",
        "antereg"
      ],
      "metadata": {
        "id": "mhu2sSDAtEwu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "outputId": "3b779c6c-433b-4db6-c0db-32359f9b2fa1"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            antecedents  \\\n",
              "712   (Product_Category_2_16.0, Product_Category_2_0...   \n",
              "709   (Product_Category_2_14.0, Product_Category_2_0...   \n",
              "706   (Product_Category_2_0.0, Product_Category_2_8....   \n",
              "703   (Product_Category_2_0.0, Product_Category_2_2....   \n",
              "702   (Product_Category_1_8, Product_Category_2_8.0, M)   \n",
              "...                                                 ...   \n",
              "1916  (Product_Category_1_1, Product_Category_2_8.0, M)   \n",
              "641   (Product_Category_1_1, Product_Category_2_8.0, M)   \n",
              "1946  (Product_Category_1_5, Product_Category_2_0.0, M)   \n",
              "671   (Product_Category_1_5, Product_Category_2_0.0, M)   \n",
              "1942  (Product_Category_1_5, Product_Category_2_0.0,...   \n",
              "\n",
              "                                         consequents  antecedent support  \\\n",
              "712                         (Product_Category_3_0.0)            0.519097   \n",
              "709                         (Product_Category_3_0.0)            0.512646   \n",
              "706                         (Product_Category_3_0.0)            0.574605   \n",
              "703                         (Product_Category_3_0.0)            0.524529   \n",
              "702                         (Product_Category_3_0.0)            0.535902   \n",
              "...                                              ...                 ...   \n",
              "1916  (Product_Category_1_8, Product_Category_3_0.0)            0.567815   \n",
              "641                           (Product_Category_1_8)            0.567815   \n",
              "1946  (Product_Category_1_8, Product_Category_3_0.0)            0.632490   \n",
              "671                           (Product_Category_1_8)            0.632490   \n",
              "1942                          (Product_Category_1_8)            0.632490   \n",
              "\n",
              "      consequent support   support  confidence      lift  leverage  conviction  \n",
              "712             0.997963  0.519097    1.000000  1.002041  0.001057         inf  \n",
              "709             0.997963  0.512646    1.000000  1.002041  0.001044         inf  \n",
              "706             0.997963  0.574605    1.000000  1.002041  0.001170         inf  \n",
              "703             0.997963  0.524529    1.000000  1.002041  0.001068         inf  \n",
              "702             0.997963  0.535902    1.000000  1.002041  0.001092         inf  \n",
              "...                  ...       ...         ...       ...       ...         ...  \n",
              "1916            0.873196  0.513325    0.904036  1.035318  0.017511    1.321365  \n",
              "641             0.873196  0.513325    0.904036  1.035318  0.017511    1.321365  \n",
              "1946            0.873196  0.569852    0.900966  1.031802  0.017564    1.280407  \n",
              "671             0.873196  0.569852    0.900966  1.031802  0.017564    1.280407  \n",
              "1942            0.873196  0.569852    0.900966  1.031802  0.017564    1.280407  \n",
              "\n",
              "[283 rows x 9 columns]"
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              "      <th>703</th>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_2_2....</td>\n",
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              "      <td>0.524529</td>\n",
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              "      <td>0.524529</td>\n",
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              "      <th>702</th>\n",
              "      <td>(Product_Category_1_8, Product_Category_2_8.0, M)</td>\n",
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              "      <th>1916</th>\n",
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              "      <td>0.567815</td>\n",
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              "      <td>0.513325</td>\n",
              "      <td>0.904036</td>\n",
              "      <td>1.035318</td>\n",
              "      <td>0.017511</td>\n",
              "      <td>1.321365</td>\n",
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              "      <th>641</th>\n",
              "      <td>(Product_Category_1_1, Product_Category_2_8.0, M)</td>\n",
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              "      <th>1946</th>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0, M)</td>\n",
              "      <td>(Product_Category_1_8, Product_Category_3_0.0)</td>\n",
              "      <td>0.632490</td>\n",
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              "      <td>0.569852</td>\n",
              "      <td>0.900966</td>\n",
              "      <td>1.031802</td>\n",
              "      <td>0.017564</td>\n",
              "      <td>1.280407</td>\n",
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              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0, M)</td>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>0.632490</td>\n",
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              "      <td>0.569852</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "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": {
        "id": "C9RRCW_ydzRC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "7b9bdd73-5329-4586-8aeb-df9c51c2cb39"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           antecedents  \\\n",
              "0                                                  (M)   \n",
              "1                               (Product_Category_1_1)   \n",
              "2                               (Product_Category_1_5)   \n",
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              "17529                        (Product_Category_2_14.0)   \n",
              "17530                         (Product_Category_2_8.0)   \n",
              "17531                        (Product_Category_2_16.0)   \n",
              "\n",
              "                                             consequents  antecedent support  \\\n",
              "0                                 (Product_Category_1_1)            0.717196   \n",
              "1                                                    (M)            0.910711   \n",
              "2                                                    (M)            0.913597   \n",
              "3                                 (Product_Category_1_5)            0.717196   \n",
              "4                                                    (M)            0.873196   \n",
              "...                                                  ...                 ...   \n",
              "17527  (Product_Category_1_5, Product_Category_2_0.0,...            0.748939   \n",
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              "17531  (Product_Category_1_5, Product_Category_2_0.0,...            0.723986   \n",
              "\n",
              "       consequent support   support  confidence      lift  leverage  \\\n",
              "0                0.910711  0.667119    0.930178  1.021375  0.013961   \n",
              "1                0.717196  0.667119    0.732526  1.021375  0.013961   \n",
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              "17531            0.608555  0.504159    0.696366  1.144293  0.063573   \n",
              "\n",
              "       conviction  \n",
              "0        1.278796  \n",
              "1        1.057314  \n",
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              "3        0.958143  \n",
              "4        0.990296  \n",
              "...           ...  \n",
              "17527    1.360763  \n",
              "17528    1.456623  \n",
              "17529    1.294321  \n",
              "17530    1.180998  \n",
              "17531    1.289198  \n",
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              "      <th>1</th>\n",
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              "      <th>2</th>\n",
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              "      <td>0.714233</td>\n",
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              "      <td>0.989631</td>\n",
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              "      <th>3</th>\n",
              "      <td>(M)</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>0.717196</td>\n",
              "      <td>0.913597</td>\n",
              "      <td>0.652521</td>\n",
              "      <td>0.909822</td>\n",
              "      <td>0.995868</td>\n",
              "      <td>-0.002707</td>\n",
              "      <td>0.958143</td>\n",
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              "      <th>4</th>\n",
              "      <td>(Product_Category_1_8)</td>\n",
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              "      <td>0.717196</td>\n",
              "      <td>0.623833</td>\n",
              "      <td>0.714425</td>\n",
              "      <td>0.996136</td>\n",
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              "      <td>0.990296</td>\n",
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              "      <th>17527</th>\n",
              "      <td>(Product_Category_1_8, Product_Category_2_8.0)</td>\n",
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              "      <td>0.673164</td>\n",
              "      <td>1.212354</td>\n",
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              "      <th>17528</th>\n",
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              "      <td>0.642506</td>\n",
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              "      <td>0.754573</td>\n",
              "      <td>1.174423</td>\n",
              "      <td>0.074877</td>\n",
              "      <td>1.456623</td>\n",
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              "      <th>17529</th>\n",
              "      <td>(Product_Category_2_14.0)</td>\n",
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              "      <td>0.757936</td>\n",
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              "      <td>0.665174</td>\n",
              "      <td>1.173918</td>\n",
              "      <td>0.074692</td>\n",
              "      <td>1.294321</td>\n",
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              "      <th>17530</th>\n",
              "      <td>(Product_Category_2_8.0)</td>\n",
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              "      <td>0.830929</td>\n",
              "      <td>0.535563</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.606742</td>\n",
              "      <td>1.132905</td>\n",
              "      <td>0.059145</td>\n",
              "      <td>1.180998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17531</th>\n",
              "      <td>(Product_Category_2_16.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.723986</td>\n",
              "      <td>0.608555</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.696366</td>\n",
              "      <td>1.144293</td>\n",
              "      <td>0.063573</td>\n",
              "      <td>1.289198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17532 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-118e329f-81c9-47b5-9ae0-d023ca819a23')\"\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-118e329f-81c9-47b5-9ae0-d023ca819a23 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-118e329f-81c9-47b5-9ae0-d023ca819a23');\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": 78
        }
      ]
    },
    {
      "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": {
        "id": "5TmDLrZH0ZM1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "eb96d553-1813-42a5-8b01-64c4275f8414"
      },
      "execution_count": 79,
      "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": "markdown",
      "source": [
        "A medida que aumentan los itemesets (k's) el soorte va disminuyendo "
      ],
      "metadata": {
        "id": "UWEl9g41Wy48"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('rules.support')\n",
        "plt.ylabel('rules.condidence')\n",
        "plt.scatter(rules.support, rules.confidence)  "
      ],
      "metadata": {
        "id": "JouNKaTQYjCV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e8c2ecda-3ff4-4358-a9d9-266b3d5eec3f"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fe3396834c0>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "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": {
        "id": "x-PqBWTJ3lVf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f950e695-6d36-4bf5-af53-3d95454202f1"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe3390fa370>]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "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": [
        "La mayor parte de las reglas estan entre un 0.5 y un 0.65 de confianza"
      ],
      "metadata": {
        "id": "gT9cLCvlXBRJ"
      }
    },
    {
      "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": 82,
      "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": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dividimos nuestros itemsets según su frecuencia donde aquellos con soporte con frecuencia menor que0.5 son de frecuencia baja, frecuencia media de soporte 0.5 a 0.75 y mas de 0.90 son de frecuencia alta"
      ],
      "metadata": {
        "id": "-ruSbbLVY4P8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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)]\n",
        "frq_items_q1_90 = frq_items_q1[frq_items_q1[\"support\"].apply(lambda x: 0.90 <= x)]"
      ],
      "metadata": {
        "id": "ODHFLkyef8fT"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frq_items_q1_50"
      ],
      "metadata": {
        "id": "D379FSV8tOja",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "21127afc-748f-47c6-b1d9-5c424cf5d491"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        support                                           itemsets\n",
              "0      0.910711                             (Product_Category_1_1)\n",
              "1      0.543711                             (Product_Category_1_2)\n",
              "4      0.913597                             (Product_Category_1_5)\n",
              "5      0.529961                             (Product_Category_1_6)\n",
              "6      0.873196                             (Product_Category_1_8)\n",
              "...         ...                                                ...\n",
              "35338  0.518757  (Product_Category_1_5, Product_Category_2_0.0,...\n",
              "35738  0.508233  (Product_Category_1_5, Product_Category_2_0.0,...\n",
              "37044  0.504159  (Product_Category_2_0.0, Product_Category_2_14...\n",
              "37398  0.502461  (Product_Category_2_0.0, Product_Category_2_14...\n",
              "40824  0.504159  (Product_Category_1_5, Product_Category_2_0.0,...\n",
              "\n",
              "[909 rows x 2 columns]"
            ],
            "text/html": [
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              "      <th>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.910711</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.543711</td>\n",
              "      <td>(Product_Category_1_2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.913597</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.529961</td>\n",
              "      <td>(Product_Category_1_6)</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.873196</td>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35338</th>\n",
              "      <td>0.518757</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35738</th>\n",
              "      <td>0.508233</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37044</th>\n",
              "      <td>0.504159</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_2_14...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37398</th>\n",
              "      <td>0.502461</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_2_14...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40824</th>\n",
              "      <td>0.504159</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>909 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frq_items_q1_75"
      ],
      "metadata": {
        "id": "kbhaSvOFtPo3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9b4974e0-99d1-4944-bded-11a3f2fcb908"
      },
      "execution_count": 86,
      "outputs": [
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              "       support                                           itemsets\n",
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              "170   0.873196     (Product_Category_1_8, Product_Category_3_0.0)\n",
              "198   0.799864   (Product_Category_2_0.0, Product_Category_2_8.0)\n",
              "205   0.944152   (Product_Category_2_0.0, Product_Category_3_0.0)\n",
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              "305   0.757936  (Product_Category_2_14.0, Product_Category_3_0.0)\n",
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              "      <th>21</th>\n",
              "      <td>0.997963</td>\n",
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              "      <td>0.835682</td>\n",
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              "      <th>38</th>\n",
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              "      <th>43</th>\n",
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              "      <td>(Product_Category_2_8.0, Product_Category_1_1)</td>\n",
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              "      <th>116</th>\n",
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              "      <th>121</th>\n",
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              "      <th>128</th>\n",
              "      <td>0.913258</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_3_0.0)</td>\n",
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              "      <th>158</th>\n",
              "      <td>0.843999</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_1_8)</td>\n",
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              "      <th>170</th>\n",
              "      <td>0.873196</td>\n",
              "      <td>(Product_Category_1_8, Product_Category_3_0.0)</td>\n",
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              "      <th>198</th>\n",
              "      <td>0.799864</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_2_8.0)</td>\n",
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              "      <th>205</th>\n",
              "      <td>0.944152</td>\n",
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              "      <th>279</th>\n",
              "      <td>0.830080</td>\n",
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              "      <th>305</th>\n",
              "      <td>0.757936</td>\n",
              "      <td>(Product_Category_2_14.0, Product_Category_3_0.0)</td>\n",
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              "      <th>414</th>\n",
              "      <td>0.753183</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_1_8, P...</td>\n",
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              "      <th>417</th>\n",
              "      <td>0.808182</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
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              "      <th>429</th>\n",
              "      <td>0.835512</td>\n",
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              "      <td>0.777287</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_1_8,...</td>\n",
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              "      <th>469</th>\n",
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              "      <td>0.861653</td>\n",
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              "      <td>0.775760</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_2_8....</td>\n",
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              "    <tr>\n",
              "      <th>983</th>\n",
              "      <td>0.795451</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
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              "      <th>995</th>\n",
              "      <td>0.814802</td>\n",
              "      <td>(Product_Category_1_8, Product_Category_1_5, P...</td>\n",
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              "      <th>1021</th>\n",
              "      <td>0.763028</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
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              "      <th>1028</th>\n",
              "      <td>0.881854</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
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              "    <tr>\n",
              "      <th>1101</th>\n",
              "      <td>0.782550</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_3_0.0,...</td>\n",
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              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>0.843999</td>\n",
              "      <td>(Product_Category_1_8, Product_Category_2_0.0,...</td>\n",
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              "    <tr>\n",
              "      <th>1563</th>\n",
              "      <td>0.799864</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_3_0....</td>\n",
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              "    <tr>\n",
              "      <th>2465</th>\n",
              "      <td>0.753183</td>\n",
              "      <td>(Product_Category_1_8, Product_Category_1_5, P...</td>\n",
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              "    <tr>\n",
              "      <th>2497</th>\n",
              "      <td>0.808182</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
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              "      <th>2766</th>\n",
              "      <td>0.777287</td>\n",
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              "      <th>4607</th>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ca1edde2-3491-4dfa-b63b-308d184542ad');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frq_items_q1_90"
      ],
      "metadata": {
        "id": "YI0YIg_7tPsS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "2dba3b36-78ae-4c0f-e9af-d26d8b8350dc"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      support                                          itemsets\n",
              "0    0.910711                            (Product_Category_1_1)\n",
              "4    0.913597                            (Product_Category_1_5)\n",
              "9    0.944152                          (Product_Category_2_0.0)\n",
              "21   0.997963                          (Product_Category_3_0.0)\n",
              "50   0.909014    (Product_Category_3_0.0, Product_Category_1_1)\n",
              "128  0.913258    (Product_Category_1_5, Product_Category_3_0.0)\n",
              "205  0.944152  (Product_Category_2_0.0, Product_Category_3_0.0)"
            ],
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              "<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>0.910711</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.913597</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.944152</td>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.997963</td>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.909014</td>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_1_1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>0.913258</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_3_0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>0.944152</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_3_0.0)</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c8de9cc7-8c2e-43ba-92ad-4d0649571664');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "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": [
        "print(frq_items_q1_75[frq_items_q1_75['itemsets'].apply(lambda x: 'Product_Category_1_5' in x)])"
      ],
      "metadata": {
        "id": "4carCpZfyiIn",
        "outputId": "2c7e8af0-6f45-4c96-b531-2f89ebe7a0d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       support                                           itemsets\n",
            "4     0.913597                             (Product_Category_1_5)\n",
            "33    0.835682       (Product_Category_1_5, Product_Category_1_1)\n",
            "113   0.814802       (Product_Category_1_5, Product_Category_1_8)\n",
            "116   0.881854     (Product_Category_1_5, Product_Category_2_0.0)\n",
            "121   0.782889     (Product_Category_1_5, Product_Category_2_8.0)\n",
            "128   0.913258     (Product_Category_1_5, Product_Category_3_0.0)\n",
            "414   0.753183  (Product_Category_1_5, Product_Category_1_8, P...\n",
            "417   0.808182  (Product_Category_1_5, Product_Category_2_0.0,...\n",
            "429   0.835512  (Product_Category_1_5, Product_Category_3_0.0,...\n",
            "983   0.795451  (Product_Category_1_5, Product_Category_2_0.0,...\n",
            "995   0.814802  (Product_Category_1_8, Product_Category_1_5, P...\n",
            "1021  0.763028  (Product_Category_1_5, Product_Category_2_0.0,...\n",
            "1028  0.881854  (Product_Category_1_5, Product_Category_2_0.0,...\n",
            "1101  0.782550  (Product_Category_1_5, Product_Category_3_0.0,...\n",
            "2465  0.753183  (Product_Category_1_8, Product_Category_1_5, P...\n",
            "2497  0.808182  (Product_Category_1_5, Product_Category_2_0.0,...\n",
            "4607  0.795451  (Product_Category_1_8, Product_Category_1_5, P...\n",
            "4852  0.763028  (Product_Category_1_5, Product_Category_2_0.0,...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_50 = association_rules(frq_items_q1_50, metric = 'confidence', min_threshold=0.61)\n",
        "rules_50"
      ],
      "metadata": {
        "id": "JmebiAFg4vKO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "46d33e54-db29-41eb-9aeb-bac03a165c0a"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             antecedents  \\\n",
              "0                                 (Product_Category_1_2)   \n",
              "1                                 (Product_Category_1_5)   \n",
              "2                                 (Product_Category_1_1)   \n",
              "3                                 (Product_Category_1_6)   \n",
              "4                                 (Product_Category_1_8)   \n",
              "...                                                  ...   \n",
              "15973  (Product_Category_2_16.0, Product_Category_2_8.0)   \n",
              "15974     (Product_Category_1_8, Product_Category_2_8.0)   \n",
              "15975    (Product_Category_2_16.0, Product_Category_1_8)   \n",
              "15976                          (Product_Category_2_14.0)   \n",
              "15977                          (Product_Category_2_16.0)   \n",
              "\n",
              "                                             consequents  antecedent support  \\\n",
              "0                                 (Product_Category_1_1)            0.543711   \n",
              "1                                 (Product_Category_1_1)            0.913597   \n",
              "2                                 (Product_Category_1_5)            0.910711   \n",
              "3                                 (Product_Category_1_1)            0.529961   \n",
              "4                                 (Product_Category_1_1)            0.873196   \n",
              "...                                                  ...                 ...   \n",
              "15973  (Product_Category_1_5, Product_Category_2_0.0,...            0.635715   \n",
              "15974  (Product_Category_1_5, Product_Category_2_0.0,...            0.748939   \n",
              "15975  (Product_Category_1_5, Product_Category_2_0.0,...            0.668138   \n",
              "15976  (Product_Category_1_5, Product_Category_2_0.0,...            0.757936   \n",
              "15977  (Product_Category_1_5, Product_Category_2_0.0,...            0.723986   \n",
              "\n",
              "       consequent support   support  confidence      lift  leverage  \\\n",
              "0                0.910711  0.523850    0.963472  1.057933  0.028686   \n",
              "1                0.910711  0.835682    0.914716  1.004397  0.003658   \n",
              "2                0.913597  0.835682    0.917614  1.004397  0.003658   \n",
              "3                0.910711  0.506026    0.954837  1.048452  0.023385   \n",
              "4                0.910711  0.802750    0.919323  1.009457  0.007520   \n",
              "...                   ...       ...         ...       ...       ...   \n",
              "15973            0.664743  0.504159    0.793057  1.193029  0.081572   \n",
              "15974            0.555254  0.504159    0.673164  1.212354  0.088308   \n",
              "15975            0.642506  0.504159    0.754573  1.174423  0.074877   \n",
              "15976            0.566627  0.504159    0.665174  1.173918  0.074692   \n",
              "15977            0.608555  0.504159    0.696366  1.144293  0.063573   \n",
              "\n",
              "       conviction  \n",
              "0        2.444375  \n",
              "1        1.046954  \n",
              "2        1.048760  \n",
              "3        1.977017  \n",
              "4        1.106750  \n",
              "...           ...  \n",
              "15973    1.620049  \n",
              "15974    1.360763  \n",
              "15975    1.456623  \n",
              "15976    1.294321  \n",
              "15977    1.289198  \n",
              "\n",
              "[15978 rows x 9 columns]"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
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              "      <th>conviction</th>\n",
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              "      <th>0</th>\n",
              "      <td>(Product_Category_1_2)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.543711</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.523850</td>\n",
              "      <td>0.963472</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.913597</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.835682</td>\n",
              "      <td>0.914716</td>\n",
              "      <td>1.004397</td>\n",
              "      <td>0.003658</td>\n",
              "      <td>1.046954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.913597</td>\n",
              "      <td>0.835682</td>\n",
              "      <td>0.917614</td>\n",
              "      <td>1.004397</td>\n",
              "      <td>0.003658</td>\n",
              "      <td>1.048760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Product_Category_1_6)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.529961</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.506026</td>\n",
              "      <td>0.954837</td>\n",
              "      <td>1.048452</td>\n",
              "      <td>0.023385</td>\n",
              "      <td>1.977017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.873196</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.802750</td>\n",
              "      <td>0.919323</td>\n",
              "      <td>1.009457</td>\n",
              "      <td>0.007520</td>\n",
              "      <td>1.106750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15973</th>\n",
              "      <td>(Product_Category_2_16.0, Product_Category_2_8.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.635715</td>\n",
              "      <td>0.664743</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.793057</td>\n",
              "      <td>1.193029</td>\n",
              "      <td>0.081572</td>\n",
              "      <td>1.620049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15974</th>\n",
              "      <td>(Product_Category_1_8, Product_Category_2_8.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.748939</td>\n",
              "      <td>0.555254</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.673164</td>\n",
              "      <td>1.212354</td>\n",
              "      <td>0.088308</td>\n",
              "      <td>1.360763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15975</th>\n",
              "      <td>(Product_Category_2_16.0, Product_Category_1_8)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.668138</td>\n",
              "      <td>0.642506</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.754573</td>\n",
              "      <td>1.174423</td>\n",
              "      <td>0.074877</td>\n",
              "      <td>1.456623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15976</th>\n",
              "      <td>(Product_Category_2_14.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.757936</td>\n",
              "      <td>0.566627</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.665174</td>\n",
              "      <td>1.173918</td>\n",
              "      <td>0.074692</td>\n",
              "      <td>1.294321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15977</th>\n",
              "      <td>(Product_Category_2_16.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.723986</td>\n",
              "      <td>0.608555</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.696366</td>\n",
              "      <td>1.144293</td>\n",
              "      <td>0.063573</td>\n",
              "      <td>1.289198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15978 rows × 9 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_75 = association_rules(frq_items_q1_75, metric = 'confidence', min_threshold=0.51)\n",
        "rules_75"
      ],
      "metadata": {
        "id": "THjZwoSM45m5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "e08f9995-62e2-4c72-8d91-33e93f02373d"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          antecedents  \\\n",
              "0                              (Product_Category_1_5)   \n",
              "1                              (Product_Category_1_1)   \n",
              "2                              (Product_Category_1_8)   \n",
              "3                              (Product_Category_1_1)   \n",
              "4                            (Product_Category_2_0.0)   \n",
              "..                                                ...   \n",
              "179  (Product_Category_3_0.0, Product_Category_2_8.0)   \n",
              "180                            (Product_Category_1_5)   \n",
              "181                          (Product_Category_2_0.0)   \n",
              "182                          (Product_Category_3_0.0)   \n",
              "183                          (Product_Category_2_8.0)   \n",
              "\n",
              "                                           consequents  antecedent support  \\\n",
              "0                               (Product_Category_1_1)            0.913597   \n",
              "1                               (Product_Category_1_5)            0.910711   \n",
              "2                               (Product_Category_1_1)            0.873196   \n",
              "3                               (Product_Category_1_8)            0.910711   \n",
              "4                               (Product_Category_1_1)            0.944152   \n",
              "..                                                 ...                 ...   \n",
              "179     (Product_Category_1_5, Product_Category_2_0.0)            0.830080   \n",
              "180  (Product_Category_2_0.0, Product_Category_3_0....            0.913597   \n",
              "181  (Product_Category_1_5, Product_Category_3_0.0,...            0.944152   \n",
              "182  (Product_Category_1_5, Product_Category_2_0.0,...            0.997963   \n",
              "183  (Product_Category_1_5, Product_Category_2_0.0,...            0.830929   \n",
              "\n",
              "     consequent support   support  confidence      lift  leverage  conviction  \n",
              "0              0.910711  0.835682    0.914716  1.004397  0.003658    1.046954  \n",
              "1              0.913597  0.835682    0.917614  1.004397  0.003658    1.048760  \n",
              "2              0.910711  0.802750    0.919323  1.009457  0.007520    1.106750  \n",
              "3              0.873196  0.802750    0.881454  1.009457  0.007520    1.069656  \n",
              "4              0.910711  0.861653    0.912621  1.002097  0.001803    1.021860  \n",
              "..                  ...       ...         ...       ...       ...         ...  \n",
              "179            0.881854  0.763028    0.919223  1.042376  0.031019    1.462622  \n",
              "180            0.799864  0.763028    0.835191  1.044166  0.032275    1.214353  \n",
              "181            0.782550  0.763028    0.808163  1.032730  0.024182    1.133513  \n",
              "182            0.763028  0.763028    0.764586  1.002041  0.001554    1.006616  \n",
              "183            0.881854  0.763028    0.918284  1.041311  0.030271    1.445816  \n",
              "\n",
              "[184 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
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              "      <th>0</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
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              "      <td>(Product_Category_1_1)</td>\n",
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              "      <td>0.910711</td>\n",
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              "      <th>2</th>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.873196</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.802750</td>\n",
              "      <td>0.919323</td>\n",
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              "      <td>0.007520</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.873196</td>\n",
              "      <td>0.802750</td>\n",
              "      <td>0.881454</td>\n",
              "      <td>1.009457</td>\n",
              "      <td>0.007520</td>\n",
              "      <td>1.069656</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.944152</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.861653</td>\n",
              "      <td>0.912621</td>\n",
              "      <td>1.002097</td>\n",
              "      <td>0.001803</td>\n",
              "      <td>1.021860</td>\n",
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              "      <th>179</th>\n",
              "      <td>(Product_Category_3_0.0, Product_Category_2_8.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0)</td>\n",
              "      <td>0.830080</td>\n",
              "      <td>0.881854</td>\n",
              "      <td>0.763028</td>\n",
              "      <td>0.919223</td>\n",
              "      <td>1.042376</td>\n",
              "      <td>0.031019</td>\n",
              "      <td>1.462622</td>\n",
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              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_2_0.0, Product_Category_3_0....</td>\n",
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              "      <td>0.799864</td>\n",
              "      <td>0.763028</td>\n",
              "      <td>0.835191</td>\n",
              "      <td>1.044166</td>\n",
              "      <td>0.032275</td>\n",
              "      <td>1.214353</td>\n",
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              "      <th>181</th>\n",
              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_3_0.0,...</td>\n",
              "      <td>0.944152</td>\n",
              "      <td>0.782550</td>\n",
              "      <td>0.763028</td>\n",
              "      <td>0.808163</td>\n",
              "      <td>1.032730</td>\n",
              "      <td>0.024182</td>\n",
              "      <td>1.133513</td>\n",
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              "      <th>182</th>\n",
              "      <td>(Product_Category_3_0.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.997963</td>\n",
              "      <td>0.763028</td>\n",
              "      <td>0.763028</td>\n",
              "      <td>0.764586</td>\n",
              "      <td>1.002041</td>\n",
              "      <td>0.001554</td>\n",
              "      <td>1.006616</td>\n",
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              "      <td>0.918284</td>\n",
              "      <td>1.041311</td>\n",
              "      <td>0.030271</td>\n",
              "      <td>1.445816</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Nos salen pocas reglas de frecuencia alta"
      ],
      "metadata": {
        "id": "y2VoG74zZN6s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules_90 = association_rules(frq_items_q1_90, metric = 'confidence', min_threshold=0.51)\n",
        "rules_90"
      ],
      "metadata": {
        "id": "f39t2xq249rv",
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          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "450bec16-d089-4e0c-c475-e19a74d9dbd8"
      },
      "execution_count": 91,
      "outputs": [
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              "                antecedents               consequents  antecedent support  \\\n",
              "0  (Product_Category_3_0.0)    (Product_Category_1_1)            0.997963   \n",
              "1    (Product_Category_1_1)  (Product_Category_3_0.0)            0.910711   \n",
              "2    (Product_Category_1_5)  (Product_Category_3_0.0)            0.913597   \n",
              "3  (Product_Category_3_0.0)    (Product_Category_1_5)            0.997963   \n",
              "4  (Product_Category_2_0.0)  (Product_Category_3_0.0)            0.944152   \n",
              "5  (Product_Category_3_0.0)  (Product_Category_2_0.0)            0.997963   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "0            0.910711  0.909014    0.910869  1.000173  0.000158    1.001772  \n",
              "1            0.997963  0.909014    0.998136  1.000173  0.000158    1.092854  \n",
              "2            0.997963  0.913258    0.999628  1.001669  0.001522    5.481582  \n",
              "3            0.913597  0.913258    0.915122  1.001669  0.001522    1.017962  \n",
              "4            0.997963  0.944152    1.000000  1.002041  0.001923         inf  \n",
              "5            0.944152  0.944152    0.946079  1.002041  0.001923    1.035741  "
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              "      <td>(Product_Category_2_0.0)</td>\n",
              "      <td>0.997963</td>\n",
              "      <td>0.944152</td>\n",
              "      <td>0.944152</td>\n",
              "      <td>0.946079</td>\n",
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              "      <td>0.001923</td>\n",
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          "metadata": {},
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    },
    {
      "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": "markdown",
      "source": [
        "Vemos como actuán nuestras reglas según son clasificadas por lift o conviction, que son dos formas de medir la precisión"
      ],
      "metadata": {
        "id": "JdiSK04jZUyA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules_50_lift = association_rules(frq_items_q1_50, metric = 'lift', min_threshold=0.61)\n",
        "rules_50_lift"
      ],
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        "id": "bha3W0fZ5VZc",
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          "height": 591
        },
        "outputId": "8c980309-ca67-485d-9217-c4d0d0711b9c"
      },
      "execution_count": 92,
      "outputs": [
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              "                     antecedents  \\\n",
              "0         (Product_Category_1_2)   \n",
              "1         (Product_Category_1_1)   \n",
              "2         (Product_Category_1_5)   \n",
              "3         (Product_Category_1_1)   \n",
              "4         (Product_Category_1_6)   \n",
              "...                          ...   \n",
              "18021  (Product_Category_2_14.0)   \n",
              "18022   (Product_Category_3_0.0)   \n",
              "18023   (Product_Category_2_8.0)   \n",
              "18024  (Product_Category_2_16.0)   \n",
              "18025     (Product_Category_1_8)   \n",
              "\n",
              "                                             consequents  antecedent support  \\\n",
              "0                                 (Product_Category_1_1)            0.543711   \n",
              "1                                 (Product_Category_1_2)            0.910711   \n",
              "2                                 (Product_Category_1_1)            0.913597   \n",
              "3                                 (Product_Category_1_5)            0.910711   \n",
              "4                                 (Product_Category_1_1)            0.529961   \n",
              "...                                                  ...                 ...   \n",
              "18021  (Product_Category_1_5, Product_Category_2_0.0,...            0.757936   \n",
              "18022  (Product_Category_1_5, Product_Category_2_0.0,...            0.997963   \n",
              "18023  (Product_Category_1_5, Product_Category_2_0.0,...            0.830929   \n",
              "18024  (Product_Category_1_5, Product_Category_2_0.0,...            0.723986   \n",
              "18025  (Product_Category_1_5, Product_Category_2_0.0,...            0.873196   \n",
              "\n",
              "       consequent support   support  confidence      lift  leverage  \\\n",
              "0                0.910711  0.523850    0.963472  1.057933  0.028686   \n",
              "1                0.543711  0.523850    0.575210  1.057933  0.028686   \n",
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              "3                0.913597  0.835682    0.917614  1.004397  0.003658   \n",
              "4                0.910711  0.506026    0.954837  1.048452  0.023385   \n",
              "...                   ...       ...         ...       ...       ...   \n",
              "18021            0.566627  0.504159    0.665174  1.173918  0.074692   \n",
              "18022            0.504159  0.504159    0.505188  1.002041  0.001027   \n",
              "18023            0.535563  0.504159    0.606742  1.132905  0.059145   \n",
              "18024            0.608555  0.504159    0.696366  1.144293  0.063573   \n",
              "18025            0.519267  0.504159    0.577372  1.111898  0.050737   \n",
              "\n",
              "       conviction  \n",
              "0        2.444375  \n",
              "1        1.074152  \n",
              "2        1.046954  \n",
              "3        1.048760  \n",
              "4        1.977017  \n",
              "...           ...  \n",
              "18021    1.294321  \n",
              "18022    1.002080  \n",
              "18023    1.180998  \n",
              "18024    1.289198  \n",
              "18025    1.137485  \n",
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              "      <td>0.910711</td>\n",
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              "      <td>0.529961</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.506026</td>\n",
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              "      <td>0.757936</td>\n",
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              "      <td>0.665174</td>\n",
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              "      <td>0.074692</td>\n",
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              "      <td>0.505188</td>\n",
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              "      <td>0.001027</td>\n",
              "      <td>1.002080</td>\n",
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              "      <th>18023</th>\n",
              "      <td>(Product_Category_2_8.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.830929</td>\n",
              "      <td>0.535563</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.606742</td>\n",
              "      <td>1.132905</td>\n",
              "      <td>0.059145</td>\n",
              "      <td>1.180998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18024</th>\n",
              "      <td>(Product_Category_2_16.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.723986</td>\n",
              "      <td>0.608555</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.696366</td>\n",
              "      <td>1.144293</td>\n",
              "      <td>0.063573</td>\n",
              "      <td>1.289198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18025</th>\n",
              "      <td>(Product_Category_1_8)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
              "      <td>0.873196</td>\n",
              "      <td>0.519267</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.577372</td>\n",
              "      <td>1.111898</td>\n",
              "      <td>0.050737</td>\n",
              "      <td>1.137485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18026 rows × 9 columns</p>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_50_conviction = association_rules(frq_items_q1_50, metric = 'conviction', min_threshold=0.61)\n",
        "rules_50_conviction"
      ],
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          "height": 591
        },
        "outputId": "2ee9a8b6-d46d-4801-e46a-3103dcaaebf7"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                     antecedents  \\\n",
              "0         (Product_Category_1_2)   \n",
              "1         (Product_Category_1_1)   \n",
              "2         (Product_Category_1_5)   \n",
              "3         (Product_Category_1_1)   \n",
              "4         (Product_Category_1_6)   \n",
              "...                          ...   \n",
              "18021  (Product_Category_2_14.0)   \n",
              "18022   (Product_Category_3_0.0)   \n",
              "18023   (Product_Category_2_8.0)   \n",
              "18024  (Product_Category_2_16.0)   \n",
              "18025     (Product_Category_1_8)   \n",
              "\n",
              "                                             consequents  antecedent support  \\\n",
              "0                                 (Product_Category_1_1)            0.543711   \n",
              "1                                 (Product_Category_1_2)            0.910711   \n",
              "2                                 (Product_Category_1_1)            0.913597   \n",
              "3                                 (Product_Category_1_5)            0.910711   \n",
              "4                                 (Product_Category_1_1)            0.529961   \n",
              "...                                                  ...                 ...   \n",
              "18021  (Product_Category_1_5, Product_Category_2_0.0,...            0.757936   \n",
              "18022  (Product_Category_1_5, Product_Category_2_0.0,...            0.997963   \n",
              "18023  (Product_Category_1_5, Product_Category_2_0.0,...            0.830929   \n",
              "18024  (Product_Category_1_5, Product_Category_2_0.0,...            0.723986   \n",
              "18025  (Product_Category_1_5, Product_Category_2_0.0,...            0.873196   \n",
              "\n",
              "       consequent support   support  confidence      lift  leverage  \\\n",
              "0                0.910711  0.523850    0.963472  1.057933  0.028686   \n",
              "1                0.543711  0.523850    0.575210  1.057933  0.028686   \n",
              "2                0.910711  0.835682    0.914716  1.004397  0.003658   \n",
              "3                0.913597  0.835682    0.917614  1.004397  0.003658   \n",
              "4                0.910711  0.506026    0.954837  1.048452  0.023385   \n",
              "...                   ...       ...         ...       ...       ...   \n",
              "18021            0.566627  0.504159    0.665174  1.173918  0.074692   \n",
              "18022            0.504159  0.504159    0.505188  1.002041  0.001027   \n",
              "18023            0.535563  0.504159    0.606742  1.132905  0.059145   \n",
              "18024            0.608555  0.504159    0.696366  1.144293  0.063573   \n",
              "18025            0.519267  0.504159    0.577372  1.111898  0.050737   \n",
              "\n",
              "       conviction  \n",
              "0        2.444375  \n",
              "1        1.074152  \n",
              "2        1.046954  \n",
              "3        1.048760  \n",
              "4        1.977017  \n",
              "...           ...  \n",
              "18021    1.294321  \n",
              "18022    1.002080  \n",
              "18023    1.180998  \n",
              "18024    1.289198  \n",
              "18025    1.137485  \n",
              "\n",
              "[18026 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
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              "      <th>antecedent support</th>\n",
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              "      <th>2</th>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.913597</td>\n",
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              "      <th>3</th>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>(Product_Category_1_5)</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.913597</td>\n",
              "      <td>0.835682</td>\n",
              "      <td>0.917614</td>\n",
              "      <td>1.004397</td>\n",
              "      <td>0.003658</td>\n",
              "      <td>1.048760</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Product_Category_1_6)</td>\n",
              "      <td>(Product_Category_1_1)</td>\n",
              "      <td>0.529961</td>\n",
              "      <td>0.910711</td>\n",
              "      <td>0.506026</td>\n",
              "      <td>0.954837</td>\n",
              "      <td>1.048452</td>\n",
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              "      <td>1.977017</td>\n",
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              "      <th>...</th>\n",
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              "      <th>18021</th>\n",
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              "      <td>0.757936</td>\n",
              "      <td>0.566627</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.665174</td>\n",
              "      <td>1.173918</td>\n",
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              "      <td>(Product_Category_3_0.0)</td>\n",
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              "      <td>0.997963</td>\n",
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              "      <td>0.504159</td>\n",
              "      <td>0.505188</td>\n",
              "      <td>1.002041</td>\n",
              "      <td>0.001027</td>\n",
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              "      <th>18023</th>\n",
              "      <td>(Product_Category_2_8.0)</td>\n",
              "      <td>(Product_Category_1_5, Product_Category_2_0.0,...</td>\n",
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              "      <td>0.059145</td>\n",
              "      <td>1.180998</td>\n",
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              "      <th>18024</th>\n",
              "      <td>(Product_Category_2_16.0)</td>\n",
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              "      <td>0.723986</td>\n",
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              "      <td>1.144293</td>\n",
              "      <td>0.063573</td>\n",
              "      <td>1.289198</td>\n",
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              "    <tr>\n",
              "      <th>18025</th>\n",
              "      <td>(Product_Category_1_8)</td>\n",
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              "      <td>0.519267</td>\n",
              "      <td>0.504159</td>\n",
              "      <td>0.577372</td>\n",
              "      <td>1.111898</td>\n",
              "      <td>0.050737</td>\n",
              "      <td>1.137485</td>\n",
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              "<p>18026 rows × 9 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "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": 374
        },
        "outputId": "8c544403-646c-4127-fe6c-21cfba924011"
      },
      "execution_count": 95,
      "outputs": [
        {
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              "                                             antecedents  \\\n",
              "15166  (Product_Category_2_15.0, Product_Category_2_0.0)   \n",
              "15127  (Product_Category_1_5, Product_Category_3_16.0...   \n",
              "15140  (Product_Category_1_5, Product_Category_3_16.0...   \n",
              "7749   (Product_Category_1_5, Product_Category_3_16.0...   \n",
              "15153  (Product_Category_2_15.0, Product_Category_2_0...   \n",
              "\n",
              "                                             consequents  antecedent support  \\\n",
              "15166  (Product_Category_1_5, Product_Category_3_16.0...            0.630283   \n",
              "15127  (Product_Category_2_15.0, Product_Category_2_0.0)            0.609574   \n",
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              "7749   (Product_Category_2_15.0, Product_Category_2_0.0)            0.609744   \n",
              "15153  (Product_Category_1_5, Product_Category_3_16.0...            0.630283   \n",
              "\n",
              "       consequent support   support  confidence      lift  leverage  \\\n",
              "15166            0.609574  0.500764    0.794506  1.303379  0.116559   \n",
              "15127            0.630283  0.500764    0.821498  1.303379  0.116559   \n",
              "15140            0.630283  0.500764    0.821269  1.303016  0.116453   \n",
              "7749             0.630283  0.500764    0.821269  1.303016  0.116453   \n",
              "15153            0.609744  0.500764    0.794506  1.303016  0.116453   \n",
              "\n",
              "       conviction  \n",
              "15166    1.899937  \n",
              "15127    2.071220  \n",
              "15140    2.068570  \n",
              "7749     2.068570  \n",
              "15153    1.899111  "
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            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules_maximales = rules [max(rules)]\n",
        "rules_maximales.head()"
      ],
      "metadata": {
        "id": "-qHV_s1Y1ndR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf9c990e-8539-4865-ebd9-14fb8b75a968"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.667119\n",
              "1    0.667119\n",
              "2    0.652521\n",
              "3    0.652521\n",
              "4    0.623833\n",
              "Name: support, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A partir de lo anterior se pide:"
      ],
      "metadata": {
        "id": "JPkUyBrlTfWE"
      }
    },
    {
      "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": "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"
      }
    }
  ]
}