BigDataManagementg_Project.ipynb

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        "<a href=\"https://colab.research.google.com/github/m-haghighi/Amazon-dataset/blob/main/BigDataManagementg_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "!python -m pip install \"dask[complete]\""
      ],
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      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "\n",
        "root_dir = \"/content/gdrive/My Drive/amazon\""
      ],
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      },
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      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import dask\n",
        "import dask.dataframe as dd"
      ],
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        "id": "ktvXeIldGHjV"
      },
      "execution_count": null,
      "outputs": []
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        "dask_df_VideoGames = dd.read_csv(root_dir+'/Video_Games.csv')\n",
        "dask_df_VideoGames.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_VideoGames['VideoGames']=1\n",
        "dask_df_VideoGames=dask_df_VideoGames.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_VideoGames=dask_df_VideoGames.drop_duplicates(subset = [\"User\"])\n",
        "dask_df_VideoGames.head()\n"
      ],
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              "             User  VideoGames\n",
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        "dask_df_ToysandGames = dd.read_csv(root_dir+'/Toys_and_Games.csv')\n",
        "dask_df_ToysandGames.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_ToysandGames['ToysandGames']=1\n",
        "dask_df_ToysandGames=dask_df_ToysandGames.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_ToysandGames=dask_df_ToysandGames.drop_duplicates(subset = [\"User\"])\n",
        "dask_df_ToysandGames.head()"
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              "             User  ToysandGames\n",
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        "dask_df_ToolsandHomeImprovement.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_ToolsandHomeImprovement['ToolsandHomeImprovement']=1\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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        "dask_df_Digital_Music = dd.read_csv(root_dir+'/Digital_Music.csv')\n",
        "dask_df_Digital_Music.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_Digital_Music['Digital_Music']=1\n",
        "dask_df_Digital_Music=dask_df_Digital_Music.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_Digital_Music=dask_df_Digital_Music.drop_duplicates(subset = ([\"User\"]))\n",
        "dask_df_Digital_Music=dask_df_Digital_Music.astype('object')\n",
        "dask_df_Digital_Music.head()"
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      "execution_count": null,
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              "             User Digital_Music\n",
              "0   AC2PL52NKPL29             1\n",
              "1  A1SUZXBDZSDQ3A             1\n",
              "2  A3A0W7FZXM0IZW             1\n",
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        "dask_df_Software = dd.read_csv(root_dir+'/Software.csv')\n",
        "dask_df_Software.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_Software['Software']=1\n",
        "dask_df_Software=dask_df_Software.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_Software=dask_df_Software.drop_duplicates(subset = ([\"User\"]))\n",
        "#dask_df_Software=dask_df_Software.astype('object')\n",
        "dask_df_Software.head()"
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            "text/plain": [
              "             User  Software\n",
              "0  A1YCCU0YRLS0FE         1\n",
              "1  A1BJHRQDYVAY2J         1\n",
              "2   APRDVZ6QBIQXT         1\n",
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    {
      "cell_type": "code",
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        "dask_df_all= dd.merge(dask_df_ToysandGames, dask_df_VideoGames, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all= dd.merge(dask_df_all, dask_df_ToolsandHomeImprovement, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all= dd.merge(dask_df_all, dask_df_Digital_Music, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all= dd.merge(dask_df_all, dask_df_Software, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all=dask_df_all.fillna(0)"
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      "metadata": {
        "id": "0WQC0-OHk4UV"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
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          "height": 676
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        "id": "NR-gUGiJiPIN",
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      "execution_count": null,
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              "              User  ToysandGames  VideoGames  ToolsandHomeImprovement  \\\n",
              "0    A4BCEVVZ4Y3V3           1.0         0.0                      0.0   \n",
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              "6   A2UKOWP9ICU416           1.0         0.0                      0.0   \n",
              "7   A2ONKKDETRWT79           1.0         1.0                      1.0   \n",
              "8    AK9GN9KZZNTEP           1.0         1.0                      1.0   \n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>User</th>\n",
              "      <th>ToysandGames</th>\n",
              "      <th>VideoGames</th>\n",
              "      <th>ToolsandHomeImprovement</th>\n",
              "      <th>Software</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A4BCEVVZ4Y3V3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A2EZ9PY1IHHBX0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A139PXTTC2LGHZ</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A3IB33V29XIL8O</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A1J86V48S4KRJE</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A14J12PRBLGHF4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>A2UKOWP9ICU416</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A2ONKKDETRWT79</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>AK9GN9KZZNTEP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A26HO01PDWN6O5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>A8PF4X87CS3ZZ</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>A28QGCUNS22JPH</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>A2X2RJWNAV4FM0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>A1DIRJ8YNI5TKL</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>A2G5IDD919P2DK</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>AGCAAWP1AVVZR</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>AD2CIUI4TSVY7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>APTEPFKL03QA0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>A14FXEKB1PXTZG</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>A1X9QQFMPGDW70</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(dask_df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ek7P8kKFXmet",
        "outputId": "1f59cad0-397d-4bba-a03a-812679b09e5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9645481"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install mlxtend --upgrade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlZlm9MoxWIG",
        "outputId": "6404e501-4f13-4ee3-9094-117e68bcdf7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.7/dist-packages (0.14.0)\n",
            "Collecting mlxtend\n",
            "  Downloading mlxtend-0.19.0-py2.py3-none-any.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 13.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from mlxtend) (57.4.0)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.4.1)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (3.2.2)\n",
            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.1.0)\n",
            "Requirement already satisfied: scikit-learn>=0.20.3 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.0.2)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->mlxtend) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->mlxtend) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->mlxtend) (3.0.8)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->mlxtend) (1.4.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.0.0->mlxtend) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.2->mlxtend) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.0.0->mlxtend) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.3->mlxtend) (3.1.0)\n",
            "Installing collected packages: mlxtend\n",
            "  Attempting uninstall: mlxtend\n",
            "    Found existing installation: mlxtend 0.14.0\n",
            "    Uninstalling mlxtend-0.14.0:\n",
            "      Successfully uninstalled mlxtend-0.14.0\n",
            "Successfully installed mlxtend-0.19.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#FP growth algorithm\n",
        "#Importing Libraries\n",
        "from mlxtend.frequent_patterns import fpgrowth\n",
        "\n",
        "#running the fpgrowth algorithm\n",
        "dask_df_all_WUser=dask_df_all.drop(columns=[\"User\"])\n",
        "res=fpgrowth(dask_df_all_WUser.compute(),min_support=0.01, use_colnames=True)\n",
        "\n",
        "# printing top 10\n",
        "res.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hH9Z_h_bwLjO",
        "outputId": "3fd2fcd7-6d9e-44e2-dee5-76d005afeeed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    support                                           itemsets\n",
              "0  0.435955                                     (ToysandGames)\n",
              "1  0.159724                                       (VideoGames)\n",
              "2  0.087126                                    (Digital_Music)\n",
              "3  0.487691                          (ToolsandHomeImprovement)\n",
              "4  0.038893                                         (Software)\n",
              "5  0.103211            (ToysandGames, ToolsandHomeImprovement)\n",
              "6  0.037022                         (VideoGames, ToysandGames)\n",
              "7  0.034376              (VideoGames, ToolsandHomeImprovement)\n",
              "8  0.016134  (VideoGames, ToysandGames, ToolsandHomeImprove...\n",
              "9  0.017068                      (ToysandGames, Digital_Music)"
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              "      width: 32px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4de573bd-da49-416f-b9f8-205c78c81e93');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Association rules\n",
        "# importing required module\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "\n",
        "# creating asssociation rules\n",
        "res=association_rules(res, metric=\"lift\", min_threshold=0.8)\n",
        "\n",
        "# printing association rules\n",
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "jflC5XNay9vL",
        "outputId": "989c77c7-a5c3-4fe8-fbc2-2bcbe42f5741"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               antecedents  \\\n",
              "0               (VideoGames, ToysandGames)   \n",
              "1    (VideoGames, ToolsandHomeImprovement)   \n",
              "2  (ToysandGames, ToolsandHomeImprovement)   \n",
              "3                             (VideoGames)   \n",
              "4                           (ToysandGames)   \n",
              "5                (ToolsandHomeImprovement)   \n",
              "\n",
              "                               consequents  antecedent support  \\\n",
              "0                (ToolsandHomeImprovement)            0.037022   \n",
              "1                           (ToysandGames)            0.034376   \n",
              "2                             (VideoGames)            0.103211   \n",
              "3  (ToysandGames, ToolsandHomeImprovement)            0.159724   \n",
              "4    (VideoGames, ToolsandHomeImprovement)            0.435955   \n",
              "5               (VideoGames, ToysandGames)            0.487691   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "0            0.487691  0.016134    0.435787  0.893572 -0.001922    0.908006  \n",
              "1            0.435955  0.016134    0.469341  1.076582  0.001148    1.062915  \n",
              "2            0.159724  0.016134    0.156319  0.978683 -0.000351    0.995964  \n",
              "3            0.103211  0.016134    0.101011  0.978683 -0.000351    0.997553  \n",
              "4            0.034376  0.016134    0.037008  1.076582  0.001148    1.002734  \n",
              "5            0.037022  0.016134    0.033082  0.893572 -0.001922    0.995925  "
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(VideoGames, ToolsandHomeImprovement)</td>\n",
              "      <td>(ToysandGames)</td>\n",
              "      <td>0.034376</td>\n",
              "      <td>0.435955</td>\n",
              "      <td>0.016134</td>\n",
              "      <td>0.469341</td>\n",
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              "      <th>3</th>\n",
              "      <td>(VideoGames)</td>\n",
              "      <td>(ToysandGames, ToolsandHomeImprovement)</td>\n",
              "      <td>0.159724</td>\n",
              "      <td>0.103211</td>\n",
              "      <td>0.016134</td>\n",
              "      <td>0.101011</td>\n",
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              "      <td>-0.000351</td>\n",
              "      <td>0.997553</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(ToysandGames)</td>\n",
              "      <td>(VideoGames, ToolsandHomeImprovement)</td>\n",
              "      <td>0.435955</td>\n",
              "      <td>0.034376</td>\n",
              "      <td>0.016134</td>\n",
              "      <td>0.037008</td>\n",
              "      <td>1.076582</td>\n",
              "      <td>0.001148</td>\n",
              "      <td>1.002734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(ToolsandHomeImprovement)</td>\n",
              "      <td>(VideoGames, ToysandGames)</td>\n",
              "      <td>0.487691</td>\n",
              "      <td>0.037022</td>\n",
              "      <td>0.016134</td>\n",
              "      <td>0.033082</td>\n",
              "      <td>0.893572</td>\n",
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              "      <td>0.995925</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-497875ff-0e18-4441-a868-fe284a598095')\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-497875ff-0e18-4441-a868-fe284a598095');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res.sort_values(\"confidence\",ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "twDe26NdIRCW",
        "outputId": "10a0221e-1f8f-4677-d98f-92cf1fbb1e4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               antecedents  \\\n",
              "1    (VideoGames, ToolsandHomeImprovement)   \n",
              "0               (VideoGames, ToysandGames)   \n",
              "2  (ToysandGames, ToolsandHomeImprovement)   \n",
              "3                             (VideoGames)   \n",
              "4                           (ToysandGames)   \n",
              "5                (ToolsandHomeImprovement)   \n",
              "\n",
              "                               consequents  antecedent support  \\\n",
              "1                           (ToysandGames)            0.034376   \n",
              "0                (ToolsandHomeImprovement)            0.037022   \n",
              "2                             (VideoGames)            0.103211   \n",
              "3  (ToysandGames, ToolsandHomeImprovement)            0.159724   \n",
              "4    (VideoGames, ToolsandHomeImprovement)            0.435955   \n",
              "5               (VideoGames, ToysandGames)            0.487691   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "1            0.435955  0.016134    0.469341  1.076582  0.001148    1.062915  \n",
              "0            0.487691  0.016134    0.435787  0.893572 -0.001922    0.908006  \n",
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              "3            0.103211  0.016134    0.101011  0.978683 -0.000351    0.997553  \n",
              "4            0.034376  0.016134    0.037008  1.076582  0.001148    1.002734  \n",
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              "      <td>(ToysandGames, ToolsandHomeImprovement)</td>\n",
              "      <td>0.159724</td>\n",
              "      <td>0.103211</td>\n",
              "      <td>0.016134</td>\n",
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              "      <td>-0.000351</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>(VideoGames, ToolsandHomeImprovement)</td>\n",
              "      <td>0.435955</td>\n",
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              "      <td>(VideoGames, ToysandGames)</td>\n",
              "      <td>0.487691</td>\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",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dask_df_VideoGames = dd.read_csv(root_dir+'/Video_Games.csv')\n",
        "dask_df_VideoGames.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_VideoGames['VideoGames']=1\n",
        "dask_df_VideoGames=dask_df_VideoGames.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_VideoGames=dask_df_VideoGames.drop_duplicates(subset = [\"User\"])\n",
        "dask_df_VideoGames.head()"
      ],
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "             User  VideoGames\n",
              "0  A3TNZ2Q5E7HTHD           1\n",
              "1  A1OKRM3QFEATQO           1\n",
              "2  A2XO1JFCNEYV3T           1\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "dask_df_CDsandVinyl = dd.read_csv(root_dir+'/CDs_and_Vinyl.csv')\n",
        "dask_df_CDsandVinyl.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_CDsandVinyl['CDsandVinyl']=1\n",
        "dask_df_CDsandVinyl=dask_df_CDsandVinyl.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_CDsandVinyl=dask_df_CDsandVinyl.drop_duplicates(subset = [\"User\"])\n",
        "dask_df_CDsandVinyl.head()"
      ],
      "metadata": {
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "             User  CDsandVinyl\n",
              "0  A1H1DL4K669VQ9            1\n",
              "1  A23WIHT5886G36            1\n",
              "2  A3SZNOJP8OL26X            1\n",
              "3  A3V5XBBT7OZG5G            1\n",
              "4  A3SNL7UJY7GWBI            1"
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              "                                                     [key], {});\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "dask_df_Appliances = dd.read_csv(root_dir+'/Appliances.csv')\n",
        "dask_df_Appliances.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_Appliances['Appliances']=1\n",
        "dask_df_Appliances=dask_df_Appliances.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_Appliances=dask_df_Appliances.drop_duplicates(subset = [\"User\"])\n",
        "dask_df_Appliances.head()"
      ],
      "metadata": {
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        "id": "cvaogbqbPJE8",
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      "execution_count": null,
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            "text/plain": [
              "             User  Appliances\n",
              "0  A3SK6VNBQDNBJE           1\n",
              "1  A3SOFHUR27FO3K           1\n",
              "2  A1HOG1PYCAE157           1\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dask_df_KindleStore = dd.read_csv(root_dir+'/Kindle_Store.csv')\n",
        "dask_df_KindleStore.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_KindleStore['KindleStore']=1\n",
        "dask_df_KindleStore=dask_df_KindleStore.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_KindleStore=dask_df_KindleStore.drop_duplicates(subset = [\"User\"])\n",
        "dask_df_KindleStore.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "GPthrchuPJP5",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             User  KindleStore\n",
              "0   A8P5DK8LLOYGH            1\n",
              "1  A3OM9W7DXSUIIY            1\n",
              "2  A3DPAR2PWB9BT8            1\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "dask_df_IndustrialandScientific = dd.read_csv(root_dir+'/Industrial_and_Scientific.csv')\n",
        "dask_df_IndustrialandScientific.columns=['Item','User','Rating','Timestamp']\n",
        "dask_df_IndustrialandScientific['IndustrialandScientific']=1\n",
        "dask_df_IndustrialandScientific=dask_df_IndustrialandScientific.drop(columns=['Item','Rating','Timestamp'])\n",
        "dask_df_IndustrialandScientific=dask_df_IndustrialandScientific.drop_duplicates(subset = [\"User\"])\n",
        "dask_df_IndustrialandScientific.head()"
      ],
      "metadata": {
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        "id": "ZATnWLyuPJeO",
        "outputId": "1d43202c-50cc-4386-b360-bfee9a95c7c4"
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             User  IndustrialandScientific\n",
              "0   AT6HRPPYOPHMB                        1\n",
              "1   A4IX7B38LIN1E                        1\n",
              "2  A12Q4LR8N17AOZ                        1\n",
              "3  A1GJXZZPOZ3OD9                        1\n",
              "4   AAHQWAS1NA6AX                        1"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "dask_df_all= dd.merge(dask_df_CDsandVinyl, dask_df_VideoGames, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all= dd.merge(dask_df_all, dask_df_Appliances, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all= dd.merge(dask_df_all, dask_df_KindleStore, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all= dd.merge(dask_df_all, dask_df_IndustrialandScientific, how=\"outer\", on=[\"User\"])\n",
        "dask_df_all=dask_df_all.fillna(0)"
      ],
      "metadata": {
        "id": "_Yu-95SXPWs8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(dask_df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xE6YhZD_2s4g",
        "outputId": "abc47a6e-c15b-4a58-aaee-16ea240277fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6827954"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#FP growth algorithm\n",
        "#Importing Libraries\n",
        "from mlxtend.frequent_patterns import fpgrowth\n",
        "\n",
        "#running the fpgrowth algorithm\n",
        "dask_df_all_WUser=dask_df_all.drop(columns=[\"User\"])\n",
        "res=fpgrowth(dask_df_all_WUser.compute(),min_support=0.01, use_colnames=True)\n",
        "\n",
        "# printing top 10\n",
        "res.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "wO_qnQpyS6dz",
        "outputId": "c8c9de26-cd9b-492a-b711-2544d04a56b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    support                                itemsets\n",
              "0  0.284758                           (CDsandVinyl)\n",
              "1  0.182504               (IndustrialandScientific)\n",
              "2  0.352853                           (KindleStore)\n",
              "3  0.225634                            (VideoGames)\n",
              "4  0.075520                            (Appliances)\n",
              "5  0.023727              (CDsandVinyl, KindleStore)\n",
              "6  0.016692  (CDsandVinyl, IndustrialandScientific)\n",
              "7  0.017513   (VideoGames, IndustrialandScientific)\n",
              "8  0.016106  (IndustrialandScientific, KindleStore)\n",
              "9  0.020125               (CDsandVinyl, VideoGames)"
            ],
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              "      <td>0.182504</td>\n",
              "      <td>(IndustrialandScientific)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.352853</td>\n",
              "      <td>(KindleStore)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.225634</td>\n",
              "      <td>(VideoGames)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.075520</td>\n",
              "      <td>(Appliances)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.023727</td>\n",
              "      <td>(CDsandVinyl, KindleStore)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.016692</td>\n",
              "      <td>(CDsandVinyl, IndustrialandScientific)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.017513</td>\n",
              "      <td>(VideoGames, IndustrialandScientific)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.016106</td>\n",
              "      <td>(IndustrialandScientific, KindleStore)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.020125</td>\n",
              "      <td>(CDsandVinyl, VideoGames)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89cb7cf8-d93a-4d81-b949-798994daa209')\"\n",
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              "          const element = document.querySelector('#df-89cb7cf8-d93a-4d81-b949-798994daa209');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Association rules\n",
        "# importing required module\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "\n",
        "# creating asssociation rules\n",
        "res=association_rules(res, metric=\"lift\", min_threshold=0.8)\n",
        "\n",
        "# printing association rules\n",
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "cdaI8QwVS8Yr",
        "outputId": "ca9e0a6b-024c-4cfa-9bf0-51e35984ac6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 antecedents                consequents  antecedent support  \\\n",
              "0               (Appliances)  (IndustrialandScientific)            0.075520   \n",
              "1  (IndustrialandScientific)               (Appliances)            0.182504   \n",
              "\n",
              "   consequent support   support  confidence     lift  leverage  conviction  \n",
              "0            0.182504  0.013435    0.177896  0.97475 -0.000348    0.994395  \n",
              "1            0.075520  0.013435    0.073614  0.97475 -0.000348    0.997942  "
            ],
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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",
              "      <th>support</th>\n",
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              "      <th>0</th>\n",
              "      <td>(Appliances)</td>\n",
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              "      <th>1</th>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95f4388d-1726-4cee-9786-82324b05c545');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    }
  ]
}