Affinity_Analysis_dataset1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Naman73598/Affinity_analysis/blob/main/Affinity_Analysis_dataset1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gz4PAhjwBZi",
        "outputId": "aed0f511-a5a6-4508-90d4-ead0ec55eb90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#mounting google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7znVR80vxTxm",
        "outputId": "3e9369b2-9d27-4558-a1c2-131c50590519"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting apyori\n",
            "  Downloading apyori-1.1.2.tar.gz (8.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: apyori\n",
            "  Building wheel for apyori (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for apyori: filename=apyori-1.1.2-py3-none-any.whl size=5976 sha256=1e8eab2ed62341ec62966641b761b0a63d69b13392255556ed2e82174feaa251\n",
            "  Stored in directory: /root/.cache/pip/wheels/32/2a/54/10c595515f385f3726642b10c60bf788029e8f3a1323e3913a\n",
            "Successfully built apyori\n",
            "Installing collected packages: apyori\n",
            "Successfully installed apyori-1.1.2\n"
          ]
        }
      ],
      "source": [
        "#Using apriori package\n",
        "!pip install apyori\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P_NblX9rxyIj"
      },
      "outputs": [],
      "source": [
        "#importing packages\n",
        "#numpy = arrays ;pandas=data manipulation\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from apyori import apriori"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gnxoXwDSq0dz"
      },
      "outputs": [],
      "source": [
        "#loading up the data from dataset of market for basket optimization\n",
        "dt=pd.read_csv('/content/drive/MyDrive/SIN datasets/Market_Basket_Optimisation.csv',header=None)   #add .header=None to remove the header here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "gtNyr46olAWT",
        "outputId": "aa38ebe0-8d31-4913-efa3-0a3ee355b126"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# looking at the frequency of most popular items \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.rcParams['figure.figsize'] = (18, 8)\n",
        "sns.countplot(x=dt[0])\n",
        "plt.title('frequency of most popular items', fontsize = 30)\n",
        "plt.xticks(rotation = 90 )\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g5_uLIDHv-kU"
      },
      "outputs": [],
      "source": [
        "data=dt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "hap-lbBgwUsy",
        "outputId": "e961dff4-f956-465f-f423-5e8c591ce00a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a8a27e31-d06d-4014-a528-212e28c54328\">\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",
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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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              "  "
            ],
            "text/plain": [
              "              0          1           2                 3             4   \\\n",
              "0         shrimp    almonds     avocado    vegetables mix  green grapes   \n",
              "1        burgers  meatballs        eggs               NaN           NaN   \n",
              "2        chutney        NaN         NaN               NaN           NaN   \n",
              "3         turkey    avocado         NaN               NaN           NaN   \n",
              "4  mineral water       milk  energy bar  whole wheat rice     green tea   \n",
              "\n",
              "                 5     6               7             8             9   \\\n",
              "0  whole weat flour  yams  cottage cheese  energy drink  tomato juice   \n",
              "1               NaN   NaN             NaN           NaN           NaN   \n",
              "2               NaN   NaN             NaN           NaN           NaN   \n",
              "3               NaN   NaN             NaN           NaN           NaN   \n",
              "4               NaN   NaN             NaN           NaN           NaN   \n",
              "\n",
              "               10         11     12     13             14      15  \\\n",
              "0  low fat yogurt  green tea  honey  salad  mineral water  salmon   \n",
              "1             NaN        NaN    NaN    NaN            NaN     NaN   \n",
              "2             NaN        NaN    NaN    NaN            NaN     NaN   \n",
              "3             NaN        NaN    NaN    NaN            NaN     NaN   \n",
              "4             NaN        NaN    NaN    NaN            NaN     NaN   \n",
              "\n",
              "                  16               17       18         19  \n",
              "0  antioxydant juice  frozen smoothie  spinach  olive oil  \n",
              "1                NaN              NaN      NaN        NaN  \n",
              "2                NaN              NaN      NaN        NaN  \n",
              "3                NaN              NaN      NaN        NaN  \n",
              "4                NaN              NaN      NaN        NaN  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vf-68IqkwXB6",
        "outputId": "e39a47ae-58f7-4735-afc4-1e90333c47ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7501, 20)\n"
          ]
        }
      ],
      "source": [
        "print(data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d6TKxiDCw8PS"
      },
      "outputs": [],
      "source": [
        "#We need to replace the empty fields with 0\n",
        "data.fillna(0,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "22Dy17GPxjwa",
        "outputId": "97cf91b2-5013-4a57-c612-f048eae5910c"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d85f6e91-27d4-4f09-99e6-84a8833af9d7');\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",
              "  "
            ],
            "text/plain": [
              "              0          1           2                 3             4   \\\n",
              "0         shrimp    almonds     avocado    vegetables mix  green grapes   \n",
              "1        burgers  meatballs        eggs                 0             0   \n",
              "2        chutney          0           0                 0             0   \n",
              "3         turkey    avocado           0                 0             0   \n",
              "4  mineral water       milk  energy bar  whole wheat rice     green tea   \n",
              "\n",
              "                 5     6               7             8             9   \\\n",
              "0  whole weat flour  yams  cottage cheese  energy drink  tomato juice   \n",
              "1                 0     0               0             0             0   \n",
              "2                 0     0               0             0             0   \n",
              "3                 0     0               0             0             0   \n",
              "4                 0     0               0             0             0   \n",
              "\n",
              "               10         11     12     13             14      15  \\\n",
              "0  low fat yogurt  green tea  honey  salad  mineral water  salmon   \n",
              "1               0          0      0      0              0       0   \n",
              "2               0          0      0      0              0       0   \n",
              "3               0          0      0      0              0       0   \n",
              "4               0          0      0      0              0       0   \n",
              "\n",
              "                  16               17       18         19  \n",
              "0  antioxydant juice  frozen smoothie  spinach  olive oil  \n",
              "1                  0                0        0          0  \n",
              "2                  0                0        0          0  \n",
              "3                  0                0        0          0  \n",
              "4                  0                0        0          0  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GIwA1cJdxy0-"
      },
      "outputs": [],
      "source": [
        "#since the algorithm uses a list format, we need to convert the data in list format.\n",
        "transactions=[]\n",
        "for i in range(0,len(data)):\n",
        "  transactions.append([str(data.values[i,j]) for j in range(0,20) if str(data.values[i,j])!='0'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tir2JcR0yJmq",
        "outputId": "8ecea67a-6987-4f37-c1e7-a3fd98fc16e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ],
      "source": [
        "print(type(transactions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fh3J4hHSys5d"
      },
      "outputs": [],
      "source": [
        "#Now, we will implemement apriori algorithm\n",
        "#using the confidence, lift and support properties of association rules with apriori\n",
        "#We will set the value of min_support=0.003, min_confidence=0.2,min_lift=3,min_length=2\n",
        "#transactions: A list of transactions.\n",
        "# min_support= To set the minimum support float value. Here we have used 0.003 that is calculated by taking 3 transactions per customer each week to the total number of transactions.\n",
        "# min_confidence: To set the minimum confidence value. Here we have taken 0.2. It can be changed as per the business problem.\n",
        "# min_lift= To set the minimum lift value.\n",
        "# min_length= It takes the minimum number of products for the association.\n",
        "# max_length = It takes the maximum number of products for the association.\n",
        "\n",
        "association_rules = apriori(transactions, min_support=0.003,min_confidence=0.2,min_lift=3,min_length=2)\n",
        "association_results = list(association_rules)   #listing the items with rules being converted to list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPH0CSId14xk",
        "outputId": "b86a43a7-ee04-4208-f473-7344a21dff95"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[RelationRecord(items=frozenset({'chicken', 'light cream'}), support=0.004532728969470737, ordered_statistics=[OrderedStatistic(items_base=frozenset({'light cream'}), items_add=frozenset({'chicken'}), confidence=0.29059829059829057, lift=4.84395061728395)]),\n",
              " RelationRecord(items=frozenset({'mushroom cream sauce', 'escalope'}), support=0.005732568990801226, ordered_statistics=[OrderedStatistic(items_base=frozenset({'mushroom cream sauce'}), items_add=frozenset({'escalope'}), confidence=0.3006993006993007, lift=3.790832696715049)]),\n",
              " RelationRecord(items=frozenset({'pasta', 'escalope'}), support=0.005865884548726837, ordered_statistics=[OrderedStatistic(items_base=frozenset({'pasta'}), items_add=frozenset({'escalope'}), confidence=0.3728813559322034, lift=4.700811850163794)]),\n",
              " RelationRecord(items=frozenset({'honey', 'fromage blanc'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'fromage blanc'}), items_add=frozenset({'honey'}), confidence=0.2450980392156863, lift=5.164270764485569)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'herb & pepper'}), support=0.015997866951073192, ordered_statistics=[OrderedStatistic(items_base=frozenset({'herb & pepper'}), items_add=frozenset({'ground beef'}), confidence=0.3234501347708895, lift=3.2919938411349285)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'tomato sauce'}), support=0.005332622317024397, ordered_statistics=[OrderedStatistic(items_base=frozenset({'tomato sauce'}), items_add=frozenset({'ground beef'}), confidence=0.3773584905660377, lift=3.840659481324083)]),\n",
              " RelationRecord(items=frozenset({'light cream', 'olive oil'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'light cream'}), items_add=frozenset({'olive oil'}), confidence=0.20512820512820515, lift=3.1147098515519573)]),\n",
              " RelationRecord(items=frozenset({'whole wheat pasta', 'olive oil'}), support=0.007998933475536596, ordered_statistics=[OrderedStatistic(items_base=frozenset({'whole wheat pasta'}), items_add=frozenset({'olive oil'}), confidence=0.2714932126696833, lift=4.122410097642296)]),\n",
              " RelationRecord(items=frozenset({'pasta', 'shrimp'}), support=0.005065991201173177, ordered_statistics=[OrderedStatistic(items_base=frozenset({'pasta'}), items_add=frozenset({'shrimp'}), confidence=0.3220338983050847, lift=4.506672147735896)]),\n",
              " RelationRecord(items=frozenset({'avocado', 'milk', 'spaghetti'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'avocado', 'spaghetti'}), items_add=frozenset({'milk'}), confidence=0.41666666666666663, lift=3.215449245541838)]),\n",
              " RelationRecord(items=frozenset({'milk', 'burgers', 'cake'}), support=0.0037328356219170776, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'cake'}), items_add=frozenset({'burgers'}), confidence=0.27999999999999997, lift=3.211437308868501)]),\n",
              " RelationRecord(items=frozenset({'chocolate', 'turkey', 'burgers'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'turkey', 'chocolate'}), items_add=frozenset({'burgers'}), confidence=0.27058823529411763, lift=3.1034898363014927)]),\n",
              " RelationRecord(items=frozenset({'turkey', 'milk', 'burgers'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'turkey', 'milk'}), items_add=frozenset({'burgers'}), confidence=0.2823529411764706, lift=3.2384241770102533)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'tomatoes', 'cake'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'cake'}), items_add=frozenset({'tomatoes'}), confidence=0.2987012987012987, lift=4.367560314928736), OrderedStatistic(items_base=frozenset({'tomatoes', 'cake'}), items_add=frozenset({'frozen vegetables'}), confidence=0.36507936507936506, lift=3.8300144300144296)]),\n",
              " RelationRecord(items=frozenset({'cereals', 'ground beef', 'spaghetti'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'cereals', 'ground beef'}), items_add=frozenset({'spaghetti'}), confidence=0.6764705882352942, lift=3.8853031258445188), OrderedStatistic(items_base=frozenset({'cereals', 'spaghetti'}), items_add=frozenset({'ground beef'}), confidence=0.45999999999999996, lift=4.681763907734057)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'chicken', 'milk'}), support=0.0038661511798426876, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'chicken'}), items_add=frozenset({'milk'}), confidence=0.40845070422535207, lift=3.152046020981858)]),\n",
              " RelationRecord(items=frozenset({'chicken', 'milk', 'olive oil'}), support=0.0035995200639914677, ordered_statistics=[OrderedStatistic(items_base=frozenset({'chicken', 'milk'}), items_add=frozenset({'olive oil'}), confidence=0.24324324324324323, lift=3.693456614509246), OrderedStatistic(items_base=frozenset({'chicken', 'olive oil'}), items_add=frozenset({'milk'}), confidence=0.5, lift=3.858539094650206), OrderedStatistic(items_base=frozenset({'milk', 'olive oil'}), items_add=frozenset({'chicken'}), confidence=0.2109375, lift=3.51609375)]),\n",
              " RelationRecord(items=frozenset({'chicken', 'spaghetti', 'olive oil'}), support=0.0034662045060658577, ordered_statistics=[OrderedStatistic(items_base=frozenset({'chicken', 'spaghetti'}), items_add=frozenset({'olive oil'}), confidence=0.20155038759689922, lift=3.0603835169318647)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'shrimp', 'chocolate'}), support=0.005332622317024397, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'chocolate'}), items_add=frozenset({'shrimp'}), confidence=0.23255813953488375, lift=3.2545123221103784), OrderedStatistic(items_base=frozenset({'shrimp', 'chocolate'}), items_add=frozenset({'frozen vegetables'}), confidence=0.29629629629629634, lift=3.1084175084175087)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'chocolate', 'herb & pepper'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'chocolate', 'herb & pepper'}), items_add=frozenset({'ground beef'}), confidence=0.4411764705882354, lift=4.4901827759597746)]),\n",
              " RelationRecord(items=frozenset({'soup', 'milk', 'chocolate'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'soup', 'chocolate'}), items_add=frozenset({'milk'}), confidence=0.3947368421052632, lift=3.0462150747238472)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'cooking oil', 'spaghetti'}), support=0.004799360085321957, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'cooking oil'}), items_add=frozenset({'spaghetti'}), confidence=0.5714285714285714, lift=3.2819951870487856), OrderedStatistic(items_base=frozenset({'cooking oil', 'spaghetti'}), items_add=frozenset({'ground beef'}), confidence=0.3025210084033613, lift=3.0789824749438446)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'herb & pepper', 'eggs'}), support=0.0041327822956939075, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'eggs'}), items_add=frozenset({'herb & pepper'}), confidence=0.2066666666666667, lift=4.178454627133872), OrderedStatistic(items_base=frozenset({'herb & pepper', 'eggs'}), items_add=frozenset({'ground beef'}), confidence=0.3297872340425532, lift=3.3564912381997174)]),\n",
              " RelationRecord(items=frozenset({'spaghetti', 'eggs', 'red wine'}), support=0.0037328356219170776, ordered_statistics=[OrderedStatistic(items_base=frozenset({'eggs', 'red wine'}), items_add=frozenset({'spaghetti'}), confidence=0.5283018867924528, lift=3.0342974370828397)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'french fries', 'herb & pepper'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'french fries'}), items_add=frozenset({'herb & pepper'}), confidence=0.23076923076923078, lift=4.665768194070081), OrderedStatistic(items_base=frozenset({'french fries', 'herb & pepper'}), items_add=frozenset({'ground beef'}), confidence=0.46153846153846156, lift=4.697421981004071)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'green tea', 'tomatoes'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'green tea'}), items_add=frozenset({'tomatoes'}), confidence=0.2314814814814815, lift=3.38468341635983)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'ground beef', 'spaghetti'}), support=0.008665511265164644, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'spaghetti'}), items_add=frozenset({'ground beef'}), confidence=0.31100478468899523, lift=3.165328208890303)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'milk', 'olive oil'}), support=0.004799360085321957, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'milk'}), items_add=frozenset({'olive oil'}), confidence=0.20338983050847456, lift=3.088314005352364), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'olive oil'}), items_add=frozenset({'milk'}), confidence=0.4235294117647058, lift=3.2684095860566447)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'soup', 'milk'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'soup'}), items_add=frozenset({'milk'}), confidence=0.5, lift=3.858539094650206)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'milk', 'tomatoes'}), support=0.0041327822956939075, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'tomatoes'}), items_add=frozenset({'frozen vegetables'}), confidence=0.29523809523809524, lift=3.0973160173160172)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'shrimp', 'mineral water'}), support=0.007199040127982935, ordered_statistics=[OrderedStatistic(items_base=frozenset({'shrimp', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.30508474576271183, lift=3.200616332819722)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'spaghetti', 'olive oil'}), support=0.005732568990801226, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'spaghetti'}), items_add=frozenset({'olive oil'}), confidence=0.20574162679425836, lift=3.1240241752707125)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'shrimp', 'spaghetti'}), support=0.005999200106652446, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'spaghetti'}), items_add=frozenset({'shrimp'}), confidence=0.21531100478468898, lift=3.0131489680782684)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'shrimp', 'tomatoes'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'shrimp'}), items_add=frozenset({'tomatoes'}), confidence=0.24000000000000002, lift=3.5092397660818717), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'tomatoes'}), items_add=frozenset({'shrimp'}), confidence=0.2479338842975207, lift=3.4696866905143704), OrderedStatistic(items_base=frozenset({'shrimp', 'tomatoes'}), items_add=frozenset({'frozen vegetables'}), confidence=0.35714285714285715, lift=3.7467532467532467)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'tomatoes', 'spaghetti'}), support=0.006665777896280496, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'spaghetti'}), items_add=frozenset({'tomatoes'}), confidence=0.23923444976076558, lift=3.4980460188216425), OrderedStatistic(items_base=frozenset({'tomatoes', 'spaghetti'}), items_add=frozenset({'frozen vegetables'}), confidence=0.3184713375796179, lift=3.341053850607991)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'spaghetti', 'grated cheese'}), support=0.005332622317024397, ordered_statistics=[OrderedStatistic(items_base=frozenset({'spaghetti', 'grated cheese'}), items_add=frozenset({'ground beef'}), confidence=0.3225806451612903, lift=3.283144395325426)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'green tea', 'tomatoes'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'green tea'}), items_add=frozenset({'tomatoes'}), confidence=0.2072072072072072, lift=3.0297490472929067)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'milk', 'herb & pepper'}), support=0.0035995200639914677, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'herb & pepper'}), items_add=frozenset({'ground beef'}), confidence=0.3913043478260869, lift=3.9825968969382335)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'mineral water', 'herb & pepper'}), support=0.006665777896280496, ordered_statistics=[OrderedStatistic(items_base=frozenset({'mineral water', 'herb & pepper'}), items_add=frozenset({'ground beef'}), confidence=0.39062500000000006, lift=3.975682666214383)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'spaghetti', 'herb & pepper'}), support=0.006399146780429276, ordered_statistics=[OrderedStatistic(items_base=frozenset({'spaghetti', 'herb & pepper'}), items_add=frozenset({'ground beef'}), confidence=0.3934426229508197, lift=4.004359721511667)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'milk', 'olive oil'}), support=0.004932675643247567, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'milk'}), items_add=frozenset({'olive oil'}), confidence=0.22424242424242427, lift=3.40494417862839)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'soup', 'milk'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'soup'}), items_add=frozenset({'milk'}), confidence=0.4109589041095891, lift=3.1714019956029094)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'pepper', 'spaghetti'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'pepper', 'spaghetti'}), items_add=frozenset({'ground beef'}), confidence=0.33783783783783783, lift=3.4384282518610876)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'shrimp', 'spaghetti'}), support=0.005999200106652446, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'shrimp'}), items_add=frozenset({'spaghetti'}), confidence=0.5232558139534884, lift=3.005315360233627)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'tomato sauce', 'spaghetti'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'tomato sauce'}), items_add=frozenset({'ground beef', 'spaghetti'}), confidence=0.2169811320754717, lift=5.535970992170453), OrderedStatistic(items_base=frozenset({'ground beef', 'tomato sauce'}), items_add=frozenset({'spaghetti'}), confidence=0.5750000000000001, lift=3.3025076569678413), OrderedStatistic(items_base=frozenset({'tomato sauce', 'spaghetti'}), items_add=frozenset({'ground beef'}), confidence=0.4893617021276596, lift=4.980599901844742)]),\n",
              " RelationRecord(items=frozenset({'spaghetti', 'light cream', 'mineral water'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'light cream'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.20512820512820515, lift=3.4345238095238098)]),\n",
              " RelationRecord(items=frozenset({'shrimp', 'milk', 'olive oil'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'shrimp', 'olive oil'}), items_add=frozenset({'milk'}), confidence=0.3934426229508197, lift=3.0362274843149164)]),\n",
              " RelationRecord(items=frozenset({'soup', 'milk', 'olive oil'}), support=0.0035995200639914677, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'olive oil'}), items_add=frozenset({'soup'}), confidence=0.2109375, lift=4.174781497361478), OrderedStatistic(items_base=frozenset({'soup', 'milk'}), items_add=frozenset({'olive oil'}), confidence=0.23684210526315788, lift=3.5962603878116344), OrderedStatistic(items_base=frozenset({'soup', 'olive oil'}), items_add=frozenset({'milk'}), confidence=0.4029850746268656, lift=3.1098673300165833)]),\n",
              " RelationRecord(items=frozenset({'milk', 'spaghetti', 'olive oil'}), support=0.007199040127982935, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'spaghetti'}), items_add=frozenset({'olive oil'}), confidence=0.20300751879699247, lift=3.0825089038385434)]),\n",
              " RelationRecord(items=frozenset({'soup', 'milk', 'tomatoes'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'tomatoes'}), items_add=frozenset({'soup'}), confidence=0.21904761904761905, lift=4.335293378565146), OrderedStatistic(items_base=frozenset({'soup', 'tomatoes'}), items_add=frozenset({'milk'}), confidence=0.44230769230769235, lift=3.4133230452674903)]),\n",
              " RelationRecord(items=frozenset({'whole wheat pasta', 'milk', 'spaghetti'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'whole wheat pasta', 'spaghetti'}), items_add=frozenset({'milk'}), confidence=0.4545454545454546, lift=3.5077628133183696)]),\n",
              " RelationRecord(items=frozenset({'soup', 'mineral water', 'olive oil'}), support=0.005199306759098787, ordered_statistics=[OrderedStatistic(items_base=frozenset({'soup', 'mineral water'}), items_add=frozenset({'olive oil'}), confidence=0.22543352601156072, lift=3.4230301186492245)]),\n",
              " RelationRecord(items=frozenset({'whole wheat pasta', 'mineral water', 'olive oil'}), support=0.0038661511798426876, ordered_statistics=[OrderedStatistic(items_base=frozenset({'whole wheat pasta', 'mineral water'}), items_add=frozenset({'olive oil'}), confidence=0.4027777777777778, lift=6.115862573099416)]),\n",
              " RelationRecord(items=frozenset({'pancakes', 'spaghetti', 'olive oil'}), support=0.005065991201173177, ordered_statistics=[OrderedStatistic(items_base=frozenset({'spaghetti', 'pancakes'}), items_add=frozenset({'olive oil'}), confidence=0.20105820105820105, lift=3.0529100529100526)]),\n",
              " RelationRecord(items=frozenset({'tomatoes', 'spaghetti', 'olive oil'}), support=0.004399413411545127, ordered_statistics=[OrderedStatistic(items_base=frozenset({'tomatoes', 'olive oil'}), items_add=frozenset({'spaghetti'}), confidence=0.6111111111111112, lift=3.5099115194827295), OrderedStatistic(items_base=frozenset({'tomatoes', 'spaghetti'}), items_add=frozenset({'olive oil'}), confidence=0.21019108280254778, lift=3.19158565202816)]),\n",
              " RelationRecord(items=frozenset({'whole wheat rice', 'tomatoes', 'spaghetti'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'whole wheat rice', 'spaghetti'}), items_add=frozenset({'tomatoes'}), confidence=0.2169811320754717, lift=3.1726617382029496)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'mineral water', 'chocolate', 'eggs'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'eggs'}), items_add=frozenset({'chocolate', 'mineral water'}), confidence=0.20000000000000004, lift=3.7979746835443047), OrderedStatistic(items_base=frozenset({'mineral water', 'chocolate', 'eggs'}), items_add=frozenset({'ground beef'}), confidence=0.29702970297029707, lift=3.023093354111531)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'ground beef', 'chocolate', 'mineral water'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'chocolate', 'mineral water'}), items_add=frozenset({'ground beef'}), confidence=0.34246575342465757, lift=3.4855300087358976), OrderedStatistic(items_base=frozenset({'ground beef', 'chocolate', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.30487804878048785, lift=3.1984478935698455)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'ground beef', 'chocolate', 'spaghetti'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'ground beef', 'chocolate'}), items_add=frozenset({'spaghetti'}), confidence=0.5348837209302326, lift=3.0721001460165964), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'chocolate', 'spaghetti'}), items_add=frozenset({'ground beef'}), confidence=0.3898305084745763, lift=3.967596531978015), OrderedStatistic(items_base=frozenset({'ground beef', 'chocolate', 'spaghetti'}), items_add=frozenset({'frozen vegetables'}), confidence=0.33333333333333337, lift=3.4969696969696975)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'milk', 'chocolate', 'mineral water'}), support=0.003999466737768298, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'chocolate', 'mineral water'}), items_add=frozenset({'milk'}), confidence=0.4109589041095891, lift=3.1714019956029094)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'milk', 'chocolate', 'spaghetti'}), support=0.0034662045060658577, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'chocolate', 'spaghetti'}), items_add=frozenset({'milk'}), confidence=0.44067796610169485, lift=3.4007463207086555), OrderedStatistic(items_base=frozenset({'milk', 'chocolate', 'spaghetti'}), items_add=frozenset({'frozen vegetables'}), confidence=0.3170731707317073, lift=3.3263858093126384)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'shrimp', 'chocolate', 'mineral water'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'chocolate', 'mineral water'}), items_add=frozenset({'shrimp'}), confidence=0.32876712328767127, lift=4.600899611531385), OrderedStatistic(items_base=frozenset({'shrimp', 'chocolate', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.4210526315789474, lift=4.417224880382776)]),\n",
              " RelationRecord(items=frozenset({'spaghetti', 'chocolate', 'mineral water', 'olive oil'}), support=0.0038661511798426876, ordered_statistics=[OrderedStatistic(items_base=frozenset({'chocolate', 'olive oil'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.23577235772357724, lift=3.947608159117306), OrderedStatistic(items_base=frozenset({'spaghetti', 'chocolate', 'mineral water'}), items_add=frozenset({'olive oil'}), confidence=0.2436974789915966, lift=3.700353825740822)]),\n",
              " RelationRecord(items=frozenset({'spaghetti', 'shrimp', 'chocolate', 'mineral water'}), support=0.0034662045060658577, ordered_statistics=[OrderedStatistic(items_base=frozenset({'spaghetti', 'chocolate', 'mineral water'}), items_add=frozenset({'shrimp'}), confidence=0.21848739495798317, lift=3.0576006522011783)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'mineral water', 'milk', 'eggs'}), support=0.0037328356219170776, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'mineral water', 'eggs'}), items_add=frozenset({'milk'}), confidence=0.411764705882353, lift=3.177620430888405)]),\n",
              " RelationRecord(items=frozenset({'mineral water', 'spaghetti', 'milk', 'frozen smoothie'}), support=0.003199573390214638, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'frozen smoothie'}), items_add=frozenset({'spaghetti', 'mineral water'}), confidence=0.22429906542056074, lift=3.7555073431241657), OrderedStatistic(items_base=frozenset({'spaghetti', 'frozen smoothie'}), items_add=frozenset({'milk', 'mineral water'}), confidence=0.20512820512820515, lift=4.274074074074075), OrderedStatistic(items_base=frozenset({'mineral water', 'spaghetti', 'frozen smoothie'}), items_add=frozenset({'milk'}), confidence=0.4705882352941177, lift=3.631566206729606), OrderedStatistic(items_base=frozenset({'spaghetti', 'milk', 'mineral water'}), items_add=frozenset({'frozen smoothie'}), confidence=0.2033898305084746, lift=3.211846565566459)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'ground beef', 'milk', 'mineral water'}), support=0.0037328356219170776, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'ground beef'}), items_add=frozenset({'milk', 'mineral water'}), confidence=0.22047244094488186, lift=4.593788276465442), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'ground beef', 'mineral water'}), items_add=frozenset({'milk'}), confidence=0.40579710144927533, lift=3.1315679608755294), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'milk', 'mineral water'}), items_add=frozenset({'ground beef'}), confidence=0.3373493975903614, lift=3.4334570302921317), OrderedStatistic(items_base=frozenset({'ground beef', 'milk', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.3373493975903614, lift=3.539101861993428)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'ground beef', 'milk', 'spaghetti'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'ground beef', 'milk'}), items_add=frozenset({'spaghetti'}), confidence=0.5348837209302326, lift=3.0721001460165964), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'milk', 'spaghetti'}), items_add=frozenset({'ground beef'}), confidence=0.3709677419354839, lift=3.7756160546242397), OrderedStatistic(items_base=frozenset({'ground beef', 'milk', 'spaghetti'}), items_add=frozenset({'frozen vegetables'}), confidence=0.3150684931506849, lift=3.305354919053549)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'ground beef', 'spaghetti', 'mineral water'}), support=0.004399413411545127, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'ground beef'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.25984251968503935, lift=4.350622187851519), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'spaghetti', 'mineral water'}), items_add=frozenset({'ground beef'}), confidence=0.3666666666666667, lift=3.7318407960199007)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'milk', 'mineral water', 'olive oil'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'olive oil'}), items_add=frozenset({'milk', 'mineral water'}), confidence=0.29411764705882354, lift=6.12826797385621), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'milk', 'mineral water'}), items_add=frozenset({'olive oil'}), confidence=0.30120481927710846, lift=4.573557387444516), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'mineral water', 'olive oil'}), items_add=frozenset({'milk'}), confidence=0.5102040816326531, lift=3.937284790459394), OrderedStatistic(items_base=frozenset({'milk', 'mineral water', 'olive oil'}), items_add=frozenset({'frozen vegetables'}), confidence=0.39062500000000006, lift=4.098011363636364)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'soup', 'milk', 'mineral water'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'soup'}), items_add=frozenset({'milk', 'mineral water'}), confidence=0.3833333333333333, lift=7.987175925925926), OrderedStatistic(items_base=frozenset({'soup', 'milk'}), items_add=frozenset({'frozen vegetables', 'mineral water'}), confidence=0.20175438596491227, lift=5.646864362398533), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'milk', 'mineral water'}), items_add=frozenset({'soup'}), confidence=0.27710843373493976, lift=5.484407286136631), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'soup', 'milk'}), items_add=frozenset({'mineral water'}), confidence=0.7666666666666666, lift=3.21631245339299), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'soup', 'mineral water'}), items_add=frozenset({'milk'}), confidence=0.6052631578947368, lift=4.670863114576565), OrderedStatistic(items_base=frozenset({'soup', 'milk', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.35937500000000006, lift=3.7701704545454553)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'spaghetti', 'milk', 'mineral water'}), support=0.004532728969470737, ordered_statistics=[OrderedStatistic(items_base=frozenset({'spaghetti', 'milk', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.28813559322033894, lift=3.0228043143297376)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'spaghetti', 'shrimp', 'mineral water'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'shrimp'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.2, lift=3.3486607142857148), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'spaghetti', 'mineral water'}), items_add=frozenset({'shrimp'}), confidence=0.2777777777777778, lift=3.8873341625207294), OrderedStatistic(items_base=frozenset({'spaghetti', 'shrimp', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.39062500000000006, lift=4.098011363636364)]),\n",
              " RelationRecord(items=frozenset({'frozen vegetables', 'spaghetti', 'tomatoes', 'mineral water'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'frozen vegetables', 'spaghetti', 'mineral water'}), items_add=frozenset({'tomatoes'}), confidence=0.2555555555555556, lift=3.7366904916612524), OrderedStatistic(items_base=frozenset({'frozen vegetables', 'tomatoes', 'mineral water'}), items_add=frozenset({'spaghetti'}), confidence=0.5227272727272727, lift=3.0022796881525826), OrderedStatistic(items_base=frozenset({'spaghetti', 'tomatoes', 'mineral water'}), items_add=frozenset({'frozen vegetables'}), confidence=0.32857142857142857, lift=3.447012987012987)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'spaghetti', 'milk', 'mineral water'}), support=0.004399413411545127, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'milk'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.2, lift=3.3486607142857148)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'spaghetti', 'mineral water', 'olive oil'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'olive oil'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.2169811320754717, lift=3.63298096361186), OrderedStatistic(items_base=frozenset({'spaghetti', 'mineral water', 'olive oil'}), items_add=frozenset({'ground beef'}), confidence=0.2987012987012987, lift=3.0401064335935435)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'spaghetti', 'mineral water', 'pancakes'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'pancakes'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.21100917431192662, lift=3.532990661861075)]),\n",
              " RelationRecord(items=frozenset({'ground beef', 'spaghetti', 'tomatoes', 'mineral water'}), support=0.0030662578322890282, ordered_statistics=[OrderedStatistic(items_base=frozenset({'ground beef', 'tomatoes'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.26136363636363635, lift=4.3760907061688314), OrderedStatistic(items_base=frozenset({'ground beef', 'tomatoes', 'mineral water'}), items_add=frozenset({'spaghetti'}), confidence=0.5609756097560976, lift=3.221958689724723), OrderedStatistic(items_base=frozenset({'spaghetti', 'tomatoes', 'mineral water'}), items_add=frozenset({'ground beef'}), confidence=0.32857142857142857, lift=3.344117076952898)]),\n",
              " RelationRecord(items=frozenset({'spaghetti', 'milk', 'mineral water', 'olive oil'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'spaghetti', 'milk', 'mineral water'}), items_add=frozenset({'olive oil'}), confidence=0.211864406779661, lift=3.216993755575379)]),\n",
              " RelationRecord(items=frozenset({'spaghetti', 'milk', 'tomatoes', 'mineral water'}), support=0.003332888948140248, ordered_statistics=[OrderedStatistic(items_base=frozenset({'milk', 'tomatoes'}), items_add=frozenset({'mineral water', 'spaghetti'}), confidence=0.2380952380952381, lift=3.9865008503401365), OrderedStatistic(items_base=frozenset({'spaghetti', 'milk', 'mineral water'}), items_add=frozenset({'tomatoes'}), confidence=0.211864406779661, lift=3.0978458387022165)])]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#uncomment it\n",
        "association_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "cQwkFmOj4YbU"
      },
      "outputs": [],
      "source": [
        "#finally forming a dataframe\n",
        "df_result=pd.DataFrame(association_results)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "TKCQpdyv4X7k",
        "outputId": "fc1f7333-6cf6-47cb-8e80-942988d63b71"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8ff89391-a522-4e2e-9a43-08a633442821\">\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>items</th>\n",
              "      <th>support</th>\n",
              "      <th>ordered_statistics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(chicken, light cream)</td>\n",
              "      <td>0.004533</td>\n",
              "      <td>[((light cream), (chicken), 0.2905982905982905...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(mushroom cream sauce, escalope)</td>\n",
              "      <td>0.005733</td>\n",
              "      <td>[((mushroom cream sauce), (escalope), 0.300699...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(escalope, pasta)</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>[((pasta), (escalope), 0.3728813559322034, 4.7...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(fromage blanc, honey)</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>[((fromage blanc), (honey), 0.2450980392156863...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(ground beef, herb &amp; pepper)</td>\n",
              "      <td>0.015998</td>\n",
              "      <td>[((herb &amp; pepper), (ground beef), 0.3234501347...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>(mineral water, olive oil, spaghetti, ground b...</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>[((olive oil, ground beef), (spaghetti, minera...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>(mineral water, spaghetti, ground beef, pancakes)</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>[((ground beef, pancakes), (spaghetti, mineral...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>(mineral water, tomatoes, spaghetti, ground beef)</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>[((tomatoes, ground beef), (spaghetti, mineral...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>(olive oil, milk, spaghetti, mineral water)</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>[((milk, spaghetti, mineral water), (olive oil...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>(tomatoes, milk, spaghetti, mineral water)</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>[((tomatoes, milk), (spaghetti, mineral water)...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ff89391-a522-4e2e-9a43-08a633442821')\"\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",
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            "text/plain": [
              "                                                items   support  \\\n",
              "0                              (chicken, light cream)  0.004533   \n",
              "1                    (mushroom cream sauce, escalope)  0.005733   \n",
              "2                                   (escalope, pasta)  0.005866   \n",
              "3                              (fromage blanc, honey)  0.003333   \n",
              "4                        (ground beef, herb & pepper)  0.015998   \n",
              "..                                                ...       ...   \n",
              "75  (mineral water, olive oil, spaghetti, ground b...  0.003066   \n",
              "76  (mineral water, spaghetti, ground beef, pancakes)  0.003066   \n",
              "77  (mineral water, tomatoes, spaghetti, ground beef)  0.003066   \n",
              "78        (olive oil, milk, spaghetti, mineral water)  0.003333   \n",
              "79         (tomatoes, milk, spaghetti, mineral water)  0.003333   \n",
              "\n",
              "                                   ordered_statistics  \n",
              "0   [((light cream), (chicken), 0.2905982905982905...  \n",
              "1   [((mushroom cream sauce), (escalope), 0.300699...  \n",
              "2   [((pasta), (escalope), 0.3728813559322034, 4.7...  \n",
              "3   [((fromage blanc), (honey), 0.2450980392156863...  \n",
              "4   [((herb & pepper), (ground beef), 0.3234501347...  \n",
              "..                                                ...  \n",
              "75  [((olive oil, ground beef), (spaghetti, minera...  \n",
              "76  [((ground beef, pancakes), (spaghetti, mineral...  \n",
              "77  [((tomatoes, ground beef), (spaghetti, mineral...  \n",
              "78  [((milk, spaghetti, mineral water), (olive oil...  \n",
              "79  [((tomatoes, milk), (spaghetti, mineral water)...  \n",
              "\n",
              "[80 rows x 3 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "GmNfOWB7Hmna"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stnbIt4b17gr",
        "outputId": "763c2d3f-e3e7-419a-a096-39aaf906084c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 80 Relation derived.\n"
          ]
        }
      ],
      "source": [
        "print(\"There are {} Relation derived.\".format(len(association_results)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndGgrBor2UWt",
        "outputId": "b4d3cbe0-02a4-438e-c83d-8caf609221de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "frozenset({'chicken', 'light cream'})\n",
            "frozenset({'mushroom cream sauce', 'escalope'})\n",
            "frozenset({'escalope', 'pasta'})\n",
            "frozenset({'fromage blanc', 'honey'})\n",
            "frozenset({'ground beef', 'herb & pepper'})\n",
            "frozenset({'tomato sauce', 'ground beef'})\n",
            "frozenset({'olive oil', 'light cream'})\n",
            "frozenset({'olive oil', 'whole wheat pasta'})\n",
            "frozenset({'pasta', 'shrimp'})\n",
            "frozenset({'avocado', 'milk', 'spaghetti'})\n",
            "frozenset({'cake', 'milk', 'burgers'})\n",
            "frozenset({'chocolate', 'turkey', 'burgers'})\n",
            "frozenset({'turkey', 'milk', 'burgers'})\n",
            "frozenset({'cake', 'frozen vegetables', 'tomatoes'})\n",
            "frozenset({'cereals', 'spaghetti', 'ground beef'})\n",
            "frozenset({'chicken', 'milk', 'ground beef'})\n",
            "frozenset({'chicken', 'milk', 'olive oil'})\n",
            "frozenset({'chicken', 'spaghetti', 'olive oil'})\n",
            "frozenset({'chocolate', 'frozen vegetables', 'shrimp'})\n",
            "frozenset({'chocolate', 'ground beef', 'herb & pepper'})\n",
            "frozenset({'chocolate', 'soup', 'milk'})\n",
            "frozenset({'spaghetti', 'cooking oil', 'ground beef'})\n",
            "frozenset({'eggs', 'ground beef', 'herb & pepper'})\n",
            "frozenset({'eggs', 'spaghetti', 'red wine'})\n",
            "frozenset({'ground beef', 'french fries', 'herb & pepper'})\n",
            "frozenset({'frozen vegetables', 'tomatoes', 'green tea'})\n",
            "frozenset({'frozen vegetables', 'spaghetti', 'ground beef'})\n",
            "frozenset({'frozen vegetables', 'milk', 'olive oil'})\n",
            "frozenset({'soup', 'frozen vegetables', 'milk'})\n",
            "frozenset({'tomatoes', 'frozen vegetables', 'milk'})\n",
            "frozenset({'frozen vegetables', 'mineral water', 'shrimp'})\n",
            "frozenset({'frozen vegetables', 'spaghetti', 'olive oil'})\n",
            "frozenset({'frozen vegetables', 'spaghetti', 'shrimp'})\n",
            "frozenset({'frozen vegetables', 'tomatoes', 'shrimp'})\n",
            "frozenset({'frozen vegetables', 'tomatoes', 'spaghetti'})\n",
            "frozenset({'grated cheese', 'spaghetti', 'ground beef'})\n",
            "frozenset({'ground beef', 'tomatoes', 'green tea'})\n",
            "frozenset({'milk', 'ground beef', 'herb & pepper'})\n",
            "frozenset({'mineral water', 'ground beef', 'herb & pepper'})\n",
            "frozenset({'spaghetti', 'ground beef', 'herb & pepper'})\n",
            "frozenset({'olive oil', 'milk', 'ground beef'})\n",
            "frozenset({'soup', 'milk', 'ground beef'})\n",
            "frozenset({'spaghetti', 'ground beef', 'pepper'})\n",
            "frozenset({'spaghetti', 'ground beef', 'shrimp'})\n",
            "frozenset({'tomato sauce', 'spaghetti', 'ground beef'})\n",
            "frozenset({'mineral water', 'spaghetti', 'light cream'})\n",
            "frozenset({'olive oil', 'milk', 'shrimp'})\n",
            "frozenset({'soup', 'olive oil', 'milk'})\n",
            "frozenset({'olive oil', 'milk', 'spaghetti'})\n",
            "frozenset({'soup', 'tomatoes', 'milk'})\n",
            "frozenset({'milk', 'spaghetti', 'whole wheat pasta'})\n",
            "frozenset({'soup', 'olive oil', 'mineral water'})\n",
            "frozenset({'olive oil', 'mineral water', 'whole wheat pasta'})\n",
            "frozenset({'olive oil', 'spaghetti', 'pancakes'})\n",
            "frozenset({'olive oil', 'tomatoes', 'spaghetti'})\n",
            "frozenset({'whole wheat rice', 'tomatoes', 'spaghetti'})\n",
            "frozenset({'chocolate', 'eggs', 'mineral water', 'ground beef'})\n",
            "frozenset({'chocolate', 'frozen vegetables', 'mineral water', 'ground beef'})\n",
            "frozenset({'chocolate', 'frozen vegetables', 'spaghetti', 'ground beef'})\n",
            "frozenset({'chocolate', 'frozen vegetables', 'milk', 'mineral water'})\n",
            "frozenset({'chocolate', 'frozen vegetables', 'milk', 'spaghetti'})\n",
            "frozenset({'chocolate', 'frozen vegetables', 'mineral water', 'shrimp'})\n",
            "frozenset({'chocolate', 'olive oil', 'spaghetti', 'mineral water'})\n",
            "frozenset({'chocolate', 'spaghetti', 'mineral water', 'shrimp'})\n",
            "frozenset({'eggs', 'frozen vegetables', 'milk', 'mineral water'})\n",
            "frozenset({'milk', 'spaghetti', 'mineral water', 'frozen smoothie'})\n",
            "frozenset({'mineral water', 'frozen vegetables', 'milk', 'ground beef'})\n",
            "frozenset({'frozen vegetables', 'milk', 'spaghetti', 'ground beef'})\n",
            "frozenset({'mineral water', 'frozen vegetables', 'spaghetti', 'ground beef'})\n",
            "frozenset({'frozen vegetables', 'milk', 'mineral water', 'olive oil'})\n",
            "frozenset({'soup', 'frozen vegetables', 'milk', 'mineral water'})\n",
            "frozenset({'frozen vegetables', 'milk', 'spaghetti', 'mineral water'})\n",
            "frozenset({'frozen vegetables', 'spaghetti', 'mineral water', 'shrimp'})\n",
            "frozenset({'frozen vegetables', 'tomatoes', 'spaghetti', 'mineral water'})\n",
            "frozenset({'mineral water', 'milk', 'spaghetti', 'ground beef'})\n",
            "frozenset({'mineral water', 'olive oil', 'spaghetti', 'ground beef'})\n",
            "frozenset({'mineral water', 'spaghetti', 'ground beef', 'pancakes'})\n",
            "frozenset({'mineral water', 'tomatoes', 'spaghetti', 'ground beef'})\n",
            "frozenset({'olive oil', 'milk', 'spaghetti', 'mineral water'})\n",
            "frozenset({'tomatoes', 'milk', 'spaghetti', 'mineral water'})\n"
          ]
        }
      ],
      "source": [
        "#association rules defined\n",
        "for i in range(0, len(association_results)):\n",
        "    print(association_results[i][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HBPli80PgFl5"
      },
      "outputs": [],
      "source": [
        "#The below code shows how the data are associated in a more detailed way...\n",
        "#association_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC_fPhjNgFYS",
        "outputId": "a8abfed5-4b9e-4c40-b0e4-ffc102977ef3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Rule: chicken -> light cream\n",
            "Support: 0.004532728969470737\n",
            "Confidence: 0.29059829059829057\n",
            "Lift: 4.84395061728395\n",
            "=====================================\n",
            "Rule: escalope -> mushroom cream sauce\n",
            "Support: 0.005732568990801226\n",
            "Confidence: 0.3006993006993007\n",
            "Lift: 3.790832696715049\n",
            "=====================================\n",
            "Rule: escalope -> pasta\n",
            "Support: 0.005865884548726837\n",
            "Confidence: 0.3728813559322034\n",
            "Lift: 4.700811850163794\n",
            "=====================================\n",
            "Rule: honey -> fromage blanc\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.2450980392156863\n",
            "Lift: 5.164270764485569\n",
            "=====================================\n",
            "Rule: ground beef -> herb & pepper\n",
            "Support: 0.015997866951073192\n",
            "Confidence: 0.3234501347708895\n",
            "Lift: 3.2919938411349285\n",
            "=====================================\n",
            "Rule: ground beef -> tomato sauce\n",
            "Support: 0.005332622317024397\n",
            "Confidence: 0.3773584905660377\n",
            "Lift: 3.840659481324083\n",
            "=====================================\n",
            "Rule: olive oil -> light cream\n",
            "Support: 0.003199573390214638\n",
            "Confidence: 0.20512820512820515\n",
            "Lift: 3.1147098515519573\n",
            "=====================================\n",
            "Rule: olive oil -> whole wheat pasta\n",
            "Support: 0.007998933475536596\n",
            "Confidence: 0.2714932126696833\n",
            "Lift: 4.122410097642296\n",
            "=====================================\n",
            "Rule: pasta -> shrimp\n",
            "Support: 0.005065991201173177\n",
            "Confidence: 0.3220338983050847\n",
            "Lift: 4.506672147735896\n",
            "=====================================\n",
            "Rule: avocado -> spaghetti\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.41666666666666663\n",
            "Lift: 3.215449245541838\n",
            "=====================================\n",
            "Rule: milk -> burgers\n",
            "Support: 0.0037328356219170776\n",
            "Confidence: 0.27999999999999997\n",
            "Lift: 3.211437308868501\n",
            "=====================================\n",
            "Rule: chocolate -> burgers\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.27058823529411763\n",
            "Lift: 3.1034898363014927\n",
            "=====================================\n",
            "Rule: milk -> burgers\n",
            "Support: 0.003199573390214638\n",
            "Confidence: 0.2823529411764706\n",
            "Lift: 3.2384241770102533\n",
            "=====================================\n",
            "Rule: tomatoes -> frozen vegetables\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.2987012987012987\n",
            "Lift: 4.367560314928736\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.6764705882352942\n",
            "Lift: 3.8853031258445188\n",
            "=====================================\n",
            "Rule: milk -> ground beef\n",
            "Support: 0.0038661511798426876\n",
            "Confidence: 0.40845070422535207\n",
            "Lift: 3.152046020981858\n",
            "=====================================\n",
            "Rule: milk -> olive oil\n",
            "Support: 0.0035995200639914677\n",
            "Confidence: 0.24324324324324323\n",
            "Lift: 3.693456614509246\n",
            "=====================================\n",
            "Rule: olive oil -> spaghetti\n",
            "Support: 0.0034662045060658577\n",
            "Confidence: 0.20155038759689922\n",
            "Lift: 3.0603835169318647\n",
            "=====================================\n",
            "Rule: chocolate -> frozen vegetables\n",
            "Support: 0.005332622317024397\n",
            "Confidence: 0.23255813953488375\n",
            "Lift: 3.2545123221103784\n",
            "=====================================\n",
            "Rule: ground beef -> chocolate\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.4411764705882354\n",
            "Lift: 4.4901827759597746\n",
            "=====================================\n",
            "Rule: milk -> chocolate\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.3947368421052632\n",
            "Lift: 3.0462150747238472\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.004799360085321957\n",
            "Confidence: 0.5714285714285714\n",
            "Lift: 3.2819951870487856\n",
            "=====================================\n",
            "Rule: ground beef -> herb & pepper\n",
            "Support: 0.0041327822956939075\n",
            "Confidence: 0.2066666666666667\n",
            "Lift: 4.178454627133872\n",
            "=====================================\n",
            "Rule: spaghetti -> red wine\n",
            "Support: 0.0037328356219170776\n",
            "Confidence: 0.5283018867924528\n",
            "Lift: 3.0342974370828397\n",
            "=====================================\n",
            "Rule: french fries -> herb & pepper\n",
            "Support: 0.003199573390214638\n",
            "Confidence: 0.23076923076923078\n",
            "Lift: 4.665768194070081\n",
            "=====================================\n",
            "Rule: tomatoes -> green tea\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.2314814814814815\n",
            "Lift: 3.38468341635983\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.008665511265164644\n",
            "Confidence: 0.31100478468899523\n",
            "Lift: 3.165328208890303\n",
            "=====================================\n",
            "Rule: milk -> olive oil\n",
            "Support: 0.004799360085321957\n",
            "Confidence: 0.20338983050847456\n",
            "Lift: 3.088314005352364\n",
            "=====================================\n",
            "Rule: milk -> frozen vegetables\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.5\n",
            "Lift: 3.858539094650206\n",
            "=====================================\n",
            "Rule: milk -> tomatoes\n",
            "Support: 0.0041327822956939075\n",
            "Confidence: 0.29523809523809524\n",
            "Lift: 3.0973160173160172\n",
            "=====================================\n",
            "Rule: mineral water -> frozen vegetables\n",
            "Support: 0.007199040127982935\n",
            "Confidence: 0.30508474576271183\n",
            "Lift: 3.200616332819722\n",
            "=====================================\n",
            "Rule: olive oil -> spaghetti\n",
            "Support: 0.005732568990801226\n",
            "Confidence: 0.20574162679425836\n",
            "Lift: 3.1240241752707125\n",
            "=====================================\n",
            "Rule: spaghetti -> frozen vegetables\n",
            "Support: 0.005999200106652446\n",
            "Confidence: 0.21531100478468898\n",
            "Lift: 3.0131489680782684\n",
            "=====================================\n",
            "Rule: tomatoes -> frozen vegetables\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.24000000000000002\n",
            "Lift: 3.5092397660818717\n",
            "=====================================\n",
            "Rule: tomatoes -> spaghetti\n",
            "Support: 0.006665777896280496\n",
            "Confidence: 0.23923444976076558\n",
            "Lift: 3.4980460188216425\n",
            "=====================================\n",
            "Rule: grated cheese -> spaghetti\n",
            "Support: 0.005332622317024397\n",
            "Confidence: 0.3225806451612903\n",
            "Lift: 3.283144395325426\n",
            "=====================================\n",
            "Rule: tomatoes -> ground beef\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.2072072072072072\n",
            "Lift: 3.0297490472929067\n",
            "=====================================\n",
            "Rule: milk -> ground beef\n",
            "Support: 0.0035995200639914677\n",
            "Confidence: 0.3913043478260869\n",
            "Lift: 3.9825968969382335\n",
            "=====================================\n",
            "Rule: mineral water -> ground beef\n",
            "Support: 0.006665777896280496\n",
            "Confidence: 0.39062500000000006\n",
            "Lift: 3.975682666214383\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.006399146780429276\n",
            "Confidence: 0.3934426229508197\n",
            "Lift: 4.004359721511667\n",
            "=====================================\n",
            "Rule: milk -> ground beef\n",
            "Support: 0.004932675643247567\n",
            "Confidence: 0.22424242424242427\n",
            "Lift: 3.40494417862839\n",
            "=====================================\n",
            "Rule: milk -> ground beef\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.4109589041095891\n",
            "Lift: 3.1714019956029094\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.33783783783783783\n",
            "Lift: 3.4384282518610876\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.005999200106652446\n",
            "Confidence: 0.5232558139534884\n",
            "Lift: 3.005315360233627\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.2169811320754717\n",
            "Lift: 5.535970992170453\n",
            "=====================================\n",
            "Rule: mineral water -> spaghetti\n",
            "Support: 0.003199573390214638\n",
            "Confidence: 0.20512820512820515\n",
            "Lift: 3.4345238095238098\n",
            "=====================================\n",
            "Rule: milk -> olive oil\n",
            "Support: 0.003199573390214638\n",
            "Confidence: 0.3934426229508197\n",
            "Lift: 3.0362274843149164\n",
            "=====================================\n",
            "Rule: milk -> olive oil\n",
            "Support: 0.0035995200639914677\n",
            "Confidence: 0.2109375\n",
            "Lift: 4.174781497361478\n",
            "=====================================\n",
            "Rule: milk -> spaghetti\n",
            "Support: 0.007199040127982935\n",
            "Confidence: 0.20300751879699247\n",
            "Lift: 3.0825089038385434\n",
            "=====================================\n",
            "Rule: milk -> tomatoes\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.21904761904761905\n",
            "Lift: 4.335293378565146\n",
            "=====================================\n",
            "Rule: milk -> spaghetti\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.4545454545454546\n",
            "Lift: 3.5077628133183696\n",
            "=====================================\n",
            "Rule: mineral water -> olive oil\n",
            "Support: 0.005199306759098787\n",
            "Confidence: 0.22543352601156072\n",
            "Lift: 3.4230301186492245\n",
            "=====================================\n",
            "Rule: mineral water -> whole wheat pasta\n",
            "Support: 0.0038661511798426876\n",
            "Confidence: 0.4027777777777778\n",
            "Lift: 6.115862573099416\n",
            "=====================================\n",
            "Rule: olive oil -> spaghetti\n",
            "Support: 0.005065991201173177\n",
            "Confidence: 0.20105820105820105\n",
            "Lift: 3.0529100529100526\n",
            "=====================================\n",
            "Rule: olive oil -> spaghetti\n",
            "Support: 0.004399413411545127\n",
            "Confidence: 0.6111111111111112\n",
            "Lift: 3.5099115194827295\n",
            "=====================================\n",
            "Rule: tomatoes -> spaghetti\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.2169811320754717\n",
            "Lift: 3.1726617382029496\n",
            "=====================================\n",
            "Rule: mineral water -> ground beef\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.20000000000000004\n",
            "Lift: 3.7979746835443047\n",
            "=====================================\n",
            "Rule: mineral water -> ground beef\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.34246575342465757\n",
            "Lift: 3.4855300087358976\n",
            "=====================================\n",
            "Rule: ground beef -> spaghetti\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.5348837209302326\n",
            "Lift: 3.0721001460165964\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.003999466737768298\n",
            "Confidence: 0.4109589041095891\n",
            "Lift: 3.1714019956029094\n",
            "=====================================\n",
            "Rule: milk -> spaghetti\n",
            "Support: 0.0034662045060658577\n",
            "Confidence: 0.44067796610169485\n",
            "Lift: 3.4007463207086555\n",
            "=====================================\n",
            "Rule: mineral water -> chocolate\n",
            "Support: 0.003199573390214638\n",
            "Confidence: 0.32876712328767127\n",
            "Lift: 4.600899611531385\n",
            "=====================================\n",
            "Rule: mineral water -> spaghetti\n",
            "Support: 0.0038661511798426876\n",
            "Confidence: 0.23577235772357724\n",
            "Lift: 3.947608159117306\n",
            "=====================================\n",
            "Rule: mineral water -> spaghetti\n",
            "Support: 0.0034662045060658577\n",
            "Confidence: 0.21848739495798317\n",
            "Lift: 3.0576006522011783\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.0037328356219170776\n",
            "Confidence: 0.411764705882353\n",
            "Lift: 3.177620430888405\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.003199573390214638\n",
            "Confidence: 0.22429906542056074\n",
            "Lift: 3.7555073431241657\n",
            "=====================================\n",
            "Rule: milk -> ground beef\n",
            "Support: 0.0037328356219170776\n",
            "Confidence: 0.22047244094488186\n",
            "Lift: 4.593788276465442\n",
            "=====================================\n",
            "Rule: milk -> ground beef\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.5348837209302326\n",
            "Lift: 3.0721001460165964\n",
            "=====================================\n",
            "Rule: mineral water -> ground beef\n",
            "Support: 0.004399413411545127\n",
            "Confidence: 0.25984251968503935\n",
            "Lift: 4.350622187851519\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.29411764705882354\n",
            "Lift: 6.12826797385621\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.3833333333333333\n",
            "Lift: 7.987175925925926\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.004532728969470737\n",
            "Confidence: 0.28813559322033894\n",
            "Lift: 3.0228043143297376\n",
            "=====================================\n",
            "Rule: mineral water -> spaghetti\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.2\n",
            "Lift: 3.3486607142857148\n",
            "=====================================\n",
            "Rule: mineral water -> spaghetti\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.2555555555555556\n",
            "Lift: 3.7366904916612524\n",
            "=====================================\n",
            "Rule: milk -> ground beef\n",
            "Support: 0.004399413411545127\n",
            "Confidence: 0.2\n",
            "Lift: 3.3486607142857148\n",
            "=====================================\n",
            "Rule: mineral water -> ground beef\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.2169811320754717\n",
            "Lift: 3.63298096361186\n",
            "=====================================\n",
            "Rule: mineral water -> ground beef\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.21100917431192662\n",
            "Lift: 3.532990661861075\n",
            "=====================================\n",
            "Rule: mineral water -> ground beef\n",
            "Support: 0.0030662578322890282\n",
            "Confidence: 0.26136363636363635\n",
            "Lift: 4.3760907061688314\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.211864406779661\n",
            "Lift: 3.216993755575379\n",
            "=====================================\n",
            "Rule: milk -> mineral water\n",
            "Support: 0.003332888948140248\n",
            "Confidence: 0.2380952380952381\n",
            "Lift: 3.9865008503401365\n",
            "=====================================\n"
          ]
        }
      ],
      "source": [
        "for item in association_results:\n",
        "    # first index of the inner list\n",
        "    # Contains base item and add item\n",
        "    pair = item[0]\n",
        "    items = [x for x in pair]\n",
        "    print(\"Rule: \" + items[0] + \" -> \" + items[1])\n",
        "\n",
        "    # second index of the inner list\n",
        "    print(\"Support: \" + str(item[1]))\n",
        "\n",
        "    # third index of the list located at 0th\n",
        "    # of the third index of the inner list\n",
        "\n",
        "    print(\"Confidence: \" + str(item[2][0][2]))\n",
        "    print(\"Lift: \" + str(item[2][0][3]))\n",
        "    print(\"=====================================\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T2c0qfoFgFJ9"
      },
      "outputs": [],
      "source": [
        "#analysis can be done from the code below which shows each parameter of apriori algorithm-->>>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pm90rBI0JHoR"
      },
      "outputs": [],
      "source": [
        "\n",
        "# putting output into a pandas dataframe\n",
        "def inspect(association_results):\n",
        "    lhs         = [tuple(result[2][0][0])[0] for result in association_results]\n",
        "    rhs         = [tuple(result[2][0][1])[0] for result in association_results]\n",
        "    support    = [result[1] for result in association_results]\n",
        "    confidence = [result[2][0][2] for result in association_results]\n",
        "    lift       = [result[2][0][3] for result in association_results]\n",
        "    return list(zip(lhs, rhs, support, confidence, lift))\n",
        "output_DataFrame = pd.DataFrame(inspect(association_results), columns = ['Left_Hand_Side', 'Right_Hand_Side', 'Support', 'Confidence', 'Lift'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "v--O4nmEJHZQ",
        "outputId": "8defc171-abfc-417e-cc22-81988d39af64"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Left_Hand_Side</th>\n",
              "      <th>Right_Hand_Side</th>\n",
              "      <th>Support</th>\n",
              "      <th>Confidence</th>\n",
              "      <th>Lift</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>light cream</td>\n",
              "      <td>chicken</td>\n",
              "      <td>0.004533</td>\n",
              "      <td>0.290598</td>\n",
              "      <td>4.843951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>mushroom cream sauce</td>\n",
              "      <td>escalope</td>\n",
              "      <td>0.005733</td>\n",
              "      <td>0.300699</td>\n",
              "      <td>3.790833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>pasta</td>\n",
              "      <td>escalope</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>0.372881</td>\n",
              "      <td>4.700812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>olive oil</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>0.216981</td>\n",
              "      <td>3.632981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>ground beef</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>0.211009</td>\n",
              "      <td>3.532991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>tomatoes</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>0.261364</td>\n",
              "      <td>4.376091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>milk</td>\n",
              "      <td>olive oil</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>0.211864</td>\n",
              "      <td>3.216994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>milk</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>0.238095</td>\n",
              "      <td>3.986501</td>\n",
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            "text/plain": [
              "          Left_Hand_Side Right_Hand_Side   Support  Confidence      Lift\n",
              "0            light cream         chicken  0.004533    0.290598  4.843951\n",
              "1   mushroom cream sauce        escalope  0.005733    0.300699  3.790833\n",
              "2                  pasta        escalope  0.005866    0.372881  4.700812\n",
              "3          fromage blanc           honey  0.003333    0.245098  5.164271\n",
              "4          herb & pepper     ground beef  0.015998    0.323450  3.291994\n",
              "..                   ...             ...       ...         ...       ...\n",
              "75             olive oil   mineral water  0.003066    0.216981  3.632981\n",
              "76           ground beef   mineral water  0.003066    0.211009  3.532991\n",
              "77              tomatoes   mineral water  0.003066    0.261364  4.376091\n",
              "78                  milk       olive oil  0.003333    0.211864  3.216994\n",
              "79                  milk   mineral water  0.003333    0.238095  3.986501\n",
              "\n",
              "[80 rows x 5 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
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        }
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        "output_DataFrame"
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      "metadata": {
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          "height": 363
        },
        "id": "mzy3MK_-JHEP",
        "outputId": "3aaef185-4801-4847-84ef-e6c0e020d31f"
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              "      <th>Left_Hand_Side</th>\n",
              "      <th>Right_Hand_Side</th>\n",
              "      <th>Support</th>\n",
              "      <th>Confidence</th>\n",
              "      <th>Lift</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>frozen vegetables</td>\n",
              "      <td>milk</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>0.383333</td>\n",
              "      <td>7.987176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>olive oil</td>\n",
              "      <td>milk</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>0.294118</td>\n",
              "      <td>6.128268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>mineral water</td>\n",
              "      <td>olive oil</td>\n",
              "      <td>0.003866</td>\n",
              "      <td>0.402778</td>\n",
              "      <td>6.115863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>tomato sauce</td>\n",
              "      <td>ground beef</td>\n",
              "      <td>0.003066</td>\n",
              "      <td>0.216981</td>\n",
              "      <td>5.535971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fromage blanc</td>\n",
              "      <td>honey</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>0.245098</td>\n",
              "      <td>5.164271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>light cream</td>\n",
              "      <td>chicken</td>\n",
              "      <td>0.004533</td>\n",
              "      <td>0.290598</td>\n",
              "      <td>4.843951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>pasta</td>\n",
              "      <td>escalope</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>0.372881</td>\n",
              "      <td>4.700812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>french fries</td>\n",
              "      <td>herb &amp; pepper</td>\n",
              "      <td>0.003200</td>\n",
              "      <td>0.230769</td>\n",
              "      <td>4.665768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>mineral water</td>\n",
              "      <td>shrimp</td>\n",
              "      <td>0.003200</td>\n",
              "      <td>0.328767</td>\n",
              "      <td>4.600900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>ground beef</td>\n",
              "      <td>milk</td>\n",
              "      <td>0.003733</td>\n",
              "      <td>0.220472</td>\n",
              "      <td>4.593788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       Left_Hand_Side Right_Hand_Side   Support  Confidence      Lift\n",
              "70  frozen vegetables            milk  0.003066    0.383333  7.987176\n",
              "69          olive oil            milk  0.003333    0.294118  6.128268\n",
              "52      mineral water       olive oil  0.003866    0.402778  6.115863\n",
              "44       tomato sauce     ground beef  0.003066    0.216981  5.535971\n",
              "3       fromage blanc           honey  0.003333    0.245098  5.164271\n",
              "0         light cream         chicken  0.004533    0.290598  4.843951\n",
              "2               pasta        escalope  0.005866    0.372881  4.700812\n",
              "24       french fries   herb & pepper  0.003200    0.230769  4.665768\n",
              "61      mineral water          shrimp  0.003200    0.328767  4.600900\n",
              "66        ground beef            milk  0.003733    0.220472  4.593788"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output_DataFrame.nlargest(n = 10, columns = 'Lift') #lift shows the actual value we are looking for"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WUCE4RAgQiNx"
      },
      "outputs": [],
      "source": [
        "# #Results\n",
        "# For soup and mineral water-->> Support=0.003066. It shows popularity\n",
        "\n",
        "# confidence =0.38333. It means out of all the transaction of soup,38.33 percent \n",
        "# people tend to buy mineral water too\n",
        "\n",
        "# Lift=7.987. It means the mineral water is 7.89 times more likely to be bought \n",
        "# by the customer \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jBAkV5MnkGwy"
      },
      "outputs": [],
      "source": []
    }
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