Notebooks/09_Apriori.ipynb

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        "## <span style=\"color : green\"> Association Rule Mining </span>\n",
        "\n",
        "# <center> Table of Contents </center>\n",
        "\n",
        "1. Encode the data into Transcript\n",
        "1. Train the apriori algorithm and compute t\n"
      ]
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      "source": [
        "### **Apriori Algorithm**"
      ]
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        "###Description\n",
        "The Apriori algorithm uses frequent itemsets to generate association rules, and it is designed to work on the databases that contain transactions. With the help of these association rule, it determines how strongly or how weakly two objects are connected. This algorithm uses a breadth-first search and Hash Tree to calculate the itemset associations efficiently. It is the iterative process for finding the frequent itemsets from the large dataset."
      ]
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          "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=04468732c5dec7adaae5934ab9f10b06387f607f281532277fac82cf65635c65\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": [
        "! pip install apyori"
      ]
    },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from apyori import apriori\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5a314b6",
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      "source": [
        "## 1) Read the data"
      ]
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              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>Item</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>mineral water,milk,energy bar,whole wheat rice...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>low fat yogurt</td>\n",
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            "text/plain": [
              "   PID                                               Item\n",
              "0    1                             burgers,meatballs,eggs\n",
              "1    2  mineral water,milk,energy bar,whole wheat rice...\n",
              "2    3                                     low fat yogurt\n",
              "3    4                     whole wheat pasta,french fries\n",
              "4    5                           soup,light cream,shallot"
            ]
          },
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          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=pd.read_csv(\"Shop1.csv\")\n",
        "df.head()"
      ]
    },
    {
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      "id": "83362417",
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      "source": [
        "## 2) Display the candidate set and frequency set for every iteration"
      ]
    },
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          "text": [
            "Candidate Set:  [' ', 'i', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [' ', 'r', 'o']\n",
            "Support:  0.84\n",
            "Candidate Set:  ['e', ' ', 'r', ',']\n",
            "Support:  0.84\n",
            "Candidate Set:  ['r', ' ', 'i', ',']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ' ', 'r', ',']\n",
            "Support:  0.8\n",
            "Candidate Set:  [' ', 'r', ',', 'o']\n",
            "Support:  0.8\n",
            "Candidate Set:  [' ', 'i', 'r', 'a']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ' ', 'r', 'a']\n",
            "Support:  0.8\n",
            "Candidate Set:  [' ', 'r', 'o', 'a']\n",
            "Support:  0.84\n",
            "Candidate Set:  ['e', ' ', 'i', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['e', 'n', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['e', ' ', 'r', 'o']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['t', ' ', 'i', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [' ', 'r', 'o', 'l']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['t', 'n', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['t', ' ', 'r', 'o']\n",
            "Support:  0.84\n",
            "Candidate Set:  [',', 'e', 'a', ' ', 'r']\n",
            "Support:  0.84\n",
            "Candidate Set:  [',', 'a', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ',', 'a', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'o', 'a', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'e', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ',', 'e', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'o', 'e', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'e', 't', ' ', 'r']\n",
            "Support:  0.84\n",
            "Candidate Set:  [',', 't', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ',', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'o', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['e', 'a', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', 'e', 'a', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['o', 'e', 'a', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['t', 'a', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['o', 'a', ' ', 'r', 'l']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', 't', 'a', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['o', 't', 'a', ' ', 'r']\n",
            "Support:  0.84\n",
            "Candidate Set:  ['e', 't', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', 'e', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['o', 'e', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['o', 't', ' ', 'r', 'l']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'e', 'a', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ',', 'e', 'a', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'o', 'e', 'a', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'e', 'a', 't', ' ', 'r']\n",
            "Support:  0.84\n",
            "Candidate Set:  [',', 'a', 't', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ',', 'a', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'o', 'a', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'e', 't', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ',', 'e', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'o', 'e', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['e', 'a', 't', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', 'e', 'a', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['o', 'e', 'a', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['o', 'a', 't', ' ', 'r', 'l']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'e', 'a', 't', ' ', 'r', 'i']\n",
            "Support:  0.8\n",
            "Candidate Set:  ['n', ',', 'e', 'a', 't', ' ', 'r']\n",
            "Support:  0.8\n",
            "Candidate Set:  [',', 'o', 'e', 'a', 't', ' ', 'r']\n",
            "Support:  0.8\n"
          ]
        }
      ],
      "source": [
        "from apyori import apriori\n",
        "import pandas as pd\n",
        "\n",
        "items = df['Item'].values\n",
        "data_list = items.tolist()\n",
        "# data_list = [x.split(',') for x in items.tolist() ]\n",
        "# data_list = reduce(lambda acc, sublist: acc + sublist, data_list)\n",
        "\n",
        "rules = apriori(data_list, min_support=0.8, \\\n",
        "                min_confidence=0.8, \\\n",
        "                min_lift=1.1, \\\n",
        "                min_length=2)\n",
        "\n",
        "for rule in rules:\n",
        "    print(\"Candidate Set: \", list(rule.items))\n",
        "    print(\"Support: \", rule.support)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "350674a5",
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        "id": "350674a5"
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      "source": [
        "## 3) Display the association rules"
      ]
    },
    {
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      "id": "24936632",
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      "outputs": [
        {
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          "text": [
            "RelationRecord(items=frozenset({' ', 'r', 'o'}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)])\n",
            "RelationRecord(items=frozenset({'e', ' ', 'r', ','}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({' ', ','}), confidence=0.9545454545454545, lift=1.1363636363636362)])\n",
            "RelationRecord(items=frozenset({' ', 'r', 'o', 'a'}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'a', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({' ', 'a'}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({' ', 'a'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'a', 'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)])\n",
            "RelationRecord(items=frozenset({'t', ' ', 'r', 'o'}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'t', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'t', ' '}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({'t', ' '}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'t', 'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)])\n",
            "RelationRecord(items=frozenset({',', 'e', 'a', ' ', 'r'}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 'a', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({',', 'r', 'a'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', 'a', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({',', ' ', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({' ', ',', 'a'}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', ' ', 'a'}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({',', 'r', 'a'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 'a', 'r'}), items_add=frozenset({',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)])\n",
            "RelationRecord(items=frozenset({',', 'e', 't', ' ', 'r'}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 't', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({'t', ',', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', 't', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({'t', ',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'t', ' ', ','}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', 't', ' '}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'t', ',', 'r'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 't', 'r'}), items_add=frozenset({',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)])\n",
            "RelationRecord(items=frozenset({'o', 't', 'a', ' ', 'r'}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'t', 'r', 'o', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({' ', 'a'}), items_add=frozenset({'t', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'t', ' '}), items_add=frozenset({'a', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({'t', ' ', 'a'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'t', ' ', 'a'}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'a', 'r', 'o'}), items_add=frozenset({'t', ' '}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'t', 'r', 'o'}), items_add=frozenset({'a', ' '}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'t', 'a', 'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)])\n",
            "RelationRecord(items=frozenset({',', 'e', 'a', 't', ' ', 'r'}), support=0.84, ordered_statistics=[OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 't', 'a', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({'t', ',', 'r', 'a'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', 't', 'a', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({'t', ',', ' ', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({' ', ',', 'a'}), items_add=frozenset({'e', 't', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'t', ' ', ','}), items_add=frozenset({'e', 'a', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', ' ', 'a'}), items_add=frozenset({'t', ',', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', 't', ' '}), items_add=frozenset({',', 'r', 'a'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({',', 'r', 'a'}), items_add=frozenset({'e', 't', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'t', ',', 'r'}), items_add=frozenset({'e', 'a', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 'a', 'r'}), items_add=frozenset({'t', ',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 't', 'r'}), items_add=frozenset({',', ' ', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'t', ' ', ',', 'a'}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'e', 't', ' ', 'a'}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365), OrderedStatistic(items_base=frozenset({'t', ',', 'r', 'a'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362), OrderedStatistic(items_base=frozenset({'e', 't', 'a', 'r'}), items_add=frozenset({',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)])\n"
          ]
        }
      ],
      "source": [
        "from apyori import apriori\n",
        "import pandas as pd\n",
        "\n",
        "items = df['Item'].values\n",
        "data_list = items.tolist()\n",
        "\n",
        "rules = apriori(data_list, min_support=0.83, \\\n",
        "                min_confidence=0.9, \\\n",
        "                min_lift=1.1, \\\n",
        "                min_length=2, verbose=True)\n",
        "\n",
        "for rule in rules :\n",
        "    print(rule)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ccf625f",
      "metadata": {
        "id": "1ccf625f"
      },
      "source": [
        "## 4) Find all the rules of these subsets that have higher confidence value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a8a35c0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8a35c0f",
        "outputId": "2f7b18b9-0788-4758-9142-3aa06d39e4be",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({' ', ','}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'a', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({' ', 'a'}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({' ', 'a'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'a', 'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'t', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'t', ' '}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({'t', ' '}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'t', 'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 'a', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({',', 'r', 'a'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', 'a', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({',', ' ', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({' ', ',', 'a'}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', ' ', 'a'}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({',', 'r', 'a'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 'a', 'r'}), items_add=frozenset({',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 't', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({'t', ',', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', 't', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({'t', ',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'t', ' ', ','}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', 't', ' '}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'t', ',', 'r'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 't', 'r'}), items_add=frozenset({',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({' '}), items_add=frozenset({'t', 'r', 'o', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({' ', 'a'}), items_add=frozenset({'t', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'t', ' '}), items_add=frozenset({'a', 'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'r', 'o'}), items_add=frozenset({'t', ' ', 'a'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'t', ' ', 'a'}), items_add=frozenset({'r', 'o'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'a', 'r', 'o'}), items_add=frozenset({'t', ' '}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'t', 'r', 'o'}), items_add=frozenset({'a', ' '}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'t', 'a', 'r', 'o'}), items_add=frozenset({' '}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({' ', ','}), items_add=frozenset({'e', 't', 'a', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', ' '}), items_add=frozenset({'t', ',', 'r', 'a'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({',', 'r'}), items_add=frozenset({'e', 't', 'a', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 'r'}), items_add=frozenset({'t', ',', ' ', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({' ', ',', 'a'}), items_add=frozenset({'e', 't', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'t', ' ', ','}), items_add=frozenset({'e', 'a', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', ' ', 'a'}), items_add=frozenset({'t', ',', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', 't', ' '}), items_add=frozenset({',', 'r', 'a'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({',', 'r', 'a'}), items_add=frozenset({'e', 't', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'t', ',', 'r'}), items_add=frozenset({'e', 'a', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 'a', 'r'}), items_add=frozenset({'t', ',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 't', 'r'}), items_add=frozenset({',', ' ', 'a'}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'t', ' ', ',', 'a'}), items_add=frozenset({'e', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'e', 't', ' ', 'a'}), items_add=frozenset({',', 'r'}), confidence=1.0, lift=1.1363636363636365)\n",
            "OrderedStatistic(items_base=frozenset({'t', ',', 'r', 'a'}), items_add=frozenset({'e', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n",
            "OrderedStatistic(items_base=frozenset({'e', 't', 'a', 'r'}), items_add=frozenset({',', ' '}), confidence=0.9545454545454545, lift=1.1363636363636362)\n"
          ]
        }
      ],
      "source": [
        "items = df['Item'].values\n",
        "data_list = items.tolist()\n",
        "\n",
        "rules = apriori(data_list, min_support=0.83, \\\n",
        "                min_confidence=0.9, \\\n",
        "                min_lift=1.1, \\\n",
        "                min_length=2)\n",
        "\n",
        "for rule in rules :\n",
        "    for ordered_statistic in rule.ordered_statistics:\n",
        "        if ordered_statistic.confidence > 0.9:\n",
        "            print(ordered_statistic)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "NISYb7_d2Xqi",
      "metadata": {
        "id": "NISYb7_d2Xqi"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.13"
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  },
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}