nlp_mini_projectNotebook.ipynb

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      "source": [
        "**Natural Language Processing Mini Project**\n"
      ]
    },
    {
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      "cell_type": "markdown",
      "metadata": {
        "id": "s5kttDrRNwbn"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "SgexbesnLttD"
      },
      "source": [
        "**Topic :** NLP based Market Basket Analysis : Stores to analyze the products their customers purchased/customers’ baskets to know how they can generate more profit."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "omeD4KwmL1wU"
      },
      "source": [
        "**Topic Distribution amongst team members:**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "*   Data transformation (Hot encoding of the data)\n",
        "*   Apriori Implementation\n",
        "\n",
        "\n",
        ">  1.   mlxtend\n",
        ">  2.   pyarules\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*   FP-Tree Implementation\n",
        "*   Jaccard Similarity of items\n",
        "*   Jaccard Similarity of users and recommendation\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "d-R9YqJoOmyV"
      },
      "source": [
        "**Apriori Implementation**"
      ]
    },
    {
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      "cell_type": "markdown",
      "metadata": {
        "id": "-Rzmv-4dP4D_"
      },
      "source": [
        "**1.   mlxtend: Ruchi**"
      ]
    },
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.9/dist-packages (0.14.0)\n",
            "Collecting mlxtend\n",
            "  Downloading mlxtend-0.21.0-py2.py3-none-any.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from mlxtend) (3.5.3)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.24.2->mlxtend) (2022.7.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=1.0.2->mlxtend) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.15.0)\n",
            "Installing collected packages: mlxtend\n",
            "  Attempting uninstall: mlxtend\n",
            "    Found existing installation: mlxtend 0.14.0\n",
            "    Uninstalling mlxtend-0.14.0:\n",
            "      Successfully uninstalled mlxtend-0.14.0\n",
            "Successfully installed mlxtend-0.21.0\n"
          ]
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        "%pip install mlxtend --upgrade"
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      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "from mlxtend.frequent_patterns import apriori, association_rules"
      ]
    },
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>transactions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>shrimp,almonds,avocado,vegetables mix,green gr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>burgers,meatballs,eggs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chutney</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>,avocado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>mineral water,milk,energy bar,whole wheat rice...</td>\n",
              "    </tr>\n",
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              "                                        transactions\n",
              "0  shrimp,almonds,avocado,vegetables mix,green gr...\n",
              "1                             burgers,meatballs,eggs\n",
              "2                                            chutney\n",
              "3                                           ,avocado\n",
              "4  mineral water,milk,energy bar,whole wheat rice..."
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#reading file in other format for implementation of apriori, file has only one \n",
        "df = pd.read_csv(\"/content/sample_data/data.csv\", sep = ',',names=[\"transactions\"])\n",
        "df.head()"
      ]
    },
    {
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          "text": [
            "[['shrimp', 'almonds', 'avocado', 'vegetables mix', 'green grapes', 'whole weat flour', 'yams', 'cottage cheese', 'energy drink', 'tomato juice', 'low fat yogurt', 'green tea', 'honey', 'salad', 'mineral water', 'salmon', 'antioxydant juice', 'frozen smoothie', 'spinach', 'olive oil'], ['burgers', 'meatballs', 'eggs']]\n"
          ]
        }
      ],
      "source": [
        "df = list(df[\"transactions\"].apply(lambda x:x.split(\",\")))\n",
        "print(df[0:2]) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "4e959e67-f3e2-4e08-e5d4-bfac836183c2"
      },
      "outputs": [
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          "text": [
            "[[False  True  True ...  True False False]\n",
            " [False False False ... False False False]\n",
            " [False False False ... False False False]\n",
            " ...\n",
            " [False False False ... False False False]\n",
            " [False False False ... False False False]\n",
            " [False False False ... False  True False]]\n"
          ]
        }
      ],
      "source": [
        "#hot encoding the dataset i.e. converting dataset into the form of 0s and 1s\n",
        "tr = TransactionEncoder()\n",
        "#fit and transform\n",
        "tr_arr = tr.fit(df).transform(df)\n",
        "print(tr_arr)\n"
      ]
    },
    {
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        {
          "data": {
            "text/plain": [
              "['',\n",
              " 'almonds',\n",
              " 'antioxydant juice',\n",
              " 'asparagus',\n",
              " 'avocado',\n",
              " 'babies food',\n",
              " 'bacon',\n",
              " 'barbecue sauce',\n",
              " 'black tea',\n",
              " 'blueberries',\n",
              " 'body spray',\n",
              " 'bramble',\n",
              " 'brownies',\n",
              " 'bug spray',\n",
              " 'burger sauce',\n",
              " 'burgers',\n",
              " 'butter',\n",
              " 'cake',\n",
              " 'candy bars',\n",
              " 'carrots',\n",
              " 'cauliflower',\n",
              " 'cereals',\n",
              " 'champagne',\n",
              " 'chicken',\n",
              " 'chili',\n",
              " 'chocolate',\n",
              " 'chocolate bread',\n",
              " 'chutney',\n",
              " 'cider',\n",
              " 'clothes accessories',\n",
              " 'cookies',\n",
              " 'cooking oil',\n",
              " 'corn',\n",
              " 'cottage cheese',\n",
              " 'cream',\n",
              " 'dessert wine',\n",
              " 'eggplant',\n",
              " 'eggs',\n",
              " 'energy bar',\n",
              " 'energy drink',\n",
              " 'escalope',\n",
              " 'extra dark chocolate',\n",
              " 'flax seed',\n",
              " 'french fries',\n",
              " 'french wine',\n",
              " 'fresh bread',\n",
              " 'fresh tuna',\n",
              " 'fromage blanc',\n",
              " 'frozen smoothie',\n",
              " 'frozen vegetables',\n",
              " 'gluten free bar',\n",
              " 'grated cheese',\n",
              " 'green beans',\n",
              " 'green grapes',\n",
              " 'green tea',\n",
              " 'gums',\n",
              " 'ham',\n",
              " 'hand protein bar',\n",
              " 'herb & pepper',\n",
              " 'honey',\n",
              " 'hot dogs',\n",
              " 'ketchup',\n",
              " 'light cream',\n",
              " 'light mayo',\n",
              " 'low fat yogurt',\n",
              " 'magazines',\n",
              " 'mashed potato',\n",
              " 'mayonnaise',\n",
              " 'meatballs',\n",
              " 'melons',\n",
              " 'milk',\n",
              " 'mineral water',\n",
              " 'mint',\n",
              " 'mint green tea',\n",
              " 'muffins',\n",
              " 'mushroom cream sauce',\n",
              " 'napkins',\n",
              " 'nonfat milk',\n",
              " 'oatmeal',\n",
              " 'oil',\n",
              " 'olive oil',\n",
              " 'pancakes',\n",
              " 'parmesan cheese',\n",
              " 'pasta',\n",
              " 'pepper',\n",
              " 'pet food',\n",
              " 'pickles',\n",
              " 'protein bar',\n",
              " 'red wine',\n",
              " 'rice',\n",
              " 'salad',\n",
              " 'salmon',\n",
              " 'salt',\n",
              " 'sandwich',\n",
              " 'shallot',\n",
              " 'shampoo',\n",
              " 'shrimp',\n",
              " 'soda',\n",
              " 'soup',\n",
              " 'spaghetti',\n",
              " 'sparkling water',\n",
              " 'spinach',\n",
              " 'strawberries',\n",
              " 'strong cheese',\n",
              " 'tea',\n",
              " 'tomato juice',\n",
              " 'tomato sauce',\n",
              " 'tomatoes',\n",
              " 'toothpaste',\n",
              " 'vegetables mix',\n",
              " 'water spray',\n",
              " 'white wine',\n",
              " 'whole weat flour',\n",
              " 'whole wheat pasta',\n",
              " 'whole wheat rice',\n",
              " 'yams',\n",
              " 'yogurt cake',\n",
              " 'zucchini']"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
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        "tr.columns_"
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            "[[True, True, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, True, False, False, False, False, False, False, False, False, True, False, False, False, False, True, True, False, False, False, False, True, False, False, False, False, True, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, True, False, False, False, False, True, False, False, False, True, False, False, False, True, False, False, True, False, False, True, False, False], [False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False], [False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False]]\n"
          ]
        }
      ],
      "source": [
        "tr_arr = list(tr_arr)\n",
        "for j in range(len(list(tr_arr))):\n",
        "  tr_arr[j] = list(tr_arr[j])[1:len(list(tr_arr[j]))]\n",
        "print(tr_arr[0:5])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkhwJkuEXsjb",
        "outputId": "b2ad60d8-12a4-4eba-de83-eddebda91bd6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['almonds', 'antioxydant juice', 'asparagus', 'avocado', 'babies food', 'bacon', 'barbecue sauce', 'black tea', 'blueberries', 'body spray', 'bramble', 'brownies', 'bug spray', 'burger sauce', 'burgers', 'butter', 'cake', 'candy bars', 'carrots', 'cauliflower', 'cereals', 'champagne', 'chicken', 'chili', 'chocolate', 'chocolate bread', 'chutney', 'cider', 'clothes accessories', 'cookies', 'cooking oil', 'corn', 'cottage cheese', 'cream', 'dessert wine', 'eggplant', 'eggs', 'energy bar', 'energy drink', 'escalope', 'extra dark chocolate', 'flax seed', 'french fries', 'french wine', 'fresh bread', 'fresh tuna', 'fromage blanc', 'frozen smoothie', 'frozen vegetables', 'gluten free bar', 'grated cheese', 'green beans', 'green grapes', 'green tea', 'gums', 'ham', 'hand protein bar', 'herb & pepper', 'honey', 'hot dogs', 'ketchup', 'light cream', 'light mayo', 'low fat yogurt', 'magazines', 'mashed potato', 'mayonnaise', 'meatballs', 'melons', 'milk', 'mineral water', 'mint', 'mint green tea', 'muffins', 'mushroom cream sauce', 'napkins', 'nonfat milk', 'oatmeal', 'oil', 'olive oil', 'pancakes', 'parmesan cheese', 'pasta', 'pepper', 'pet food', 'pickles', 'protein bar', 'red wine', 'rice', 'salad', 'salmon', 'salt', 'sandwich', 'shallot', 'shampoo', 'shrimp', 'soda', 'soup', 'spaghetti', 'sparkling water', 'spinach', 'strawberries', 'strong cheese', 'tea', 'tomato juice', 'tomato sauce', 'tomatoes', 'toothpaste', 'vegetables mix', 'water spray', 'white wine', 'whole weat flour', 'whole wheat pasta', 'whole wheat rice', 'yams', 'yogurt cake', 'zucchini']\n",
            "117\n"
          ]
        }
      ],
      "source": [
        "print(tr.columns_[1:])\n",
        "print(len(tr_arr[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "i52SDIXRj7lC",
        "outputId": "f62f35f0-f8b1-4a77-cbed-c932b18d2b80"
      },
      "outputs": [
        {
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7467 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79427413-bcb3-4b7d-badb-af3118af9f7d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79427413-bcb3-4b7d-badb-af3118af9f7d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-79427413-bcb3-4b7d-badb-af3118af9f7d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
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              "      almonds  antioxydant juice  asparagus  avocado  babies food  bacon  \\\n",
              "0        True               True      False     True        False  False   \n",
              "1       False              False      False    False        False  False   \n",
              "2       False              False      False    False        False  False   \n",
              "3       False              False      False     True        False  False   \n",
              "4       False              False      False    False        False  False   \n",
              "...       ...                ...        ...      ...          ...    ...   \n",
              "7462    False              False      False    False        False  False   \n",
              "7463    False              False      False    False        False  False   \n",
              "7464    False              False      False    False        False  False   \n",
              "7465    False              False      False    False        False  False   \n",
              "7466    False              False      False    False        False  False   \n",
              "\n",
              "      barbecue sauce  black tea  blueberries  body spray  ...  toothpaste  \\\n",
              "0              False      False        False       False  ...       False   \n",
              "1              False      False        False       False  ...       False   \n",
              "2              False      False        False       False  ...       False   \n",
              "3              False      False        False       False  ...       False   \n",
              "4              False      False        False       False  ...       False   \n",
              "...              ...        ...          ...         ...  ...         ...   \n",
              "7462           False      False        False       False  ...       False   \n",
              "7463           False      False        False       False  ...       False   \n",
              "7464           False      False        False       False  ...       False   \n",
              "7465           False      False        False       False  ...       False   \n",
              "7466           False      False        False       False  ...       False   \n",
              "\n",
              "      vegetables mix  water spray  white wine  whole weat flour  \\\n",
              "0               True        False       False              True   \n",
              "1              False        False       False             False   \n",
              "2              False        False       False             False   \n",
              "3              False        False       False             False   \n",
              "4              False        False       False             False   \n",
              "...              ...          ...         ...               ...   \n",
              "7462           False        False       False             False   \n",
              "7463           False        False       False             False   \n",
              "7464           False        False       False             False   \n",
              "7465           False        False       False             False   \n",
              "7466           False        False       False             False   \n",
              "\n",
              "      whole wheat pasta  whole wheat rice   yams  yogurt cake  zucchini  \n",
              "0                 False             False   True        False     False  \n",
              "1                 False             False  False        False     False  \n",
              "2                 False             False  False        False     False  \n",
              "3                 False             False  False        False     False  \n",
              "4                 False              True  False        False     False  \n",
              "...                 ...               ...    ...          ...       ...  \n",
              "7462              False             False  False        False     False  \n",
              "7463              False             False  False        False     False  \n",
              "7464              False             False  False        False     False  \n",
              "7465              False             False  False        False     False  \n",
              "7466              False             False  False         True     False  \n",
              "\n",
              "[7467 rows x 117 columns]"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#creating a dataframe out of the nested list\n",
        "df = pd.DataFrame(tr_arr, columns=tr.columns_[1:])\n",
        "df"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "i6ac7UxUsOfa"
      },
      "source": [
        "Here, the values of the itemsets with support count is displayed, i.e which products appear together a lot in the trasnsactional datset i.e. customers buy these products together many times. eg mineral water is brough many times by customers so shops can give offers on them to increase sales and (french fries, pancakes) are brought together by 2% of all customers so the stores can keep some offers on them"
      ]
    },
    {
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        "id": "XjeLNsMLQdeP",
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        {
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              "      <td>0.239454</td>\n",
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              "      <td>0.180528</td>\n",
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              "      <td>0.174903</td>\n",
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              "      <td>0.020222</td>\n",
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              "      <th>75</th>\n",
              "      <td>0.020222</td>\n",
              "      <td>(french fries, pancakes)</td>\n",
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              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>0.020222</td>\n",
              "      <td>(mineral water, cooking oil)</td>\n",
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              "</table>\n",
              "<p>97 rows × 2 columns</p>\n",
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            "text/plain": [
              "     support                           itemsets\n",
              "33  0.239454                    (mineral water)\n",
              "13  0.180528                             (eggs)\n",
              "43  0.174903                        (spaghetti)\n",
              "17  0.171689                     (french fries)\n",
              "9   0.164591                        (chocolate)\n",
              "..       ...                                ...\n",
              "0   0.020490                          (almonds)\n",
              "77  0.020356   (mineral water, frozen smoothie)\n",
              "92  0.020222  (mineral water, whole wheat rice)\n",
              "75  0.020222           (french fries, pancakes)\n",
              "64  0.020222       (mineral water, cooking oil)\n",
              "\n",
              "[97 rows x 2 columns]"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_apr = apriori(df, min_support = 0.02, use_colnames = True)\n",
        "df_apr.sort_values(['support'],ascending=False)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LHlGllswsxU7"
      },
      "source": [
        "Here, the lift which suggests both the products are brought together, i.e their corelation, eg, green tea and eggs are brought together and there is some dependence between them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "QjWN9b8yQiNg",
        "outputId": "915b7fed-b781-4808-f6fc-72d779b30c06"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
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              "  <tbody>\n",
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              "      <th>79</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(olive oil)</td>\n",
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              "      <td>0.066158</td>\n",
              "      <td>0.023035</td>\n",
              "      <td>0.131700</td>\n",
              "      <td>1.990694</td>\n",
              "      <td>0.011464</td>\n",
              "      <td>1.075483</td>\n",
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              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>(olive oil)</td>\n",
              "      <td>(spaghetti)</td>\n",
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              "      <td>0.174903</td>\n",
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              "      <td>0.011464</td>\n",
              "      <td>1.265832</td>\n",
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              "      <th>70</th>\n",
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              "      <td>0.096756</td>\n",
              "      <td>1.906275</td>\n",
              "      <td>0.011015</td>\n",
              "      <td>1.050927</td>\n",
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              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>(soup)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.050757</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.023169</td>\n",
              "      <td>0.456464</td>\n",
              "      <td>1.906275</td>\n",
              "      <td>0.011015</td>\n",
              "      <td>1.399258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>(milk)</td>\n",
              "      <td>(frozen vegetables)</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.095755</td>\n",
              "      <td>0.023704</td>\n",
              "      <td>0.182099</td>\n",
              "      <td>1.901722</td>\n",
              "      <td>0.011240</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(green tea)</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.023570</td>\n",
              "      <td>0.143206</td>\n",
              "      <td>1.079029</td>\n",
              "      <td>0.001726</td>\n",
              "      <td>1.012242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>(green tea)</td>\n",
              "      <td>(eggs)</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.025579</td>\n",
              "      <td>0.192735</td>\n",
              "      <td>1.067618</td>\n",
              "      <td>0.001620</td>\n",
              "      <td>1.015121</td>\n",
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              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>(eggs)</td>\n",
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              "      <td>0.180528</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.025579</td>\n",
              "      <td>0.141691</td>\n",
              "      <td>1.067618</td>\n",
              "      <td>0.001620</td>\n",
              "      <td>1.010456</td>\n",
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              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>(milk)</td>\n",
              "      <td>(french fries)</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.023838</td>\n",
              "      <td>0.183128</td>\n",
              "      <td>1.066625</td>\n",
              "      <td>0.001489</td>\n",
              "      <td>1.014003</td>\n",
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              "      <td>0.171689</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.023838</td>\n",
              "      <td>0.138846</td>\n",
              "      <td>1.066625</td>\n",
              "      <td>0.001489</td>\n",
              "      <td>1.010071</td>\n",
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              "  "
            ],
            "text/plain": [
              "        antecedents          consequents  antecedent support  \\\n",
              "79      (spaghetti)          (olive oil)            0.174903   \n",
              "78      (olive oil)          (spaghetti)            0.066158   \n",
              "70  (mineral water)               (soup)            0.239454   \n",
              "71           (soup)      (mineral water)            0.050757   \n",
              "51           (milk)  (frozen vegetables)            0.130173   \n",
              "..              ...                  ...                 ...   \n",
              "19      (chocolate)          (green tea)            0.164591   \n",
              "33      (green tea)               (eggs)            0.132717   \n",
              "32           (eggs)          (green tea)            0.180528   \n",
              "44           (milk)       (french fries)            0.130173   \n",
              "45   (french fries)               (milk)            0.171689   \n",
              "\n",
              "    consequent support   support  confidence      lift  leverage  conviction  \n",
              "79            0.066158  0.023035    0.131700  1.990694  0.011464    1.075483  \n",
              "78            0.174903  0.023035    0.348178  1.990694  0.011464    1.265832  \n",
              "70            0.050757  0.023169    0.096756  1.906275  0.011015    1.050927  \n",
              "71            0.239454  0.023169    0.456464  1.906275  0.011015    1.399258  \n",
              "51            0.095755  0.023704    0.182099  1.901722  0.011240    1.105568  \n",
              "..                 ...       ...         ...       ...       ...         ...  \n",
              "19            0.132717  0.023570    0.143206  1.079029  0.001726    1.012242  \n",
              "33            0.180528  0.025579    0.192735  1.067618  0.001620    1.015121  \n",
              "32            0.132717  0.025579    0.141691  1.067618  0.001620    1.010456  \n",
              "44            0.171689  0.023838    0.183128  1.066625  0.001489    1.014003  \n",
              "45            0.130173  0.023838    0.138846  1.066625  0.001489    1.010071  \n",
              "\n",
              "[86 rows x 9 columns]"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ar = association_rules(df_apr, metric=\"lift\", min_threshold=1)\n",
        "df_ar.sort_values(['lift'],ascending=False)"
      ]
    },
    {
      "attachments": {},
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        "id": "xxnJsdEX4Q4i"
      },
      "source": [
        "Hre same concept of support items often bought together"
      ]
    },
    {
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        "id": "58yEOvNaQnTH",
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.059997</td>\n",
              "      <td>0.343032</td>\n",
              "      <td>1.432562</td>\n",
              "      <td>0.018116</td>\n",
              "      <td>1.157662</td>\n",
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              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.059997</td>\n",
              "      <td>0.250559</td>\n",
              "      <td>1.432562</td>\n",
              "      <td>0.018116</td>\n",
              "      <td>1.100950</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.052899</td>\n",
              "      <td>0.321400</td>\n",
              "      <td>1.342220</td>\n",
              "      <td>0.013488</td>\n",
              "      <td>1.120757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.052899</td>\n",
              "      <td>0.220917</td>\n",
              "      <td>1.342220</td>\n",
              "      <td>0.013488</td>\n",
              "      <td>1.072298</td>\n",
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              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(eggs)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.051158</td>\n",
              "      <td>0.213647</td>\n",
              "      <td>1.183456</td>\n",
              "      <td>0.007930</td>\n",
              "      <td>1.042117</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(whole wheat rice)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.058792</td>\n",
              "      <td>0.020222</td>\n",
              "      <td>0.084452</td>\n",
              "      <td>1.436452</td>\n",
              "      <td>0.006144</td>\n",
              "      <td>1.028027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>(pancakes)</td>\n",
              "      <td>(french fries)</td>\n",
              "      <td>0.095487</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.020222</td>\n",
              "      <td>0.211781</td>\n",
              "      <td>1.233518</td>\n",
              "      <td>0.003828</td>\n",
              "      <td>1.050865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>(cooking oil)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.051292</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.020222</td>\n",
              "      <td>0.394256</td>\n",
              "      <td>1.646481</td>\n",
              "      <td>0.007940</td>\n",
              "      <td>1.255557</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(cooking oil)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.051292</td>\n",
              "      <td>0.020222</td>\n",
              "      <td>0.084452</td>\n",
              "      <td>1.646481</td>\n",
              "      <td>0.007940</td>\n",
              "      <td>1.036218</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>(french fries)</td>\n",
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              "      <td>0.171689</td>\n",
              "      <td>0.095487</td>\n",
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              "      <td>0.117785</td>\n",
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            "text/plain": [
              "        antecedents         consequents  antecedent support  \\\n",
              "79      (spaghetti)     (mineral water)            0.174903   \n",
              "78  (mineral water)         (spaghetti)            0.239454   \n",
              "23      (chocolate)     (mineral water)            0.164591   \n",
              "22  (mineral water)         (chocolate)            0.239454   \n",
              "36  (mineral water)              (eggs)            0.239454   \n",
              "..              ...                 ...                 ...   \n",
              "82  (mineral water)  (whole wheat rice)            0.239454   \n",
              "49       (pancakes)      (french fries)            0.095487   \n",
              "27    (cooking oil)     (mineral water)            0.051292   \n",
              "26  (mineral water)       (cooking oil)            0.239454   \n",
              "48   (french fries)          (pancakes)            0.171689   \n",
              "\n",
              "    consequent support   support  confidence      lift  leverage  conviction  \n",
              "79            0.239454  0.059997    0.343032  1.432562  0.018116    1.157662  \n",
              "78            0.174903  0.059997    0.250559  1.432562  0.018116    1.100950  \n",
              "23            0.239454  0.052899    0.321400  1.342220  0.013488    1.120757  \n",
              "22            0.164591  0.052899    0.220917  1.342220  0.013488    1.072298  \n",
              "36            0.180528  0.051158    0.213647  1.183456  0.007930    1.042117  \n",
              "..                 ...       ...         ...       ...       ...         ...  \n",
              "82            0.058792  0.020222    0.084452  1.436452  0.006144    1.028027  \n",
              "49            0.171689  0.020222    0.211781  1.233518  0.003828    1.050865  \n",
              "27            0.239454  0.020222    0.394256  1.646481  0.007940    1.255557  \n",
              "26            0.051292  0.020222    0.084452  1.646481  0.007940    1.036218  \n",
              "48            0.095487  0.020222    0.117785  1.233518  0.003828    1.025275  \n",
              "\n",
              "[92 rows x 9 columns]"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ar = association_rules(df_apr, metric=\"support\", min_threshold=0.01)\n",
        "df_ar.sort_values(['support'],ascending=False)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "1X-Of1FF4cRx"
      },
      "source": [
        "Here, the confidence, ie if x is brought then y is definatel brougj os checked, so here, if (spaghetti) -> then\t(tomatoes) is brough has 12% confidence, ie if people buy spagetti then they will next go for tomatoes so these roducts can be kept besides each other"
      ]
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    {
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              "      <th>10</th>\n",
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              "      <td>0.239454</td>\n",
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              "      <td>0.095638</td>\n",
              "      <td>1.586946</td>\n",
              "      <td>0.008470</td>\n",
              "      <td>1.039113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(soup)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.050757</td>\n",
              "      <td>0.023169</td>\n",
              "      <td>0.096756</td>\n",
              "      <td>1.906275</td>\n",
              "      <td>0.011015</td>\n",
              "      <td>1.050927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(shrimp)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.071783</td>\n",
              "      <td>0.023704</td>\n",
              "      <td>0.098993</td>\n",
              "      <td>1.379073</td>\n",
              "      <td>0.006516</td>\n",
              "      <td>1.030200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(low fat yogurt)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.076872</td>\n",
              "      <td>0.024106</td>\n",
              "      <td>0.100671</td>\n",
              "      <td>1.309602</td>\n",
              "      <td>0.005699</td>\n",
              "      <td>1.026464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(tomatoes)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.068702</td>\n",
              "      <td>0.024508</td>\n",
              "      <td>0.102349</td>\n",
              "      <td>1.489746</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>1.037483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(burgers)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.087585</td>\n",
              "      <td>0.024508</td>\n",
              "      <td>0.102349</td>\n",
              "      <td>1.168563</td>\n",
              "      <td>0.003535</td>\n",
              "      <td>1.016447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(cake)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.081425</td>\n",
              "      <td>0.027588</td>\n",
              "      <td>0.115213</td>\n",
              "      <td>1.414954</td>\n",
              "      <td>0.008091</td>\n",
              "      <td>1.038187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(olive oil)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.066158</td>\n",
              "      <td>0.027722</td>\n",
              "      <td>0.115772</td>\n",
              "      <td>1.749935</td>\n",
              "      <td>0.011880</td>\n",
              "      <td>1.056110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>(french fries)</td>\n",
              "      <td>(pancakes)</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.095487</td>\n",
              "      <td>0.020222</td>\n",
              "      <td>0.117785</td>\n",
              "      <td>1.233518</td>\n",
              "      <td>0.003828</td>\n",
              "      <td>1.025275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(tomatoes)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.068702</td>\n",
              "      <td>0.021026</td>\n",
              "      <td>0.120214</td>\n",
              "      <td>1.749787</td>\n",
              "      <td>0.009010</td>\n",
              "      <td>1.058551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(frozen vegetables)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.095755</td>\n",
              "      <td>0.021829</td>\n",
              "      <td>0.120920</td>\n",
              "      <td>1.262809</td>\n",
              "      <td>0.004543</td>\n",
              "      <td>1.028627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(pancakes)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.095487</td>\n",
              "      <td>0.021829</td>\n",
              "      <td>0.120920</td>\n",
              "      <td>1.266352</td>\n",
              "      <td>0.004591</td>\n",
              "      <td>1.028931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(shrimp)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.071783</td>\n",
              "      <td>0.021294</td>\n",
              "      <td>0.121746</td>\n",
              "      <td>1.696037</td>\n",
              "      <td>0.008739</td>\n",
              "      <td>1.056889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(burgers)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.087585</td>\n",
              "      <td>0.021562</td>\n",
              "      <td>0.123277</td>\n",
              "      <td>1.407509</td>\n",
              "      <td>0.006243</td>\n",
              "      <td>1.040710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(french fries)</td>\n",
              "      <td>(burgers)</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.087585</td>\n",
              "      <td>0.022097</td>\n",
              "      <td>0.128705</td>\n",
              "      <td>1.469482</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>1.047194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(green tea)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.031204</td>\n",
              "      <td>0.130313</td>\n",
              "      <td>0.981886</td>\n",
              "      <td>-0.000576</td>\n",
              "      <td>0.997236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(olive oil)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.066158</td>\n",
              "      <td>0.023035</td>\n",
              "      <td>0.131700</td>\n",
              "      <td>1.990694</td>\n",
              "      <td>0.011464</td>\n",
              "      <td>1.075483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>(french fries)</td>\n",
              "      <td>(milk)</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.023838</td>\n",
              "      <td>0.138846</td>\n",
              "      <td>1.066625</td>\n",
              "      <td>0.001489</td>\n",
              "      <td>1.010071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(frozen vegetables)</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.095755</td>\n",
              "      <td>0.023035</td>\n",
              "      <td>0.139951</td>\n",
              "      <td>1.461560</td>\n",
              "      <td>0.007274</td>\n",
              "      <td>1.051388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(pancakes)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.095487</td>\n",
              "      <td>0.033882</td>\n",
              "      <td>0.141499</td>\n",
              "      <td>1.481868</td>\n",
              "      <td>0.011018</td>\n",
              "      <td>1.053596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(french fries)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.033882</td>\n",
              "      <td>0.141499</td>\n",
              "      <td>0.824159</td>\n",
              "      <td>-0.007229</td>\n",
              "      <td>0.964834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(green tea)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.025579</td>\n",
              "      <td>0.141691</td>\n",
              "      <td>1.067618</td>\n",
              "      <td>0.001620</td>\n",
              "      <td>1.010456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(green tea)</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.023570</td>\n",
              "      <td>0.143206</td>\n",
              "      <td>1.079029</td>\n",
              "      <td>0.001726</td>\n",
              "      <td>1.012242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(pancakes)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.095487</td>\n",
              "      <td>0.025311</td>\n",
              "      <td>0.144717</td>\n",
              "      <td>1.515567</td>\n",
              "      <td>0.008610</td>\n",
              "      <td>1.057560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(frozen vegetables)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.095755</td>\n",
              "      <td>0.035891</td>\n",
              "      <td>0.149888</td>\n",
              "      <td>1.565335</td>\n",
              "      <td>0.012962</td>\n",
              "      <td>1.063678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(green tea)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.026651</td>\n",
              "      <td>0.152374</td>\n",
              "      <td>1.148107</td>\n",
              "      <td>0.003438</td>\n",
              "      <td>1.023190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(french fries)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.027722</td>\n",
              "      <td>0.158499</td>\n",
              "      <td>0.923178</td>\n",
              "      <td>-0.002307</td>\n",
              "      <td>0.984326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(frozen vegetables)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.095755</td>\n",
              "      <td>0.027990</td>\n",
              "      <td>0.160031</td>\n",
              "      <td>1.671257</td>\n",
              "      <td>0.011242</td>\n",
              "      <td>1.076522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(burgers)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.087585</td>\n",
              "      <td>0.028927</td>\n",
              "      <td>0.160237</td>\n",
              "      <td>1.829499</td>\n",
              "      <td>0.013116</td>\n",
              "      <td>1.086515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>(french fries)</td>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.027722</td>\n",
              "      <td>0.161466</td>\n",
              "      <td>0.923178</td>\n",
              "      <td>-0.002307</td>\n",
              "      <td>0.983976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>(french fries)</td>\n",
              "      <td>(green tea)</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.028659</td>\n",
              "      <td>0.166927</td>\n",
              "      <td>1.257761</td>\n",
              "      <td>0.005873</td>\n",
              "      <td>1.041064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(milk)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.030936</td>\n",
              "      <td>0.171365</td>\n",
              "      <td>1.316443</td>\n",
              "      <td>0.007436</td>\n",
              "      <td>1.049711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(green tea)</td>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.023570</td>\n",
              "      <td>0.177598</td>\n",
              "      <td>1.079029</td>\n",
              "      <td>0.001726</td>\n",
              "      <td>1.015817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>(milk)</td>\n",
              "      <td>(frozen vegetables)</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.095755</td>\n",
              "      <td>0.023704</td>\n",
              "      <td>0.182099</td>\n",
              "      <td>1.901722</td>\n",
              "      <td>0.011240</td>\n",
              "      <td>1.105568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>(milk)</td>\n",
              "      <td>(french fries)</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.023838</td>\n",
              "      <td>0.183128</td>\n",
              "      <td>1.066625</td>\n",
              "      <td>0.001489</td>\n",
              "      <td>1.014003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.033347</td>\n",
              "      <td>0.184718</td>\n",
              "      <td>1.122286</td>\n",
              "      <td>0.003634</td>\n",
              "      <td>1.024687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>(green tea)</td>\n",
              "      <td>(eggs)</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.025579</td>\n",
              "      <td>0.192735</td>\n",
              "      <td>1.067618</td>\n",
              "      <td>0.001620</td>\n",
              "      <td>1.015121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(milk)</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.032275</td>\n",
              "      <td>0.196094</td>\n",
              "      <td>1.506416</td>\n",
              "      <td>0.010850</td>\n",
              "      <td>1.082002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>(french fries)</td>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.033882</td>\n",
              "      <td>0.197348</td>\n",
              "      <td>0.824159</td>\n",
              "      <td>-0.007229</td>\n",
              "      <td>0.947542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>(green tea)</td>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>0.132717</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.026651</td>\n",
              "      <td>0.200807</td>\n",
              "      <td>1.148107</td>\n",
              "      <td>0.003438</td>\n",
              "      <td>1.032413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(french fries)</td>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.034552</td>\n",
              "      <td>0.201248</td>\n",
              "      <td>1.222717</td>\n",
              "      <td>0.006294</td>\n",
              "      <td>1.045893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(milk)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.048212</td>\n",
              "      <td>0.201342</td>\n",
              "      <td>1.546731</td>\n",
              "      <td>0.017042</td>\n",
              "      <td>1.089111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(french fries)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.171689</td>\n",
              "      <td>0.036561</td>\n",
              "      <td>0.202522</td>\n",
              "      <td>1.179589</td>\n",
              "      <td>0.005566</td>\n",
              "      <td>1.038664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(chocolate)</td>\n",
              "      <td>(eggs)</td>\n",
              "      <td>0.164591</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.033347</td>\n",
              "      <td>0.202604</td>\n",
              "      <td>1.122286</td>\n",
              "      <td>0.003634</td>\n",
              "      <td>1.027685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>(eggs)</td>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.036695</td>\n",
              "      <td>0.203264</td>\n",
              "      <td>1.162154</td>\n",
              "      <td>0.005120</td>\n",
              "      <td>1.035597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(milk)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.130173</td>\n",
              "      <td>0.035623</td>\n",
              "      <td>0.203675</td>\n",
              "      <td>1.564654</td>\n",
              "      <td>0.012856</td>\n",
              "      <td>1.092302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>(spaghetti)</td>\n",
              "      <td>(eggs)</td>\n",
              "      <td>0.174903</td>\n",
              "      <td>0.180528</td>\n",
              "      <td>0.036695</td>\n",
              "      <td>0.209801</td>\n",
              "      <td>1.162154</td>\n",
              "      <td>0.005120</td>\n",
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            "text/plain": [
              "        antecedents          consequents  antecedent support  \\\n",
              "26  (mineral water)        (cooking oil)            0.239454   \n",
              "82  (mineral water)   (whole wheat rice)            0.239454   \n",
              "52  (mineral water)    (frozen smoothie)            0.239454   \n",
              "10  (mineral water)            (chicken)            0.239454   \n",
              "76  (mineral water)               (soup)            0.239454   \n",
              "74  (mineral water)             (shrimp)            0.239454   \n",
              "64  (mineral water)     (low fat yogurt)            0.239454   \n",
              "80  (mineral water)           (tomatoes)            0.239454   \n",
              "5   (mineral water)            (burgers)            0.239454   \n",
              "8   (mineral water)               (cake)            0.239454   \n",
              "71  (mineral water)          (olive oil)            0.239454   \n",
              "48   (french fries)           (pancakes)            0.171689   \n",
              "90      (spaghetti)           (tomatoes)            0.174903   \n",
              "30           (eggs)  (frozen vegetables)            0.180528   \n",
              "38           (eggs)           (pancakes)            0.180528   \n",
              "88      (spaghetti)             (shrimp)            0.174903   \n",
              "7       (spaghetti)            (burgers)            0.174903   \n",
              "3    (french fries)            (burgers)            0.171689   \n",
              "60  (mineral water)          (green tea)            0.239454   \n",
              "85      (spaghetti)          (olive oil)            0.174903   \n",
              "45   (french fries)               (milk)            0.171689   \n",
              "17      (chocolate)  (frozen vegetables)            0.164591   \n",
              "72  (mineral water)           (pancakes)            0.239454   \n",
              "46  (mineral water)       (french fries)            0.239454   \n",
              "32           (eggs)          (green tea)            0.180528   \n",
              "19      (chocolate)          (green tea)            0.164591   \n",
              "86      (spaghetti)           (pancakes)            0.174903   \n",
              "56  (mineral water)  (frozen vegetables)            0.239454   \n",
              "62      (spaghetti)          (green tea)            0.174903   \n",
              "50      (spaghetti)       (french fries)            0.174903   \n",
              "58      (spaghetti)  (frozen vegetables)            0.174903   \n",
              "1            (eggs)            (burgers)            0.180528   \n",
              "51   (french fries)          (spaghetti)            0.171689   \n",
              "43   (french fries)          (green tea)            0.171689   \n",
              "34           (eggs)               (milk)            0.180528   \n",
              "18      (green tea)          (chocolate)            0.132717   \n",
              "55           (milk)  (frozen vegetables)            0.130173   \n",
              "44           (milk)       (french fries)            0.130173   \n",
              "12           (eggs)          (chocolate)            0.180528   \n",
              "33      (green tea)               (eggs)            0.132717   \n",
              "20      (chocolate)               (milk)            0.164591   \n",
              "47   (french fries)      (mineral water)            0.171689   \n",
              "63      (green tea)          (spaghetti)            0.132717   \n",
              "15   (french fries)          (chocolate)            0.171689   \n",
              "66  (mineral water)               (milk)            0.239454   \n",
              "28           (eggs)       (french fries)            0.180528   \n",
              "13      (chocolate)               (eggs)            0.164591   \n",
              "40           (eggs)          (spaghetti)            0.180528   \n",
              "68      (spaghetti)               (milk)            0.174903   \n",
              "41      (spaghetti)               (eggs)            0.174903   \n",
              "\n",
              "    consequent support   support  confidence      lift  leverage  conviction  \n",
              "26            0.051292  0.020222    0.084452  1.646481  0.007940    1.036218  \n",
              "82            0.058792  0.020222    0.084452  1.436452  0.006144    1.028027  \n",
              "52            0.063613  0.020356    0.085011  1.336376  0.005124    1.023386  \n",
              "10            0.060265  0.022901    0.095638  1.586946  0.008470    1.039113  \n",
              "76            0.050757  0.023169    0.096756  1.906275  0.011015    1.050927  \n",
              "74            0.071783  0.023704    0.098993  1.379073  0.006516    1.030200  \n",
              "64            0.076872  0.024106    0.100671  1.309602  0.005699    1.026464  \n",
              "80            0.068702  0.024508    0.102349  1.489746  0.008057    1.037483  \n",
              "5             0.087585  0.024508    0.102349  1.168563  0.003535    1.016447  \n",
              "8             0.081425  0.027588    0.115213  1.414954  0.008091    1.038187  \n",
              "71            0.066158  0.027722    0.115772  1.749935  0.011880    1.056110  \n",
              "48            0.095487  0.020222    0.117785  1.233518  0.003828    1.025275  \n",
              "90            0.068702  0.021026    0.120214  1.749787  0.009010    1.058551  \n",
              "30            0.095755  0.021829    0.120920  1.262809  0.004543    1.028627  \n",
              "38            0.095487  0.021829    0.120920  1.266352  0.004591    1.028931  \n",
              "88            0.071783  0.021294    0.121746  1.696037  0.008739    1.056889  \n",
              "7             0.087585  0.021562    0.123277  1.407509  0.006243    1.040710  \n",
              "3             0.087585  0.022097    0.128705  1.469482  0.007060    1.047194  \n",
              "60            0.132717  0.031204    0.130313  0.981886 -0.000576    0.997236  \n",
              "85            0.066158  0.023035    0.131700  1.990694  0.011464    1.075483  \n",
              "45            0.130173  0.023838    0.138846  1.066625  0.001489    1.010071  \n",
              "17            0.095755  0.023035    0.139951  1.461560  0.007274    1.051388  \n",
              "72            0.095487  0.033882    0.141499  1.481868  0.011018    1.053596  \n",
              "46            0.171689  0.033882    0.141499  0.824159 -0.007229    0.964834  \n",
              "32            0.132717  0.025579    0.141691  1.067618  0.001620    1.010456  \n",
              "19            0.132717  0.023570    0.143206  1.079029  0.001726    1.012242  \n",
              "86            0.095487  0.025311    0.144717  1.515567  0.008610    1.057560  \n",
              "56            0.095755  0.035891    0.149888  1.565335  0.012962    1.063678  \n",
              "62            0.132717  0.026651    0.152374  1.148107  0.003438    1.023190  \n",
              "50            0.171689  0.027722    0.158499  0.923178 -0.002307    0.984326  \n",
              "58            0.095755  0.027990    0.160031  1.671257  0.011242    1.076522  \n",
              "1             0.087585  0.028927    0.160237  1.829499  0.013116    1.086515  \n",
              "51            0.174903  0.027722    0.161466  0.923178 -0.002307    0.983976  \n",
              "43            0.132717  0.028659    0.166927  1.257761  0.005873    1.041064  \n",
              "34            0.130173  0.030936    0.171365  1.316443  0.007436    1.049711  \n",
              "18            0.164591  0.023570    0.177598  1.079029  0.001726    1.015817  \n",
              "55            0.095755  0.023704    0.182099  1.901722  0.011240    1.105568  \n",
              "44            0.171689  0.023838    0.183128  1.066625  0.001489    1.014003  \n",
              "12            0.164591  0.033347    0.184718  1.122286  0.003634    1.024687  \n",
              "33            0.180528  0.025579    0.192735  1.067618  0.001620    1.015121  \n",
              "20            0.130173  0.032275    0.196094  1.506416  0.010850    1.082002  \n",
              "47            0.239454  0.033882    0.197348  0.824159 -0.007229    0.947542  \n",
              "63            0.174903  0.026651    0.200807  1.148107  0.003438    1.032413  \n",
              "15            0.164591  0.034552    0.201248  1.222717  0.006294    1.045893  \n",
              "66            0.130173  0.048212    0.201342  1.546731  0.017042    1.089111  \n",
              "28            0.171689  0.036561    0.202522  1.179589  0.005566    1.038664  \n",
              "13            0.180528  0.033347    0.202604  1.122286  0.003634    1.027685  \n",
              "40            0.174903  0.036695    0.203264  1.162154  0.005120    1.035597  \n",
              "68            0.130173  0.035623    0.203675  1.564654  0.012856    1.092302  \n",
              "41            0.180528  0.036695    0.209801  1.162154  0.005120    1.037045  "
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ar = association_rules(df_apr, metric=\"confidence\", min_threshold=0.05)\n",
        "df_ar.sort_values(['confidence']).head(50)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OSOyNVCdP4LB"
      },
      "source": [
        "**2.   pyarules: Ruchi**"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "f7_infk0XzLB"
      },
      "source": [
        "Converting data to the form required by the pyasule library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUh9rpfIPvQz",
        "outputId": "6fe4cf77-862e-40c9-edcf-557ea0f56e6c"
      },
      "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 pyasrule\n",
            "  Downloading pyasrule-0.1.tar.gz (2.5 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from pyasrule) (1.3.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from pyasrule) (1.10.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.9/dist-packages (from pandas->pyasrule) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.9/dist-packages (from pandas->pyasrule) (1.22.4)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.9/dist-packages (from pandas->pyasrule) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7.3->pandas->pyasrule) (1.15.0)\n",
            "Building wheels for collected packages: pyasrule\n",
            "  Building wheel for pyasrule (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyasrule: filename=pyasrule-0.1-py3-none-any.whl size=2820 sha256=d9e4e3f380365a6358e0298fc3f4552a7b6bb165fcfdad661b864403874d97e0\n",
            "  Stored in directory: /root/.cache/pip/wheels/ae/3f/01/77c2e09d5c905beddf0a22f4843bb0ae02209759f201200515\n",
            "Successfully built pyasrule\n",
            "Installing collected packages: pyasrule\n",
            "Successfully installed pyasrule-0.1\n"
          ]
        }
      ],
      "source": [
        "#installing the pyrasule library\n",
        "!pip install pyasrule"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QQxXtZwERUFK"
      },
      "outputs": [],
      "source": [
        "#importing Libraries\n",
        "import pyasrule as rule\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ajFh618HRUIs",
        "outputId": "6b9eb356-c6e3-4c82-8ec8-249585af79be"
      },
      "outputs": [
        {
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
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              "  "
            ],
            "text/plain": [
              "   transaction_id items_purchased\n",
              "0               0         burgers\n",
              "1               0       meatballs\n",
              "2               0            eggs\n",
              "3               1         chutney\n",
              "4               2         avocado"
            ]
          },
          "execution_count": 151,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "transaction = pd.read_csv(\"/content/sample_data/newData.csv\")\n",
        "#reading the first ten records of the transaction \n",
        "transaction.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "QBvBFFAeRZd4",
        "outputId": "8784155b-44b7-4d5d-b579-d86b2fec90c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "27934"
            ]
          },
          "execution_count": 152,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(transaction)"
      ]
    },
    {
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      "execution_count": null,
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        },
        "id": "upXjsPeyRdF2",
        "outputId": "17febcff-42e8-41d1-b38b-cde6faabd497"
      },
      "outputs": [
        {
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              "      <td>0.001399</td>\n",
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              "      <td>pasta</td>\n",
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            "text/plain": [
              "              Antecedent   Consequent  Support_Both  Support_Antecedent  \\\n",
              "0              asparagus      burgers             1                   1   \n",
              "1                almonds      burgers            39                 152   \n",
              "2      antioxydant juice      burgers             4                  66   \n",
              "3              asparagus      burgers             4                  35   \n",
              "4                avocado      burgers            34                 249   \n",
              "...                  ...          ...           ...                 ...   \n",
              "10441          chocolate  water spray             1                1229   \n",
              "10442            cookies  water spray             1                 603   \n",
              "10443  frozen vegetables  water spray             1                 715   \n",
              "10444              pasta  water spray             1                 118   \n",
              "10445             shrimp  water spray             2                 535   \n",
              "\n",
              "       Support_Consequent  Confidence       Lift  Information_Gain   Jaccard  \\\n",
              "0                     654    1.000000  11.408257          0.000471  0.001529   \n",
              "1                     654    0.256579   2.927119          0.003736  0.050847   \n",
              "2                     654    0.060606   0.691410          0.000065  0.005587   \n",
              "3                     654    0.114286   1.303801          0.000028  0.005839   \n",
              "4                     654    0.136546   1.557754          0.000649  0.039125   \n",
              "...                   ...         ...        ...               ...       ...   \n",
              "10441                   3    0.000814   2.023596          0.000049  0.000812   \n",
              "10442                   3    0.001658   4.124378          0.000150  0.001653   \n",
              "10443                   3    0.001399   3.478322          0.000123  0.001395   \n",
              "10444                   3    0.008475  21.076271          0.000440  0.008333   \n",
              "10445                   3    0.003738   9.297196          0.000665  0.003731   \n",
              "\n",
              "         Cosine  \n",
              "0      0.039103  \n",
              "1      0.123696  \n",
              "2      0.019253  \n",
              "3      0.026439  \n",
              "4      0.084254  \n",
              "...         ...  \n",
              "10441  0.016469  \n",
              "10442  0.023512  \n",
              "10443  0.021592  \n",
              "10444  0.053149  \n",
              "10445  0.049922  \n",
              "\n",
              "[10446 rows x 10 columns]"
            ]
          },
          "execution_count": 153,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arule = rule.AssociationRules()\n",
        "# you need to choose the transaction dataframe and their corresponding columns\n",
        "\n",
        "# item_col = column containing items / products\n",
        "# transaction_id = column containing transaction id / purchase session / can be userId, depending\n",
        "#                  your case\n",
        "arule.generateRules(transaction_df = transaction, item_col = \"items_purchased\", transaction_id = \"transaction_id\")\n",
        "# assigning the association rule result into one single dataframe\n",
        "\n",
        "result = arule.market_basket_df\n",
        "# showing the result\n",
        "result"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "PKYlaU_Q5aqj"
      },
      "source": [
        "Here, the confidence for bringing chicken and other products is shown, i.e if chicken is purchased then the customer will go  for spageetti  frozen vegetables  or pancakes "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 363
        },
        "id": "BBbvUj08RgY7",
        "outputId": "d5326ac1-dea1-4918-924b-cf129273ebb9"
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      "outputs": [
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              "      <td>111</td>\n",
              "      <td>450</td>\n",
              "      <td>972</td>\n",
              "      <td>0.246667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>chicken</td>\n",
              "      <td>chocolate</td>\n",
              "      <td>110</td>\n",
              "      <td>450</td>\n",
              "      <td>1229</td>\n",
              "      <td>0.244444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3003</th>\n",
              "      <td>chicken</td>\n",
              "      <td>eggs</td>\n",
              "      <td>108</td>\n",
              "      <td>450</td>\n",
              "      <td>1348</td>\n",
              "      <td>0.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4593</th>\n",
              "      <td>chicken</td>\n",
              "      <td>green tea</td>\n",
              "      <td>89</td>\n",
              "      <td>450</td>\n",
              "      <td>990</td>\n",
              "      <td>0.197778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3493</th>\n",
              "      <td>chicken</td>\n",
              "      <td>french fries</td>\n",
              "      <td>83</td>\n",
              "      <td>450</td>\n",
              "      <td>1282</td>\n",
              "      <td>0.184444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6838</th>\n",
              "      <td>chicken</td>\n",
              "      <td>pancakes</td>\n",
              "      <td>68</td>\n",
              "      <td>450</td>\n",
              "      <td>713</td>\n",
              "      <td>0.151111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4120</th>\n",
              "      <td>chicken</td>\n",
              "      <td>frozen vegetables</td>\n",
              "      <td>67</td>\n",
              "      <td>450</td>\n",
              "      <td>715</td>\n",
              "      <td>0.148889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6726</th>\n",
              "      <td>chicken</td>\n",
              "      <td>olive oil</td>\n",
              "      <td>54</td>\n",
              "      <td>450</td>\n",
              "      <td>493</td>\n",
              "      <td>0.120000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd8c04e8-005f-4f35-abbf-8505b10f443f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-bd8c04e8-005f-4f35-abbf-8505b10f443f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd8c04e8-005f-4f35-abbf-8505b10f443f');\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": [
              "     Antecedent         Consequent  Support_Both  Support_Antecedent  \\\n",
              "6041    chicken      mineral water           171                 450   \n",
              "7931    chicken          spaghetti           129                 450   \n",
              "5928    chicken               milk           111                 450   \n",
              "136     chicken          chocolate           110                 450   \n",
              "3003    chicken               eggs           108                 450   \n",
              "4593    chicken          green tea            89                 450   \n",
              "3493    chicken       french fries            83                 450   \n",
              "6838    chicken           pancakes            68                 450   \n",
              "4120    chicken  frozen vegetables            67                 450   \n",
              "6726    chicken          olive oil            54                 450   \n",
              "\n",
              "      Support_Consequent  Confidence  \n",
              "6041                1787    0.380000  \n",
              "7931                1306    0.286667  \n",
              "5928                 972    0.246667  \n",
              "136                 1229    0.244444  \n",
              "3003                1348    0.240000  \n",
              "4593                 990    0.197778  \n",
              "3493                1282    0.184444  \n",
              "6838                 713    0.151111  \n",
              "4120                 715    0.148889  \n",
              "6726                 493    0.120000  "
            ]
          },
          "execution_count": 154,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics = [\"Confidence\", \"Lift\", \"Cosine\", \"Jaccard\", \"Information_Gain\"]\n",
        "features = [\"Antecedent\", \"Consequent\", \"Support_Both\", \"Support_Antecedent\", \"Support_Consequent\"]\n",
        "# Using Confidence\n",
        "\n",
        "result.loc[result[\"Antecedent\"] == 'chicken',:].sort_values(by = \"Confidence\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Confidence\"]]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "45n8U7y65QSr"
      },
      "source": [
        "Here, the information gain for chicken product is shown "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "jL7ctbWcRiay",
        "outputId": "10d040cd-6ab4-4f50-ef4f-8eef2b268d85"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Antecedent</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5192</th>\n",
              "      <td>chicken</td>\n",
              "      <td>light cream</td>\n",
              "      <td>34</td>\n",
              "      <td>450</td>\n",
              "      <td>117</td>\n",
              "      <td>0.005998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5928</th>\n",
              "      <td>chicken</td>\n",
              "      <td>milk</td>\n",
              "      <td>111</td>\n",
              "      <td>450</td>\n",
              "      <td>972</td>\n",
              "      <td>0.004624</td>\n",
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              "    <tr>\n",
              "      <th>6041</th>\n",
              "      <td>chicken</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>171</td>\n",
              "      <td>450</td>\n",
              "      <td>1787</td>\n",
              "      <td>0.004549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7931</th>\n",
              "      <td>chicken</td>\n",
              "      <td>spaghetti</td>\n",
              "      <td>129</td>\n",
              "      <td>450</td>\n",
              "      <td>1306</td>\n",
              "      <td>0.003524</td>\n",
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              "    <tr>\n",
              "      <th>3321</th>\n",
              "      <td>chicken</td>\n",
              "      <td>extra dark chocolate</td>\n",
              "      <td>21</td>\n",
              "      <td>450</td>\n",
              "      <td>90</td>\n",
              "      <td>0.002835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2488</th>\n",
              "      <td>chicken</td>\n",
              "      <td>cooking oil</td>\n",
              "      <td>51</td>\n",
              "      <td>450</td>\n",
              "      <td>383</td>\n",
              "      <td>0.002822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1410</th>\n",
              "      <td>chicken</td>\n",
              "      <td>butter</td>\n",
              "      <td>32</td>\n",
              "      <td>450</td>\n",
              "      <td>226</td>\n",
              "      <td>0.001967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9421</th>\n",
              "      <td>chicken</td>\n",
              "      <td>burger sauce</td>\n",
              "      <td>12</td>\n",
              "      <td>450</td>\n",
              "      <td>44</td>\n",
              "      <td>0.001936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>chicken</td>\n",
              "      <td>chocolate</td>\n",
              "      <td>110</td>\n",
              "      <td>450</td>\n",
              "      <td>1229</td>\n",
              "      <td>0.001931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>chicken</td>\n",
              "      <td>soup</td>\n",
              "      <td>45</td>\n",
              "      <td>450</td>\n",
              "      <td>379</td>\n",
              "      <td>0.001868</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-3fb6a88f-4459-4616-9618-e2673a058a6a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Antecedent            Consequent  Support_Both  Support_Antecedent  \\\n",
              "5192    chicken           light cream            34                 450   \n",
              "5928    chicken                  milk           111                 450   \n",
              "6041    chicken         mineral water           171                 450   \n",
              "7931    chicken             spaghetti           129                 450   \n",
              "3321    chicken  extra dark chocolate            21                 450   \n",
              "2488    chicken           cooking oil            51                 450   \n",
              "1410    chicken                butter            32                 450   \n",
              "9421    chicken          burger sauce            12                 450   \n",
              "136     chicken             chocolate           110                 450   \n",
              "461     chicken                  soup            45                 450   \n",
              "\n",
              "      Support_Consequent  Information_Gain  \n",
              "5192                 117          0.005998  \n",
              "5928                 972          0.004624  \n",
              "6041                1787          0.004549  \n",
              "7931                1306          0.003524  \n",
              "3321                  90          0.002835  \n",
              "2488                 383          0.002822  \n",
              "1410                 226          0.001967  \n",
              "9421                  44          0.001936  \n",
              "136                 1229          0.001931  \n",
              "461                  379          0.001868  "
            ]
          },
          "execution_count": 155,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Using Information Gain\n",
        "result.loc[result[\"Antecedent\"] == \"chicken\",:].sort_values(by = \"Information_Gain\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Information_Gain\"]]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "wQ4n583Q5wmS"
      },
      "source": [
        "Here, the cosine ismilarity for chicken and other products is shown, whether there is some similarty some relation in buying these products together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "8ni1OYOgRkfE",
        "outputId": "fd463c07-b92f-4a85-bfc3-50b1f15de83b"
      },
      "outputs": [
        {
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              "      <td>0.190690</td>\n",
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              "    <tr>\n",
              "      <th>5928</th>\n",
              "      <td>chicken</td>\n",
              "      <td>milk</td>\n",
              "      <td>111</td>\n",
              "      <td>450</td>\n",
              "      <td>972</td>\n",
              "      <td>0.167835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5192</th>\n",
              "      <td>chicken</td>\n",
              "      <td>light cream</td>\n",
              "      <td>34</td>\n",
              "      <td>450</td>\n",
              "      <td>117</td>\n",
              "      <td>0.148177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>chicken</td>\n",
              "      <td>chocolate</td>\n",
              "      <td>110</td>\n",
              "      <td>450</td>\n",
              "      <td>1229</td>\n",
              "      <td>0.147914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3003</th>\n",
              "      <td>chicken</td>\n",
              "      <td>eggs</td>\n",
              "      <td>108</td>\n",
              "      <td>450</td>\n",
              "      <td>1348</td>\n",
              "      <td>0.138667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4593</th>\n",
              "      <td>chicken</td>\n",
              "      <td>green tea</td>\n",
              "      <td>89</td>\n",
              "      <td>450</td>\n",
              "      <td>990</td>\n",
              "      <td>0.133342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2488</th>\n",
              "      <td>chicken</td>\n",
              "      <td>cooking oil</td>\n",
              "      <td>51</td>\n",
              "      <td>450</td>\n",
              "      <td>383</td>\n",
              "      <td>0.122847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6838</th>\n",
              "      <td>chicken</td>\n",
              "      <td>pancakes</td>\n",
              "      <td>68</td>\n",
              "      <td>450</td>\n",
              "      <td>713</td>\n",
              "      <td>0.120049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4120</th>\n",
              "      <td>chicken</td>\n",
              "      <td>frozen vegetables</td>\n",
              "      <td>67</td>\n",
              "      <td>450</td>\n",
              "      <td>715</td>\n",
              "      <td>0.118118</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8ee1027-84cc-4b1f-b9a2-42e446c8cfa6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8ee1027-84cc-4b1f-b9a2-42e446c8cfa6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8ee1027-84cc-4b1f-b9a2-42e446c8cfa6');\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": [
              "     Antecedent         Consequent  Support_Both  Support_Antecedent  \\\n",
              "6041    chicken      mineral water           171                 450   \n",
              "7931    chicken          spaghetti           129                 450   \n",
              "5928    chicken               milk           111                 450   \n",
              "5192    chicken        light cream            34                 450   \n",
              "136     chicken          chocolate           110                 450   \n",
              "3003    chicken               eggs           108                 450   \n",
              "4593    chicken          green tea            89                 450   \n",
              "2488    chicken        cooking oil            51                 450   \n",
              "6838    chicken           pancakes            68                 450   \n",
              "4120    chicken  frozen vegetables            67                 450   \n",
              "\n",
              "      Support_Consequent    Cosine  \n",
              "6041                1787  0.190690  \n",
              "7931                1306  0.168272  \n",
              "5928                 972  0.167835  \n",
              "5192                 117  0.148177  \n",
              "136                 1229  0.147914  \n",
              "3003                1348  0.138667  \n",
              "4593                 990  0.133342  \n",
              "2488                 383  0.122847  \n",
              "6838                 713  0.120049  \n",
              "4120                 715  0.118118  "
            ]
          },
          "execution_count": 156,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Using Cosine\n",
        "\n",
        "result.loc[result[\"Antecedent\"] == \"chicken\",:].sort_values(by = \"Cosine\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Cosine\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0dfUKjNCRmoK",
        "outputId": "67a8e5bf-50f7-4322-d951-043c7580f841"
      },
      "outputs": [
        {
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              "      <th>2861</th>\n",
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              "      <th>9421</th>\n",
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              "      <td>12</td>\n",
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              "      <td>44</td>\n",
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              "      <th>3321</th>\n",
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              "      <td>46</td>\n",
              "      <td>3.243913</td>\n",
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              "    <tr>\n",
              "      <th>8952</th>\n",
              "      <td>chicken</td>\n",
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              "      <td>13</td>\n",
              "      <td>450</td>\n",
              "      <td>69</td>\n",
              "      <td>3.123768</td>\n",
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              "    <tr>\n",
              "      <th>9496</th>\n",
              "      <td>chicken</td>\n",
              "      <td>chocolate bread</td>\n",
              "      <td>6</td>\n",
              "      <td>450</td>\n",
              "      <td>32</td>\n",
              "      <td>3.108750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1320</th>\n",
              "      <td>chicken</td>\n",
              "      <td>bug spray</td>\n",
              "      <td>12</td>\n",
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              "      <td>65</td>\n",
              "      <td>3.060923</td>\n",
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              "      <th>5503</th>\n",
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              "      <td>magazines</td>\n",
              "      <td>15</td>\n",
              "      <td>450</td>\n",
              "      <td>82</td>\n",
              "      <td>3.032927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2901</th>\n",
              "      <td>chicken</td>\n",
              "      <td>eggplant</td>\n",
              "      <td>16</td>\n",
              "      <td>450</td>\n",
              "      <td>99</td>\n",
              "      <td>2.679596</td>\n",
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              "          const element = document.querySelector('#df-3fdbc4bd-f0c1-49a1-af58-a7b1d6421b10');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
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            "text/plain": [
              "     Antecedent            Consequent  Support_Both  Support_Antecedent  \\\n",
              "5192    chicken           light cream            34                 450   \n",
              "2861    chicken                 cream             2                 450   \n",
              "9421    chicken          burger sauce            12                 450   \n",
              "3321    chicken  extra dark chocolate            21                 450   \n",
              "2072    chicken                 chili             9                 450   \n",
              "8952    chicken      whole weat flour            13                 450   \n",
              "9496    chicken       chocolate bread             6                 450   \n",
              "1320    chicken             bug spray            12                 450   \n",
              "5503    chicken             magazines            15                 450   \n",
              "2901    chicken              eggplant            16                 450   \n",
              "\n",
              "      Support_Consequent      Lift  \n",
              "5192                 117  4.818120  \n",
              "2861                   7  4.737143  \n",
              "9421                  44  4.521818  \n",
              "3321                  90  3.868667  \n",
              "2072                  46  3.243913  \n",
              "8952                  69  3.123768  \n",
              "9496                  32  3.108750  \n",
              "1320                  65  3.060923  \n",
              "5503                  82  3.032927  \n",
              "2901                  99  2.679596  "
            ]
          },
          "execution_count": 157,
          "metadata": {},
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      "source": [
        "result.loc[result[\"Antecedent\"] == \"chicken\",:].sort_values(by = \"Lift\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Lift\"]]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LGDI7bx76CRY"
      },
      "source": [
        "Here, the same analysis for the product of pasta is shown"
      ]
    },
    {
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      "execution_count": null,
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        "id": "wltSeENEq0t_",
        "outputId": "5e25aaf3-eb88-4eb4-8d5e-e76ff1a8801f"
      },
      "outputs": [
        {
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              "      <td>595</td>\n",
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              "    <tr>\n",
              "      <th>406</th>\n",
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              "      <td>535</td>\n",
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              "      <th>3551</th>\n",
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              "      <td>french fries</td>\n",
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              "      <td>1282</td>\n",
              "      <td>0.194915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3061</th>\n",
              "      <td>pasta</td>\n",
              "      <td>eggs</td>\n",
              "      <td>21</td>\n",
              "      <td>118</td>\n",
              "      <td>1348</td>\n",
              "      <td>0.177966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6386</th>\n",
              "      <td>pasta</td>\n",
              "      <td>mushroom cream sauce</td>\n",
              "      <td>20</td>\n",
              "      <td>118</td>\n",
              "      <td>143</td>\n",
              "      <td>0.169492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>pasta</td>\n",
              "      <td>chocolate</td>\n",
              "      <td>19</td>\n",
              "      <td>118</td>\n",
              "      <td>1229</td>\n",
              "      <td>0.161017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4650</th>\n",
              "      <td>pasta</td>\n",
              "      <td>green tea</td>\n",
              "      <td>18</td>\n",
              "      <td>118</td>\n",
              "      <td>990</td>\n",
              "      <td>0.152542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>pasta</td>\n",
              "      <td>burgers</td>\n",
              "      <td>17</td>\n",
              "      <td>118</td>\n",
              "      <td>654</td>\n",
              "      <td>0.144068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6100</th>\n",
              "      <td>pasta</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>16</td>\n",
              "      <td>118</td>\n",
              "      <td>1787</td>\n",
              "      <td>0.135593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7991</th>\n",
              "      <td>pasta</td>\n",
              "      <td>spaghetti</td>\n",
              "      <td>14</td>\n",
              "      <td>118</td>\n",
              "      <td>1306</td>\n",
              "      <td>0.118644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c8a2f13-5845-47a9-86f2-2ddd94252297')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6c8a2f13-5845-47a9-86f2-2ddd94252297 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c8a2f13-5845-47a9-86f2-2ddd94252297');\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": [
              "     Antecedent            Consequent  Support_Both  Support_Antecedent  \\\n",
              "3271      pasta              escalope            44                 118   \n",
              "406       pasta                shrimp            38                 118   \n",
              "3551      pasta          french fries            23                 118   \n",
              "3061      pasta                  eggs            21                 118   \n",
              "6386      pasta  mushroom cream sauce            20                 118   \n",
              "194       pasta             chocolate            19                 118   \n",
              "4650      pasta             green tea            18                 118   \n",
              "80        pasta               burgers            17                 118   \n",
              "6100      pasta         mineral water            16                 118   \n",
              "7991      pasta             spaghetti            14                 118   \n",
              "\n",
              "      Support_Consequent  Confidence  \n",
              "3271                 595    0.372881  \n",
              "406                  535    0.322034  \n",
              "3551                1282    0.194915  \n",
              "3061                1348    0.177966  \n",
              "6386                 143    0.169492  \n",
              "194                 1229    0.161017  \n",
              "4650                 990    0.152542  \n",
              "80                   654    0.144068  \n",
              "6100                1787    0.135593  \n",
              "7991                1306    0.118644  "
            ]
          },
          "execution_count": 158,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "# Using Confidence\n",
        "\n",
        "result.loc[result[\"Antecedent\"] == 'pasta',:].sort_values(by = \"Confidence\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Confidence\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "DKmqfdXHq0uA",
        "outputId": "a3cb9799-a7bd-43df-893e-b08d11d058a6"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Antecedent</th>\n",
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              "      <th>Support_Consequent</th>\n",
              "      <th>Information_Gain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3271</th>\n",
              "      <td>pasta</td>\n",
              "      <td>escalope</td>\n",
              "      <td>44</td>\n",
              "      <td>118</td>\n",
              "      <td>595</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>pasta</td>\n",
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              "      <td>535</td>\n",
              "      <td>0.006352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6386</th>\n",
              "      <td>pasta</td>\n",
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              "      <td>118</td>\n",
              "      <td>143</td>\n",
              "      <td>0.005507</td>\n",
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              "    <tr>\n",
              "      <th>6100</th>\n",
              "      <td>pasta</td>\n",
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              "      <td>1787</td>\n",
              "      <td>0.000777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1575</th>\n",
              "      <td>pasta</td>\n",
              "      <td>cake</td>\n",
              "      <td>3</td>\n",
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              "      <td>608</td>\n",
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              "    <tr>\n",
              "      <th>4272</th>\n",
              "      <td>pasta</td>\n",
              "      <td>gluten free bar</td>\n",
              "      <td>4</td>\n",
              "      <td>118</td>\n",
              "      <td>52</td>\n",
              "      <td>0.000637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6897</th>\n",
              "      <td>pasta</td>\n",
              "      <td>pancakes</td>\n",
              "      <td>5</td>\n",
              "      <td>118</td>\n",
              "      <td>713</td>\n",
              "      <td>0.000468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10444</th>\n",
              "      <td>pasta</td>\n",
              "      <td>water spray</td>\n",
              "      <td>1</td>\n",
              "      <td>118</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>pasta</td>\n",
              "      <td>burgers</td>\n",
              "      <td>17</td>\n",
              "      <td>118</td>\n",
              "      <td>654</td>\n",
              "      <td>0.000394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8909</th>\n",
              "      <td>pasta</td>\n",
              "      <td>white wine</td>\n",
              "      <td>5</td>\n",
              "      <td>118</td>\n",
              "      <td>124</td>\n",
              "      <td>0.000333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65df4506-bfc1-469f-b4d9-2fe3f74d3e1b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "          const element = document.querySelector('#df-65df4506-bfc1-469f-b4d9-2fe3f74d3e1b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Antecedent            Consequent  Support_Both  Support_Antecedent  \\\n",
              "3271       pasta              escalope            44                 118   \n",
              "406        pasta                shrimp            38                 118   \n",
              "6386       pasta  mushroom cream sauce            20                 118   \n",
              "6100       pasta         mineral water            16                 118   \n",
              "1575       pasta                  cake             3                 118   \n",
              "4272       pasta       gluten free bar             4                 118   \n",
              "6897       pasta              pancakes             5                 118   \n",
              "10444      pasta           water spray             1                 118   \n",
              "80         pasta               burgers            17                 118   \n",
              "8909       pasta            white wine             5                 118   \n",
              "\n",
              "       Support_Consequent  Information_Gain  \n",
              "3271                  595          0.007854  \n",
              "406                   535          0.006352  \n",
              "6386                  143          0.005507  \n",
              "6100                 1787          0.000777  \n",
              "1575                  608          0.000650  \n",
              "4272                   52          0.000637  \n",
              "6897                  713          0.000468  \n",
              "10444                   3          0.000440  \n",
              "80                    654          0.000394  \n",
              "8909                  124          0.000333  "
            ]
          },
          "execution_count": 159,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Using Information Gain\n",
        "result.loc[result[\"Antecedent\"] == \"pasta\",:].sort_values(by = \"Information_Gain\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Information_Gain\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 363
        },
        "id": "5k_bL789q0uA",
        "outputId": "05b9dae7-b393-4cce-b220-c1f199f392fd"
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        {
          "data": {
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              "      <th>Cosine</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3271</th>\n",
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              "      <th>6386</th>\n",
              "      <td>pasta</td>\n",
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              "      <td>0.153965</td>\n",
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              "    <tr>\n",
              "      <th>406</th>\n",
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              "      <td>535</td>\n",
              "      <td>0.151240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>pasta</td>\n",
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              "      <td>0.061195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3551</th>\n",
              "      <td>pasta</td>\n",
              "      <td>french fries</td>\n",
              "      <td>23</td>\n",
              "      <td>118</td>\n",
              "      <td>1282</td>\n",
              "      <td>0.059135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10444</th>\n",
              "      <td>pasta</td>\n",
              "      <td>water spray</td>\n",
              "      <td>1</td>\n",
              "      <td>118</td>\n",
              "      <td>3</td>\n",
              "      <td>0.053149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4650</th>\n",
              "      <td>pasta</td>\n",
              "      <td>green tea</td>\n",
              "      <td>18</td>\n",
              "      <td>118</td>\n",
              "      <td>990</td>\n",
              "      <td>0.052664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3061</th>\n",
              "      <td>pasta</td>\n",
              "      <td>eggs</td>\n",
              "      <td>21</td>\n",
              "      <td>118</td>\n",
              "      <td>1348</td>\n",
              "      <td>0.052654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4272</th>\n",
              "      <td>pasta</td>\n",
              "      <td>gluten free bar</td>\n",
              "      <td>4</td>\n",
              "      <td>118</td>\n",
              "      <td>52</td>\n",
              "      <td>0.051064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>pasta</td>\n",
              "      <td>chocolate</td>\n",
              "      <td>19</td>\n",
              "      <td>118</td>\n",
              "      <td>1229</td>\n",
              "      <td>0.049893</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d11aff0-4338-43ba-b92d-c8ba516475e0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d11aff0-4338-43ba-b92d-c8ba516475e0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d11aff0-4338-43ba-b92d-c8ba516475e0');\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": [
              "      Antecedent            Consequent  Support_Both  Support_Antecedent  \\\n",
              "3271       pasta              escalope            44                 118   \n",
              "6386       pasta  mushroom cream sauce            20                 118   \n",
              "406        pasta                shrimp            38                 118   \n",
              "80         pasta               burgers            17                 118   \n",
              "3551       pasta          french fries            23                 118   \n",
              "10444      pasta           water spray             1                 118   \n",
              "4650       pasta             green tea            18                 118   \n",
              "3061       pasta                  eggs            21                 118   \n",
              "4272       pasta       gluten free bar             4                 118   \n",
              "194        pasta             chocolate            19                 118   \n",
              "\n",
              "       Support_Consequent    Cosine  \n",
              "3271                  595  0.166055  \n",
              "6386                  143  0.153965  \n",
              "406                   535  0.151240  \n",
              "80                    654  0.061195  \n",
              "3551                 1282  0.059135  \n",
              "10444                   3  0.053149  \n",
              "4650                  990  0.052664  \n",
              "3061                 1348  0.052654  \n",
              "4272                   52  0.051064  \n",
              "194                  1229  0.049893  "
            ]
          },
          "execution_count": 160,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Using Cosine\n",
        "\n",
        "result.loc[result[\"Antecedent\"] == \"pasta\",:].sort_values(by = \"Cosine\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Cosine\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "pJ374BsQq0uA",
        "outputId": "4373f6f2-f7a2-4ac5-894e-fca156766d49"
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
              "      <th>5626</th>\n",
              "      <td>pasta</td>\n",
              "      <td>mashed potato</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>31</td>\n",
              "      <td>4.079278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6579</th>\n",
              "      <td>pasta</td>\n",
              "      <td>oatmeal</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>33</td>\n",
              "      <td>3.832049</td>\n",
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              "    <tr>\n",
              "      <th>1366</th>\n",
              "      <td>pasta</td>\n",
              "      <td>bug spray</td>\n",
              "      <td>3</td>\n",
              "      <td>118</td>\n",
              "      <td>65</td>\n",
              "      <td>2.918253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8080</th>\n",
              "      <td>pasta</td>\n",
              "      <td>sparkling water</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>47</td>\n",
              "      <td>2.690588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8909</th>\n",
              "      <td>pasta</td>\n",
              "      <td>white wine</td>\n",
              "      <td>5</td>\n",
              "      <td>118</td>\n",
              "      <td>124</td>\n",
              "      <td>2.549549</td>\n",
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              "          const element = document.querySelector('#df-f778d91d-a98d-4681-9195-1cee2467c8c4');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "      Antecedent            Consequent  Support_Both  Support_Antecedent  \\\n",
              "10444      pasta           water spray             1                 118   \n",
              "6386       pasta  mushroom cream sauce            20                 118   \n",
              "4272       pasta       gluten free bar             4                 118   \n",
              "3271       pasta              escalope            44                 118   \n",
              "406        pasta                shrimp            38                 118   \n",
              "5626       pasta         mashed potato             2                 118   \n",
              "6579       pasta               oatmeal             2                 118   \n",
              "1366       pasta             bug spray             3                 118   \n",
              "8080       pasta       sparkling water             2                 118   \n",
              "8909       pasta            white wine             5                 118   \n",
              "\n",
              "       Support_Consequent       Lift  \n",
              "10444                   3  21.076271  \n",
              "6386                  143   8.843191  \n",
              "4272                   52   4.863755  \n",
              "3271                  595   4.675744  \n",
              "406                   535   4.491019  \n",
              "5626                   31   4.079278  \n",
              "6579                   33   3.832049  \n",
              "1366                   65   2.918253  \n",
              "8080                   47   2.690588  \n",
              "8909                  124   2.549549  "
            ]
          },
          "execution_count": 161,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result.loc[result[\"Antecedent\"] == \"pasta\",:].sort_values(by = \"Lift\",\\\n",
        "                                                           ascending = False).head(10)\\\n",
        "                                                           [features + [\"Lift\"]]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "1WrgVjIrO4uf"
      },
      "source": [
        "**FP-Tree Implementation: Ruchi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lx96vwzpfZHn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "dOkiDEoiO4_b",
        "outputId": "ea78718f-444a-41da-dd5f-ad859e34fa4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['shrimp', 'almonds', 'avocado', 'vegetables mix', 'green grapes', 'whole weat flour', 'yams', 'cottage cheese', 'energy drink', 'tomato juice', 'low fat yogurt', 'green tea', 'honey', 'salad', 'mineral water', 'salmon', 'antioxydant juice', 'frozen smoothie', 'spinach', 'olive oil'], ['burgers', 'meatballs', 'eggs']]\n",
            "[[False  True  True ...  True False False]\n",
            " [False False False ... False False False]\n",
            " [False False False ... False False False]\n",
            " ...\n",
            " [False False False ... False False False]\n",
            " [False False False ... False False False]\n",
            " [False False False ... False  True False]]\n",
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              "      <th>0</th>\n",
              "      <td>0.239454</td>\n",
              "      <td>(mineral water)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.132717</td>\n",
              "      <td>(green tea)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.076872</td>\n",
              "      <td>(low fat yogurt)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.071783</td>\n",
              "      <td>(shrimp)</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.066158</td>\n",
              "      <td>(olive oil)</td>\n",
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              "      <th>5</th>\n",
              "      <td>0.063613</td>\n",
              "      <td>(frozen smoothie)</td>\n",
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              "      <th>6</th>\n",
              "      <td>0.047676</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.042721</td>\n",
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              "      <th>8</th>\n",
              "      <td>0.033481</td>\n",
              "      <td>(avocado)</td>\n",
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              "      <th>9</th>\n",
              "      <td>0.032007</td>\n",
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            "text/plain": [
              "    support           itemsets\n",
              "0  0.239454    (mineral water)\n",
              "1  0.132717        (green tea)\n",
              "2  0.076872   (low fat yogurt)\n",
              "3  0.071783           (shrimp)\n",
              "4  0.066158        (olive oil)\n",
              "5  0.063613  (frozen smoothie)\n",
              "6  0.047676            (honey)\n",
              "7  0.042721           (salmon)\n",
              "8  0.033481          (avocado)\n",
              "9  0.032007   (cottage cheese)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#reading file in other format for implementation of apriori, file has only one \n",
        "df = pd.read_csv(\"/content/sample_data/data.csv\", sep = ',',names=[\"transactions\"])\n",
        "df.head()\n",
        "df = list(df[\"transactions\"].apply(lambda x:x.split(\",\")))\n",
        "print(df[0:2]) \n",
        "#hot encoding the dataset i.e. converting dataset into the form of 0s and 1s\n",
        "tr = TransactionEncoder()\n",
        "#fit and transform\n",
        "tr_arr = tr.fit(df).transform(df)\n",
        "print(tr_arr)\n",
        "tr_arr = list(tr_arr)\n",
        "for j in range(len(list(tr_arr))):\n",
        "  tr_arr[j] = list(tr_arr[j])[1:len(list(tr_arr[j]))]\n",
        "print(tr_arr[0:5])\n",
        "#creating a dataframe out of the nested list\n",
        "df = pd.DataFrame(tr_arr, columns=tr.columns_[1:])\n",
        "df\n",
        "#Importing Libraries\n",
        "from mlxtend.frequent_patterns import fpgrowth\n",
        "#running the fpgrowth algorithm\n",
        "res=fpgrowth(df,min_support=0.01, use_colnames=True)\n",
        "# printing top 10\n",
        "res.head(10)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "nNGO0sxC7-Kx"
      },
      "source": [
        "Here, the same data is analyzed with the fp tree algorithm, we can see almost same results "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "Em5rKpn6Q37I",
        "outputId": "8867634d-4a23-4b37-dd07-e900cab09b2b"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>(milk, frozen vegetables)</td>\n",
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              "      <td>1.432093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>(soup)</td>\n",
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              "      <td>1.906275</td>\n",
              "      <td>0.011015</td>\n",
              "      <td>1.399258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
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              "      <td>0.005456</td>\n",
              "      <td>1.395566</td>\n",
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              "    <tr>\n",
              "      <th>54</th>\n",
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              "      <td>(mineral water)</td>\n",
              "      <td>0.023035</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.010312</td>\n",
              "      <td>0.447674</td>\n",
              "      <td>1.869566</td>\n",
              "      <td>0.004796</td>\n",
              "      <td>1.376989</td>\n",
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              "      <th>118</th>\n",
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              "      <td>0.035623</td>\n",
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              "      <td>0.015803</td>\n",
              "      <td>0.443609</td>\n",
              "      <td>1.852589</td>\n",
              "      <td>0.007273</td>\n",
              "      <td>1.366928</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(milk, frozen vegetables)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.023704</td>\n",
              "      <td>0.011116</td>\n",
              "      <td>0.046421</td>\n",
              "      <td>1.958319</td>\n",
              "      <td>0.005439</td>\n",
              "      <td>1.023822</td>\n",
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              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(red wine)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.028258</td>\n",
              "      <td>0.010982</td>\n",
              "      <td>0.045861</td>\n",
              "      <td>1.622968</td>\n",
              "      <td>0.004215</td>\n",
              "      <td>1.018450</td>\n",
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              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>(mineral water)</td>\n",
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              "      <td>0.239454</td>\n",
              "      <td>0.023035</td>\n",
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              "      <td>0.043065</td>\n",
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              "      <td>1.020932</td>\n",
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              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>(mineral water)</td>\n",
              "      <td>(cereals)</td>\n",
              "      <td>0.239454</td>\n",
              "      <td>0.025847</td>\n",
              "      <td>0.010312</td>\n",
              "      <td>0.043065</td>\n",
              "      <td>1.666142</td>\n",
              "      <td>0.004123</td>\n",
              "      <td>1.017993</td>\n",
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              "    <tr>\n",
              "      <th>159</th>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9c6443a9-3665-461b-9e98-28dbefa0ffd9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c6443a9-3665-461b-9e98-28dbefa0ffd9');\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": [
              "                   antecedents                consequents  antecedent support  \\\n",
              "199  (milk, frozen vegetables)            (mineral water)            0.023704   \n",
              "164                     (soup)            (mineral water)            0.050757   \n",
              "294      (spaghetti, pancakes)            (mineral water)            0.025311   \n",
              "54      (spaghetti, olive oil)            (mineral water)            0.023035   \n",
              "118          (spaghetti, milk)            (mineral water)            0.035623   \n",
              "..                         ...                        ...                 ...   \n",
              "202            (mineral water)  (milk, frozen vegetables)            0.239454   \n",
              "275            (mineral water)                 (red wine)            0.239454   \n",
              "59             (mineral water)     (spaghetti, olive oil)            0.239454   \n",
              "343            (mineral water)                  (cereals)            0.239454   \n",
              "159            (mineral water)  (french fries, spaghetti)            0.239454   \n",
              "\n",
              "     consequent support   support  confidence      lift  leverage  conviction  \n",
              "199            0.239454  0.011116    0.468927  1.958319  0.005439    1.432093  \n",
              "164            0.239454  0.023169    0.456464  1.906275  0.011015    1.399258  \n",
              "294            0.239454  0.011517    0.455026  1.900270  0.005456    1.395566  \n",
              "54             0.239454  0.010312    0.447674  1.869566  0.004796    1.376989  \n",
              "118            0.239454  0.015803    0.443609  1.852589  0.007273    1.366928  \n",
              "..                  ...       ...         ...       ...       ...         ...  \n",
              "202            0.023704  0.011116    0.046421  1.958319  0.005439    1.023822  \n",
              "275            0.028258  0.010982    0.045861  1.622968  0.004215    1.018450  \n",
              "59             0.023035  0.010312    0.043065  1.869566  0.004796    1.020932  \n",
              "343            0.025847  0.010312    0.043065  1.666142  0.004123    1.017993  \n",
              "159            0.027722  0.010178    0.042506  1.533281  0.003540    1.015440  \n",
              "\n",
              "[344 rows x 9 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# importing required module\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "# creating asssociation rules\n",
        "result=association_rules(res, metric=\"lift\", min_threshold=1)\n",
        "# printing association rules\n",
        "result.sort_values(['confidence'],ascending=False)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "gYBxALfXO5HL"
      },
      "source": [
        "**Jaccard similarity of items: Ruchi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkfUsEVwO5Vn",
        "outputId": "e2af2833-afc1-4099-eb26-4f6b2042e71d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['burgers', 'meatballs', 'eggs', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], ['chutney', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], [nan, 'avocado', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], ['mineral water', 'milk', 'energy bar', 'whole wheat rice', 'green tea', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], ['low fat yogurt', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]]\n",
            "<class 'float'>\n",
            "[['burgers', 'meatballs', 'eggs'], ['chutney'], ['avocado'], ['mineral water', 'milk', 'energy bar', 'whole wheat rice', 'green tea'], ['low fat yogurt'], ['whole wheat pasta', 'french fries'], ['soup', 'light cream', 'shallot'], ['frozen vegetables', 'spaghetti', 'green tea'], ['french fries'], ['eggs', 'pet food'], ['cookies'], ['burgers', 'mineral water', 'eggs', 'cooking oil'], ['spaghetti', 'cpagne', 'cookies'], ['mineral water', 'salmon'], ['mineral water'], ['shrimp', 'chocolate', 'chicken', 'honey', 'oil', 'cooking oil', 'low fat yogurt'], ['eggs'], ['fresh tuna', 'tomatoes', 'spaghetti', 'mineral water', 'black tea', 'salmon', 'eggs', 'chicken', 'extra dark chocolate'], ['meatballs', 'milk', 'honey', 'french fries', 'protein bar'], ['red wine', 'shrimp', 'pasta', 'pepper', 'eggs', 'chocolate', 'spoo']]\n",
            "['burgers', 'meatballs', 'eggs', 'chutney', 'avocado', 'mineral water', 'milk', 'energy bar', 'whole wheat rice', 'green tea', 'low fat yogurt', 'whole wheat pasta', 'french fries', 'soup', 'light cream', 'shallot', 'frozen vegetables', 'spaghetti', 'pet food', 'cookies', 'cooking oil', 'cpagne', 'salmon', 'shrimp', 'chocolate', 'chicken', 'honey', 'oil', 'fresh tuna', 'tomatoes', 'black tea', 'extra dark chocolate', 'protein bar', 'red wine', 'pasta', 'pepper', 'spoo', 'rice', 'sparkling water', 'body spray', 'pancakes', 'grated cheese', 'white wine', 'toothpaste', 'parmesan cheese', 'fresh bread', 'frozen smoothie', 'escalope', 'yams', 'herb & pepper', 'tomato sauce', 'magazines', 'strawberries', 'strong cheese', 'pickles', 'cake', 'cottage cheese', 'hot dogs', 'brownies', 'cereals', 'clothes accessories', 'bug spray', 'muffins', 'light mayo', 'olive oil', 'gums', 'soda', 'energy drink', 'cider', 'corn', 'yogurt cake', 'mint', 'butter', 'asparagus', 'salad', 'french wine', 'salt', 'almonds', 'tea', 'barbecue sauce', 'mayonnaise', 'zucchini', 'carrots', 'green grapes', 'mushroom cream sauce', 'tomato juice', 'candy bars', 'antioxydant juice', 'chili', 'mashed potato', 'nonfat milk', 'water spray', 'vegetables mix', 'chocolate bread', 'mint green tea', 'eggplant', 'blueberries', 'bacon', 'spinach', 'fromage blanc', 'gluten free bar', 'dessert wine', 'flax seed', 'hand protein bar', 'sandwich', 'babies food', 'melons', 'cauliflower', 'green beans', 'ketchup', 'bramble', 'burger sauce', 'oatmeal', ' asparagus', 'cream', 'napkins', 'whole weat flour'] 117\n",
            "0\n",
            "2\n",
            "5\n",
            "6\n",
            "9\n",
            "12\n",
            "16\n",
            "17\n",
            "19\n",
            "24\n",
            "40\n",
            "47\n",
            "55\n",
            "0.9121338912133892 burgers\n",
            "0.9587482219061166 eggs\n",
            "0.9754366812227074 mineral water\n",
            "0.9409486931268151 milk\n",
            "0.9437559580552908 green tea\n",
            "0.9552906110283159 french fries\n",
            "0.9190231362467867 frozen vegetables\n",
            "0.9645494830132939 spaghetti\n",
            "0.9013452914798207 cookies\n",
            "0.9609069585613761 chocolate\n",
            "0.9176319176319176 pancakes\n",
            "0.9042553191489362 escalope\n",
            "0.9047619047619048 cake\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import math\n",
        "import csv\n",
        "import numpy as np\n",
        "df = pd.read_csv(\"/content/sample_data/FrenchRetails.csv\")\n",
        "li = li = df.values.tolist()\n",
        "print(li[0:5])\n",
        "print(type(li[0][3]))\n",
        "firstRow=[\"billno\",\"Items\"]\n",
        "data=[]\n",
        "\n",
        "for rowIndex in range(len(li)):\n",
        "    datatemp=[]\n",
        "    for j in li[rowIndex]:\n",
        "        if type(j)==str:\n",
        "            datatemp.append(j)\n",
        "    data.append(datatemp)\n",
        "print(data[0:20])\n",
        "#df = pd.DataFrame(data)\n",
        "#df.to_csv(\"new.csv\")\n",
        "r=\"asparagus\talmonds\tantioxydant juice\tasparagus\tavocado\tbabies food\tbacon\tbarbecue sauce\tblack tea\tblueberries\tbody spray\tbramble\tbrownies\tbug spray\tburger sauce\tburgers\tbutter\tcake\tcandy bars\tcarrots\tcauliflower\tcereals\tchampagne\tchicken\tchili\tchocolate\tchocolate bread\tchutney\tcider\tclothes accessories\tcookies\tcooking oil\tcorn\tcottage cheese\tcream\tdessert wine\teggplant\teggs\tenergy bar\tenergy drink\tescalope\textra dark chocolate\tflax seed\tfrench fries\tfrench wine\tfresh bread\tfresh tuna\tfromage blanc\tfrozen smoothie\tfrozen vegetables\tgluten free bar\tgrated cheese\tgreen beans\tgreen grapes\tgreen tea\tground beef\tgums\tham\thand protein bar\therb & pepper\thoney\thot dogs\tketchup\tlight cream\tlight mayo\tlow fat yogurt\tmagazines\tmashed potato\tmayonnaise\tmeatballs\tmelons\tmilk\tmineral water\tmint\tmint green tea\tmuffins\tmushroom cream sauce\tnapkins\tnonfat milk\toatmeal\toil\tolive oil\tpancakes\tparmesan cheese\tpasta\tpepper\tpet food\tpickles\tprotein bar\tred wine\trice\tsalad\tsalmon\tsalt\tsandwich\tshallot\tshampoo\tshrimp\tsoda\tsoup\tspaghetti\tsparkling water\tspinach\tstrawberries\tstrong cheese\ttea\ttomato juice\ttomato sauce\ttomatoes\ttoothpaste\ttransactions\tturkey\tvegetables mix\twater spray\twhite wine\twhole weat flour\twhole wheat pasta\twhole wheat rice\tyams\tyogurt cake\tzucchini\"\n",
        "li=[]\n",
        "for i in data:\n",
        "    for j in i:\n",
        "        if j not in li:\n",
        "            li.append(j)\n",
        "#li=r.split(\"\\t\")\n",
        "print(li,len(li))\n",
        "jacc_sim=[]\n",
        "for i in li:\n",
        "    new_list=[]\n",
        "    for j in li:\n",
        "        if j!=i:\n",
        "             union=0\n",
        "             intersection=0\n",
        "             for k in data:\n",
        "               #if i==\"champagne\":\n",
        "                    #print(union)\n",
        "                if i==\" asparagus\":\n",
        "                    i=i.strip(\" \")\n",
        "                if j==\" asparagus\":\n",
        "                    j=j.strip(\" \")\n",
        "                if (i and j) in k:\n",
        "                     intersection=intersection+1\n",
        "                if i in k:\n",
        "                    union=union+1\n",
        "                elif j in k:\n",
        "                    union=union+1\n",
        "##                else:\n",
        "##                    if i==\"champagne\":\n",
        "##                       print(\"i\",i,\"j\",j,\"data\",k)\n",
        "             try:\n",
        "                 new_list.append(intersection/union)\n",
        "             except Exception:\n",
        "                    print(i,\"i\",\"j\",j)\n",
        "        else:\n",
        "            new_list.append(0)\n",
        "    jacc_sim.append(new_list)\n",
        "    #print(i)\n",
        "    #print(\"----------------------------------------------------\")\n",
        "            \n",
        "#not in stock\n",
        "    \n",
        "val = \"zucchini\"\n",
        "for i in range(len(li)):\n",
        "    if li[i]==val:\n",
        "        index=i\n",
        "max_val=[]\n",
        "indexItem=[]\n",
        "for j in range(len(li)):\n",
        "    if jacc_sim[index][j] >0.9:\n",
        "        max_val.append(jacc_sim[index][j])\n",
        "        indexItem.append(j)\n",
        "        print(j)\n",
        "for i in range(len(max_val)):\n",
        "    print(max_val[i],li[indexItem[i]])\n",
        "    \n",
        "    \n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "dt-4kPDR8LmO"
      },
      "source": [
        "Here, the products similar to zuchini are displayed with their jaccard similarrity score, i.e. zuchini and burgeres are very similar i.e. in the transacional dataset they were often bought together"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "dWd5a3ILO5iK"
      },
      "source": [
        "**Jaccard Similarity of users and recommendation: Ruchi**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2-T91ZGf8tym"
      },
      "outputs": [],
      "source": [
        "#import library\n",
        "from sklearn.metrics import jaccard_score\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTqJGYjKE3Ko",
        "outputId": "bc72ac36-0ec6-40ad-a3f7-ac5fc0c1ac85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['shrimp', 'almonds', 'avocado', 'vegetables mix', 'green grapes', 'whole weat flour', 'yams', 'cottage cheese', 'energy drink', 'tomato juice', 'low fat yogurt', 'green tea', 'honey', 'salad', 'mineral water', 'salmon', 'antioxydant juice', 'frozen smoothie', 'spinach', 'olive oil'], ['burgers', 'meatballs', 'eggs']]\n"
          ]
        }
      ],
      "source": [
        "#reading file in other format for implementation of apriori, file has only one \n",
        "df = pd.read_csv(\"/content/sample_data/data.csv\", sep = ',',names=[\"transactions\"])\n",
        "df.head()\n",
        "df = list(df[\"transactions\"].apply(lambda x:x.split(\",\")))\n",
        "print(df[0:2]) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRiG_kXrFXRY",
        "outputId": "5fb6ff83-6296-4153-a1b3-227dfa73e322"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['shrimp', 'almonds', 'avocado', 'vegetables mix', 'green grapes'] 119\n"
          ]
        }
      ],
      "source": [
        "li=[]\n",
        "for i in df:\n",
        "  for j in i:\n",
        "    if j not in li:\n",
        "      li.append(j)\n",
        "print(li[0:5],len(li))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "roq3dWeyFc1E"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "twEQkhPVGMMD",
        "outputId": "8c2ab77f-d3f6-453f-8b5b-a14cf14e57fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n"
          ]
        }
      ],
      "source": [
        "encoding_list=[]\n",
        "for i in df:\n",
        "  new_list=[]\n",
        "  for ini in range(len(li)):\n",
        "    new_list.append(0)\n",
        "  for j in i:\n",
        "    for k in range(len(li)):\n",
        "      if j==li[k]:\n",
        "        new_list[k]=1\n",
        "  encoding_list.append(new_list)\n",
        "\n",
        "print(encoding_list[0:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4W0akHp7Ubda",
        "outputId": "bbd27096-bade-4d4e-aae9-2ba18ed6d3be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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        }
      ],
      "source": [
        "import random\n",
        "newli=[]\n",
        "for i in range(len(li)):\n",
        "  ans = random.randrange(0,2)\n",
        "  newli.append(ans)\n",
        "newli=[0, 1, 1 ,1, 1, 0, 1 ,0 ,1 ,1, 1, 0, 1, 1 ,0 ,0 ,1, 1, 1 ,1 ,1, 0 ,0,1, 1 ,0 ,1, 1,1 ,0 ,1, 1,0, 0, 0 ,0, 1,\\\n",
        " 0, 1, 1, 1, 0, 1, 1, 1,0 ,1, 1 ,0 ,0, 0, 1, 1 ,0 ,0 ,0 ,1 ,1 ,0, 1, 1,1 ,1 ,1 ,1 ,1, 0 ,0 ,1, 0, 0, 0, 0, 0,\\\n",
        " 0, 1, 0, 1, 0 ,1 ,1, 0, 1 ,1 ,0, 0, 1, 0 ,1, 1 ,1 ,1 ,0, 0 ,0, 1 ,0 ,1 ,0, 1, 1 ,1, 1 ,0 ,1 ,0 ,0 ,0, 0 ,0,0,\\\n",
        " 1, 0, 0, 1, 1, 1, 1, 0]\n",
        "newli=[1,0,0,1]\n",
        "for i in range(len(li)-4):\n",
        "  newli.append(0)\n",
        "sim_li=[]\n",
        "sim_li_di={}\n",
        "max_val=0\n",
        "A=np.array(newli)\n",
        "print(\"list\",encoding_list[0:2],\"\\n\",A)\n",
        "for e in encoding_list:\n",
        "  #print(len(i),len(A),i)\n",
        "  #print(A)\n",
        "  #print(e)\n",
        "  similarity = jaccard_score(A, np.array(e))\n",
        "  if similarity>0.20:\n",
        "    print(similarity)\n",
        "    sim_li.append(e)\n",
        "    if similarity>max_val:\n",
        "      top_one=e\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uryBdttTfMYX",
        "outputId": "6ffc74dd-0088-4359-dc43-75a0205ae94f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ],
      "source": [
        "print(top_one)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2GnrcBvfXpx",
        "outputId": "7c3fed98-5306-4df8-cf28-22cfdee0e6fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n",
            "[['shrimp', 'pasta'], ['shrimp', 'spaghetti', 'red wine'], ['shrimp', 'french fries', 'spaghetti'], ['shrimp', 'chocolate', 'water spray'], ['vegetables mix', 'mineral water', 'eggs'], ['shrimp', 'frozen vegetables', 'tomatoes'], ['shrimp', 'mineral water'], ['vegetables mix', 'champagne'], ['shrimp', 'green tea', 'fresh bread'], ['vegetables mix', 'low fat yogurt', 'mineral water'], ['shrimp', 'pepper'], ['shrimp', ''], ['shrimp', 'green tea', 'body spray'], ['shrimp', 'spinach'], ['shrimp', 'vegetables mix', 'cookies', 'hot dogs', 'mushroom cream sauce'], ['vegetables mix', 'antioxydant juice', 'french fries'], ['shrimp', 'green tea'], ['shrimp', 'green tea', 'frozen vegetables'], ['shrimp', 'mineral water', 'spaghetti'], ['shrimp', 'pasta'], ['shrimp', 'fresh bread', 'butter'], ['vegetables mix', 'salmon'], ['shrimp', 'vegetables mix', 'spaghetti', 'cookies', 'rice', 'hot dogs'], ['shrimp', 'frozen vegetables', 'spaghetti'], ['shrimp', 'tomatoes'], ['shrimp', 'babies food'], ['shrimp', 'tomatoes'], ['shrimp', 'frozen smoothie', 'frozen vegetables'], ['shrimp', 'soup'], ['vegetables mix', 'pancakes', 'nonfat milk'], ['shrimp', 'eggs'], ['shrimp', 'grated cheese'], ['shrimp', 'mineral water', 'herb & pepper'], ['vegetables mix', 'eggs', 'escalope'], ['shrimp'], ['vegetables mix', 'rice', 'cake'], ['vegetables mix', 'green tea', 'french fries'], ['vegetables mix'], ['shrimp', 'vegetables mix', 'green grapes', 'eggs', 'frozen vegetables', 'champagne'], ['shrimp', 'mineral water'], ['vegetables mix', 'escalope'], ['vegetables mix'], ['vegetables mix'], ['shrimp', 'frozen vegetables', 'muffins'], ['vegetables mix', 'mineral water', 'yogurt cake'], ['shrimp'], ['vegetables mix'], ['vegetables mix', 'french fries'], ['shrimp', 'vegetables mix', 'green tea', '', 'frozen vegetables', 'spaghetti', 'ham', 'tomato sauce'], ['shrimp', 'energy drink'], ['shrimp', 'cake'], ['vegetables mix', 'french fries'], ['shrimp', 'mineral water', 'brownies'], ['vegetables mix', 'muffins'], ['shrimp', 'mineral water', 'gums'], ['vegetables mix'], ['vegetables mix', 'eggs', 'whole wheat rice'], ['shrimp', 'burgers', 'chocolate'], ['vegetables mix', 'asparagus'], ['shrimp', 'avocado', 'low fat yogurt'], ['vegetables mix', 'french fries'], ['shrimp', 'light mayo'], ['vegetables mix'], ['shrimp', 'pancakes'], ['shrimp'], ['vegetables mix', 'energy drink'], ['shrimp', 'mineral water'], ['shrimp', 'vegetables mix', 'low fat yogurt', 'cooking oil', 'chocolate', 'flax seed', 'babies food'], ['shrimp', 'whole weat flour', 'extra dark chocolate'], ['shrimp', 'vegetables mix', 'tomato juice', 'green tea', '', 'tomatoes', 'pancakes', 'grated cheese', 'brownies'], ['shrimp', 'frozen vegetables'], ['shrimp', 'spaghetti'], ['shrimp', 'avocado', 'candy bars'], ['shrimp', 'tomato juice', 'milk'], ['vegetables mix'], ['vegetables mix', 'salmon'], ['shrimp', 'soup', 'butter'], ['shrimp'], ['vegetables mix', 'frozen smoothie', 'ham'], ['vegetables mix'], ['vegetables mix', 'salmon', 'olive oil'], ['shrimp', 'champagne'], ['vegetables mix', 'honey', 'frozen smoothie'], ['shrimp', 'tomatoes'], ['shrimp', 'vegetables mix', 'green tea', 'meatballs', '', 'energy bar', 'whole wheat rice', 'tomatoes', 'carrots'], ['vegetables mix', 'mineral water', 'chocolate'], ['vegetables mix', 'green tea', 'burgers'], ['vegetables mix', 'pepper'], ['shrimp'], ['vegetables mix'], ['shrimp', 'spaghetti'], ['vegetables mix', 'spinach', 'muffins'], ['shrimp', 'pancakes', 'oatmeal'], ['shrimp', '', 'spaghetti'], ['shrimp', 'milk', 'spaghetti'], ['shrimp', 'vegetables mix', 'frozen smoothie', 'spaghetti', 'pancakes', 'mint'], ['shrimp', 'burgers', 'spaghetti'], ['shrimp', 'french fries', 'champagne'], ['vegetables mix', 'spaghetti', 'pancakes'], ['shrimp', 'chocolate', 'white wine'], ['shrimp', 'low fat yogurt'], ['shrimp'], ['vegetables mix', 'french fries'], ['shrimp', 'olive oil'], ['almonds', 'vegetables mix', 'milk'], ['shrimp', 'energy drink', 'eggs'], ['shrimp', 'frozen vegetables'], ['vegetables mix', 'spaghetti', 'tomatoes'], ['vegetables mix', 'french fries'], ['shrimp', 'avocado', 'melons'], ['shrimp', 'energy drink'], ['vegetables mix'], ['vegetables mix'], ['shrimp', 'green tea'], ['shrimp', 'tomatoes', 'herb & pepper'], ['vegetables mix', 'milk', 'french fries'], ['shrimp'], ['shrimp', 'tomato juice', 'magazines'], ['shrimp', 'mineral water', 'mint'], ['shrimp', 'spaghetti', 'tomatoes'], ['shrimp', 'low fat yogurt', 'gums'], ['shrimp', 'burgers'], ['shrimp', 'champagne', 'oil'], ['vegetables mix', 'milk', 'french fries'], ['shrimp', 'french fries'], ['shrimp', 'vegetables mix', 'spaghetti', 'cookies', 'rice', 'escalope'], ['shrimp', 'light cream', 'flax seed'], ['shrimp', 'pepper', 'chili'], ['shrimp', 'vegetables mix', '', 'frozen vegetables', 'chocolate', 'nonfat milk'], ['shrimp'], ['shrimp', 'gums'], ['vegetables mix'], ['shrimp', 'spaghetti', 'herb & pepper'], ['shrimp', 'vegetables mix', 'cookies', 'mushroom cream sauce'], ['shrimp', 'eggs', ''], ['shrimp', 'milk', 'french fries'], ['vegetables mix', 'energy drink'], ['vegetables mix', 'red wine', 'mushroom cream sauce'], ['shrimp'], ['shrimp', 'energy bar', 'spaghetti'], ['shrimp'], ['shrimp'], ['vegetables mix', 'salmon', 'whole wheat pasta'], ['vegetables mix', 'french fries', 'fresh bread'], ['shrimp', 'almonds', 'vegetables mix', 'honey', 'mineral water', 'burgers', 'strawberries', 'candy bars'], ['shrimp', 'mineral water', 'pancakes'], ['shrimp', 'vegetables mix', 'mineral water', 'eggs', 'whole wheat rice', 'frozen vegetables'], ['shrimp', 'mineral water'], ['vegetables mix', 'french wine'], ['shrimp', 'parmesan cheese'], ['vegetables mix', 'chutney'], ['vegetables mix', 'herb & pepper', 'clothes accessories'], ['vegetables mix'], ['shrimp', 'energy drink', 'green tea'], ['shrimp', 'french wine'], ['shrimp'], ['vegetables mix', 'french fries', 'spaghetti'], ['shrimp'], ['vegetables mix', 'green tea', 'cereals'], ['shrimp'], ['vegetables mix', 'mineral water', 'muffins'], ['vegetables mix', 'whole wheat rice', 'muffins'], ['shrimp', 'low fat yogurt', 'pasta']]\n"
          ]
        }
      ],
      "source": [
        "new_li_itemss=[]\n",
        "for i in range(len(newli)):\n",
        "  if newli[i]==1:\n",
        "    new_li_itemss.append(li[i])\n",
        "print(len(new_li_itemss))\n",
        "\n",
        "sim_li_itemms=[]\n",
        "\n",
        "for i in sim_li:\n",
        "  new_lii=[]\n",
        "  for j in range(len(i)):\n",
        "    if i[j]==1:\n",
        "      new_lii.append(li[j])\n",
        "  sim_li_itemms.append(new_lii)\n",
        "\n",
        "print(sim_li_itemms)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsKhsDcQfdlS",
        "outputId": "d1bfe42c-192d-4a6b-ee2b-0bb71ebb6af9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "customer basket ['shrimp', 'vegetables mix']\n",
            "Suggestions ['pasta', 'spaghetti', 'red wine', 'french fries', 'spaghetti', 'chocolate', 'water spray', 'mineral water', 'eggs', 'frozen vegetables', 'tomatoes', 'mineral water', 'champagne', 'green tea', 'fresh bread', 'low fat yogurt', 'mineral water', 'pepper', '', 'green tea', 'body spray', 'spinach', 'cookies', 'hot dogs', 'mushroom cream sauce', 'antioxydant juice', 'french fries', 'green tea', 'green tea', 'frozen vegetables', 'mineral water', 'spaghetti', 'pasta', 'fresh bread', 'butter', 'salmon', 'spaghetti', 'cookies', 'rice', 'hot dogs', 'frozen vegetables', 'spaghetti', 'tomatoes', 'babies food', 'tomatoes', 'frozen smoothie', 'frozen vegetables', 'soup', 'pancakes', 'nonfat milk', 'eggs', 'grated cheese', 'mineral water', 'herb & pepper', 'eggs', 'escalope', 'rice', 'cake', 'green tea', 'french fries', 'green grapes', 'eggs', 'frozen vegetables', 'champagne', 'mineral water', 'escalope', 'frozen vegetables', 'muffins', 'mineral water', 'yogurt cake', 'french fries', 'green tea', '', 'frozen vegetables', 'spaghetti', 'ham', 'tomato sauce', 'energy drink', 'cake', 'french fries', 'mineral water', 'brownies', 'muffins', 'mineral water', 'gums', 'eggs', 'whole wheat rice', 'burgers', 'chocolate', 'asparagus', 'avocado', 'low fat yogurt', 'french fries', 'light mayo', 'pancakes', 'energy drink', 'mineral water', 'low fat yogurt', 'cooking oil', 'chocolate', 'flax seed', 'babies food', 'whole weat flour', 'extra dark chocolate', 'tomato juice', 'green tea', '', 'tomatoes', 'pancakes', 'grated cheese', 'brownies', 'frozen vegetables', 'spaghetti', 'avocado', 'candy bars', 'tomato juice', 'milk', 'salmon', 'soup', 'butter', 'frozen smoothie', 'ham', 'salmon', 'olive oil', 'champagne', 'honey', 'frozen smoothie', 'tomatoes', 'green tea', 'meatballs', '', 'energy bar', 'whole wheat rice', 'tomatoes', 'carrots', 'mineral water', 'chocolate', 'green tea', 'burgers', 'pepper', 'spaghetti', 'spinach', 'muffins', 'pancakes', 'oatmeal', '', 'spaghetti', 'milk', 'spaghetti', 'frozen smoothie', 'spaghetti', 'pancakes', 'mint', 'burgers', 'spaghetti', 'french fries', 'champagne', 'spaghetti', 'pancakes', 'chocolate', 'white wine', 'low fat yogurt', 'french fries', 'olive oil', 'almonds', 'milk', 'energy drink', 'eggs', 'frozen vegetables', 'spaghetti', 'tomatoes', 'french fries', 'avocado', 'melons', 'energy drink', 'green tea', 'tomatoes', 'herb & pepper', 'milk', 'french fries', 'tomato juice', 'magazines', 'mineral water', 'mint', 'spaghetti', 'tomatoes', 'low fat yogurt', 'gums', 'burgers', 'champagne', 'oil', 'milk', 'french fries', 'french fries', 'spaghetti', 'cookies', 'rice', 'escalope', 'light cream', 'flax seed', 'pepper', 'chili', '', 'frozen vegetables', 'chocolate', 'nonfat milk', 'gums', 'spaghetti', 'herb & pepper', 'cookies', 'mushroom cream sauce', 'eggs', '', 'milk', 'french fries', 'energy drink', 'red wine', 'mushroom cream sauce', 'energy bar', 'spaghetti', 'salmon', 'whole wheat pasta', 'french fries', 'fresh bread', 'almonds', 'honey', 'mineral water', 'burgers', 'strawberries', 'candy bars', 'mineral water', 'pancakes', 'mineral water', 'eggs', 'whole wheat rice', 'frozen vegetables', 'mineral water', 'french wine', 'parmesan cheese', 'chutney', 'herb & pepper', 'clothes accessories', 'energy drink', 'green tea', 'french wine', 'french fries', 'spaghetti', 'green tea', 'cereals', 'mineral water', 'muffins', 'whole wheat rice', 'muffins', 'low fat yogurt', 'pasta']\n",
            "['shrimp', 0, 0, 0, 0, 0, 0, 0, 0, 0, 'low fat yogurt', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 'pasta', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "Suggestion top one ['low fat yogurt', 'pasta']\n"
          ]
        }
      ],
      "source": [
        "#for all similar baskets, suggestions/item recommendation\n",
        "sugges=[]\n",
        "for i in sim_li_itemms:\n",
        "  for j in i:\n",
        "    if j not in new_li_itemss:\n",
        "      sugges.append(j)\n",
        "print(\"customer basket\",new_li_itemss)\n",
        "print(\"Suggestions\",sugges)\n",
        "\n",
        "sugges_top_one=[]\n",
        "\n",
        "for i in range(len(top_one)):\n",
        "  if top_one[i]==1:\n",
        "    top_one[i]=li[i]\n",
        "print(top_one)\n",
        "for j in top_one:\n",
        "  if j not in new_li_itemss and j!=0:\n",
        "    sugges_top_one.append(j)\n",
        "print(\"Suggestion top one\",sugges_top_one)\n",
        "#for top 1 similar baset, item recommendation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0k5t3JXS81gQ",
        "outputId": "0a4c8263-936b-407f-d030-a82a11666b68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Customer basket\t\t Suggestion(all similar)\t\t top one suggestion\n",
            "shrimp\t\t\t pasta\t\t\t low fat yogurt\n",
            "vegetables mix\t\t\t spaghetti\t\t\t pasta\n",
            "\t\t\t red wine\n",
            "\t\t\t french fries\n",
            "\t\t\t spaghetti\n",
            "\t\t\t chocolate\n",
            "\t\t\t water spray\n",
            "\t\t\t mineral water\n",
            "\t\t\t eggs\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t tomatoes\n",
            "\t\t\t mineral water\n",
            "\t\t\t champagne\n",
            "\t\t\t green tea\n",
            "\t\t\t fresh bread\n",
            "\t\t\t low fat yogurt\n",
            "\t\t\t mineral water\n",
            "\t\t\t pepper\n",
            "\t\t\t \n",
            "\t\t\t green tea\n",
            "\t\t\t body spray\n",
            "\t\t\t spinach\n",
            "\t\t\t cookies\n",
            "\t\t\t hot dogs\n",
            "\t\t\t mushroom cream sauce\n",
            "\t\t\t antioxydant juice\n",
            "\t\t\t french fries\n",
            "\t\t\t green tea\n",
            "\t\t\t green tea\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t mineral water\n",
            "\t\t\t spaghetti\n",
            "\t\t\t pasta\n",
            "\t\t\t fresh bread\n",
            "\t\t\t butter\n",
            "\t\t\t salmon\n",
            "\t\t\t spaghetti\n",
            "\t\t\t cookies\n",
            "\t\t\t rice\n",
            "\t\t\t hot dogs\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t spaghetti\n",
            "\t\t\t tomatoes\n",
            "\t\t\t babies food\n",
            "\t\t\t tomatoes\n",
            "\t\t\t frozen smoothie\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t soup\n",
            "\t\t\t pancakes\n",
            "\t\t\t nonfat milk\n",
            "\t\t\t eggs\n",
            "\t\t\t grated cheese\n",
            "\t\t\t mineral water\n",
            "\t\t\t herb & pepper\n",
            "\t\t\t eggs\n",
            "\t\t\t escalope\n",
            "\t\t\t rice\n",
            "\t\t\t cake\n",
            "\t\t\t green tea\n",
            "\t\t\t french fries\n",
            "\t\t\t green grapes\n",
            "\t\t\t eggs\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t champagne\n",
            "\t\t\t mineral water\n",
            "\t\t\t escalope\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t muffins\n",
            "\t\t\t mineral water\n",
            "\t\t\t yogurt cake\n",
            "\t\t\t french fries\n",
            "\t\t\t green tea\n",
            "\t\t\t \n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t spaghetti\n",
            "\t\t\t ham\n",
            "\t\t\t tomato sauce\n",
            "\t\t\t energy drink\n",
            "\t\t\t cake\n",
            "\t\t\t french fries\n",
            "\t\t\t mineral water\n",
            "\t\t\t brownies\n",
            "\t\t\t muffins\n",
            "\t\t\t mineral water\n",
            "\t\t\t gums\n",
            "\t\t\t eggs\n",
            "\t\t\t whole wheat rice\n",
            "\t\t\t burgers\n",
            "\t\t\t chocolate\n",
            "\t\t\t asparagus\n",
            "\t\t\t avocado\n",
            "\t\t\t low fat yogurt\n",
            "\t\t\t french fries\n",
            "\t\t\t light mayo\n",
            "\t\t\t pancakes\n",
            "\t\t\t energy drink\n",
            "\t\t\t mineral water\n",
            "\t\t\t low fat yogurt\n",
            "\t\t\t cooking oil\n",
            "\t\t\t chocolate\n",
            "\t\t\t flax seed\n",
            "\t\t\t babies food\n",
            "\t\t\t whole weat flour\n",
            "\t\t\t extra dark chocolate\n",
            "\t\t\t tomato juice\n",
            "\t\t\t green tea\n",
            "\t\t\t \n",
            "\t\t\t tomatoes\n",
            "\t\t\t pancakes\n",
            "\t\t\t grated cheese\n",
            "\t\t\t brownies\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t spaghetti\n",
            "\t\t\t avocado\n",
            "\t\t\t candy bars\n",
            "\t\t\t tomato juice\n",
            "\t\t\t milk\n",
            "\t\t\t salmon\n",
            "\t\t\t soup\n",
            "\t\t\t butter\n",
            "\t\t\t frozen smoothie\n",
            "\t\t\t ham\n",
            "\t\t\t salmon\n",
            "\t\t\t olive oil\n",
            "\t\t\t champagne\n",
            "\t\t\t honey\n",
            "\t\t\t frozen smoothie\n",
            "\t\t\t tomatoes\n",
            "\t\t\t green tea\n",
            "\t\t\t meatballs\n",
            "\t\t\t \n",
            "\t\t\t energy bar\n",
            "\t\t\t whole wheat rice\n",
            "\t\t\t tomatoes\n",
            "\t\t\t carrots\n",
            "\t\t\t mineral water\n",
            "\t\t\t chocolate\n",
            "\t\t\t green tea\n",
            "\t\t\t burgers\n",
            "\t\t\t pepper\n",
            "\t\t\t spaghetti\n",
            "\t\t\t spinach\n",
            "\t\t\t muffins\n",
            "\t\t\t pancakes\n",
            "\t\t\t oatmeal\n",
            "\t\t\t \n",
            "\t\t\t spaghetti\n",
            "\t\t\t milk\n",
            "\t\t\t spaghetti\n",
            "\t\t\t frozen smoothie\n",
            "\t\t\t spaghetti\n",
            "\t\t\t pancakes\n",
            "\t\t\t mint\n",
            "\t\t\t burgers\n",
            "\t\t\t spaghetti\n",
            "\t\t\t french fries\n",
            "\t\t\t champagne\n",
            "\t\t\t spaghetti\n",
            "\t\t\t pancakes\n",
            "\t\t\t chocolate\n",
            "\t\t\t white wine\n",
            "\t\t\t low fat yogurt\n",
            "\t\t\t french fries\n",
            "\t\t\t olive oil\n",
            "\t\t\t almonds\n",
            "\t\t\t milk\n",
            "\t\t\t energy drink\n",
            "\t\t\t eggs\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t spaghetti\n",
            "\t\t\t tomatoes\n",
            "\t\t\t french fries\n",
            "\t\t\t avocado\n",
            "\t\t\t melons\n",
            "\t\t\t energy drink\n",
            "\t\t\t green tea\n",
            "\t\t\t tomatoes\n",
            "\t\t\t herb & pepper\n",
            "\t\t\t milk\n",
            "\t\t\t french fries\n",
            "\t\t\t tomato juice\n",
            "\t\t\t magazines\n",
            "\t\t\t mineral water\n",
            "\t\t\t mint\n",
            "\t\t\t spaghetti\n",
            "\t\t\t tomatoes\n",
            "\t\t\t low fat yogurt\n",
            "\t\t\t gums\n",
            "\t\t\t burgers\n",
            "\t\t\t champagne\n",
            "\t\t\t oil\n",
            "\t\t\t milk\n",
            "\t\t\t french fries\n",
            "\t\t\t french fries\n",
            "\t\t\t spaghetti\n",
            "\t\t\t cookies\n",
            "\t\t\t rice\n",
            "\t\t\t escalope\n",
            "\t\t\t light cream\n",
            "\t\t\t flax seed\n",
            "\t\t\t pepper\n",
            "\t\t\t chili\n",
            "\t\t\t \n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t chocolate\n",
            "\t\t\t nonfat milk\n",
            "\t\t\t gums\n",
            "\t\t\t spaghetti\n",
            "\t\t\t herb & pepper\n",
            "\t\t\t cookies\n",
            "\t\t\t mushroom cream sauce\n",
            "\t\t\t eggs\n",
            "\t\t\t \n",
            "\t\t\t milk\n",
            "\t\t\t french fries\n",
            "\t\t\t energy drink\n",
            "\t\t\t red wine\n",
            "\t\t\t mushroom cream sauce\n",
            "\t\t\t energy bar\n",
            "\t\t\t spaghetti\n",
            "\t\t\t salmon\n",
            "\t\t\t whole wheat pasta\n",
            "\t\t\t french fries\n",
            "\t\t\t fresh bread\n",
            "\t\t\t almonds\n",
            "\t\t\t honey\n",
            "\t\t\t mineral water\n",
            "\t\t\t burgers\n",
            "\t\t\t strawberries\n",
            "\t\t\t candy bars\n",
            "\t\t\t mineral water\n",
            "\t\t\t pancakes\n",
            "\t\t\t mineral water\n",
            "\t\t\t eggs\n",
            "\t\t\t whole wheat rice\n",
            "\t\t\t frozen vegetables\n",
            "\t\t\t mineral water\n",
            "\t\t\t french wine\n",
            "\t\t\t parmesan cheese\n",
            "\t\t\t chutney\n",
            "\t\t\t herb & pepper\n",
            "\t\t\t clothes accessories\n",
            "\t\t\t energy drink\n",
            "\t\t\t green tea\n",
            "\t\t\t french wine\n",
            "\t\t\t french fries\n",
            "\t\t\t spaghetti\n",
            "\t\t\t green tea\n",
            "\t\t\t cereals\n",
            "\t\t\t mineral water\n",
            "\t\t\t muffins\n",
            "\t\t\t whole wheat rice\n",
            "\t\t\t muffins\n",
            "\t\t\t low fat yogurt\n",
            "\t\t\t pasta\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ccd28237-5fc8-4faa-a434-d982abfb44b9\">\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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer basket</th>\n",
              "      <th>Suggestion(all similar)</th>\n",
              "      <th>top one suggestion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>shrimp</td>\n",
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              "      <td>pasta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>red wine</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>french fries</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>spaghetti</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>None</td>\n",
              "      <td>chocolate</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>None</td>\n",
              "      <td>water spray</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>None</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>None</td>\n",
              "      <td>eggs</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>None</td>\n",
              "      <td>frozen vegetables</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>None</td>\n",
              "      <td>tomatoes</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>None</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>None</td>\n",
              "      <td>champagne</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>None</td>\n",
              "      <td>green tea</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>None</td>\n",
              "      <td>fresh bread</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>None</td>\n",
              "      <td>low fat yogurt</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>None</td>\n",
              "      <td>mineral water</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>None</td>\n",
              "      <td>pepper</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>None</td>\n",
              "      <td></td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>None</td>\n",
              "      <td>green tea</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>None</td>\n",
              "      <td>body spray</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>None</td>\n",
              "      <td>spinach</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>None</td>\n",
              "      <td>cookies</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>None</td>\n",
              "      <td>hot dogs</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>None</td>\n",
              "      <td>mushroom cream sauce</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>None</td>\n",
              "      <td>antioxydant juice</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>None</td>\n",
              "      <td>french fries</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccd28237-5fc8-4faa-a434-d982abfb44b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "   Customer basket Suggestion(all similar) top one suggestion\n",
              "0           shrimp                   pasta     low fat yogurt\n",
              "1   vegetables mix               spaghetti              pasta\n",
              "2             None                red wine               None\n",
              "3             None            french fries               None\n",
              "4             None               spaghetti               None\n",
              "5             None               chocolate               None\n",
              "6             None             water spray               None\n",
              "7             None           mineral water               None\n",
              "8             None                    eggs               None\n",
              "9             None       frozen vegetables               None\n",
              "10            None                tomatoes               None\n",
              "11            None           mineral water               None\n",
              "12            None               champagne               None\n",
              "13            None               green tea               None\n",
              "14            None             fresh bread               None\n",
              "15            None          low fat yogurt               None\n",
              "16            None           mineral water               None\n",
              "17            None                  pepper               None\n",
              "18            None                                       None\n",
              "19            None               green tea               None\n",
              "20            None              body spray               None\n",
              "21            None                 spinach               None\n",
              "22            None                 cookies               None\n",
              "23            None                hot dogs               None\n",
              "24            None    mushroom cream sauce               None\n",
              "25            None       antioxydant juice               None\n",
              "26            None            french fries               None"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "#print\n",
        "t=0\n",
        "print(\"Customer basket\\t\\t Suggestion(all similar)\\t\\t top one suggestion\")\n",
        "while True:\n",
        " \n",
        "  if t<len(new_li_itemss):\n",
        "    print(new_li_itemss[t],end=\"\")\n",
        "  if t<len(sugges):\n",
        "    print(\"\\t\\t\\t\",sugges[t],end=\"\")\n",
        "  if t<len(sugges_top_one):\n",
        "    print(\"\\t\\t\\t\",sugges_top_one[t],end=\"\")\n",
        "  print()\n",
        "  if t>=len(new_li_itemss) and  t>=len(sugges) and t>=len(sugges_top_one):\n",
        "    break\n",
        "  t=t+1\n",
        "dff = pd.DataFrame([new_li_itemss,sugges,sugges_top_one],[\"Customer basket\",\"Suggestion(all similar)\",\"top one suggestion\"]).T\n",
        "dff.head(27)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}