Market_Basket_Analysis.ipynb

{
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    {
      "cell_type": "markdown",
      "id": "9518d7b3",
      "metadata": {
        "id": "9518d7b3"
      },
      "source": [
        "### MARKET BASKET ANALYSIS\n",
        "\n",
        "#### The market basket analysis aims to identify products purchased together and to make predictionsn and recommendations based on the result of the analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d26c76ed",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "d26c76ed",
        "outputId": "9ae6f9ca-2374-4d1e-c40b-b547a809bd23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Required libaries imported\n"
          ]
        }
      ],
      "source": [
        "#Importing the required libraries\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "#Importing the required libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "print('Required libaries imported')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing files(dataset)\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
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        {
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              "\n",
              "     <input type=\"file\" id=\"files-548c8909-1ed4-434c-b0b8-84a041f7b343\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-548c8909-1ed4-434c-b0b8-84a041f7b343\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
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              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Market_Basket_Analysis-Groceries_dataset.csv to Market_Basket_Analysis-Groceries_dataset.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "113a8c27",
      "metadata": {
        "id": "113a8c27"
      },
      "outputs": [],
      "source": [
        "# loading of the groceries dataset\n",
        "grocery= pd.read_csv('Market_Basket_Analysis-Groceries_dataset.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fac08590",
      "metadata": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Member_number        Date   itemDescription\n",
              "0           1808  21-07-2015    tropical fruit\n",
              "1           2552  05-01-2015        whole milk\n",
              "2           2300  19-09-2015         pip fruit\n",
              "3           1187  12-12-2015  other vegetables\n",
              "4           3037  01-02-2015        whole milk"
            ],
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              "      <td>1808</td>\n",
              "      <td>21-07-2015</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2552</td>\n",
              "      <td>05-01-2015</td>\n",
              "      <td>whole milk</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2300</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1187</td>\n",
              "      <td>12-12-2015</td>\n",
              "      <td>other vegetables</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3037</td>\n",
              "      <td>01-02-2015</td>\n",
              "      <td>whole milk</td>\n",
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              "\n",
              "  @keyframes spin {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# checking the first five rows\n",
        "grocery.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6116bb53",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6116bb53",
        "outputId": "e08168cf-8a93-4e49-d617-f44f999af9d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38765 entries, 0 to 38764\n",
            "Data columns (total 3 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   Member_number    38765 non-null  int64 \n",
            " 1   Date             38765 non-null  object\n",
            " 2   itemDescription  38765 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 908.7+ KB\n"
          ]
        }
      ],
      "source": [
        "#checking to see if there are any nulls\n",
        "grocery.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06d09857",
      "metadata": {
        "id": "06d09857"
      },
      "outputs": [],
      "source": [
        "#convert date as object to datetime\n",
        "grocery['Date']= pd.to_datetime(grocery['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7afd7f6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7afd7f6",
        "outputId": "a3f365da-022a-4774-91d4-277f27cd3f80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38765 entries, 0 to 38764\n",
            "Data columns (total 3 columns):\n",
            " #   Column           Non-Null Count  Dtype         \n",
            "---  ------           --------------  -----         \n",
            " 0   Member_number    38765 non-null  int64         \n",
            " 1   Date             38765 non-null  datetime64[ns]\n",
            " 2   itemDescription  38765 non-null  object        \n",
            "dtypes: datetime64[ns](1), int64(1), object(1)\n",
            "memory usage: 908.7+ KB\n"
          ]
        }
      ],
      "source": [
        "#getting the info again to check if the  date column was converted to date time\n",
        "grocery.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68315c19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68315c19",
        "outputId": "506776c9-d72e-430e-ed61-6480b5b49548"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Member_number      0\n",
              "Date               0\n",
              "itemDescription    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "#Just to confirm there are no null values\n",
        "grocery.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f2bfdcb",
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              "       Member_number\n",
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              "mean     3003.641868\n",
              "std      1153.611031\n",
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          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#Description of the dataset\n",
        "grocery.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed79297f",
      "metadata": {
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        "id": "ed79297f",
        "outputId": "0cfef3e3-9d71-4bc0-a42a-a6339712c29a"
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        {
          "output_type": "execute_result",
          "data": {
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              "   Member_number       Date   itemDescription     single_trans\n",
              "0           1808 2015-07-21    tropical fruit  1808_2015-07-21\n",
              "1           2552 2015-05-01        whole milk  2552_2015-05-01\n",
              "2           2300 2015-09-19         pip fruit  2300_2015-09-19\n",
              "3           1187 2015-12-12  other vegetables  1187_2015-12-12\n",
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              "        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",
              "\n",
              "\n",
              "<div id=\"df-96bdeef3-1128-48a0-9a6c-fd8e20a34485\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-96bdeef3-1128-48a0-9a6c-fd8e20a34485')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-96bdeef3-1128-48a0-9a6c-fd8e20a34485 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#adding a new column that groups each transaction by member number and date to provide a list of products purchased in the same transaction.\n",
        "grocery['single_trans'] =  grocery['Member_number'].astype(str)+'_'+grocery['Date'].astype(str)\n",
        "grocery.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf83472e",
      "metadata": {
        "id": "cf83472e"
      },
      "outputs": [],
      "source": [
        "#creating a basket matrix by grouping multiple items within the same order and then unstacking to transpose the itemDescription column to row level\n",
        "bas = grocery.groupby(['single_trans', 'itemDescription']).sum().unstack().reset_index().fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "899e2b3a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "899e2b3a",
        "outputId": "e32ecd36-5e05-4f89-d952-a88616f68f07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    single_trans         Member_number           \\\n",
              "itemDescription                  Instant food products UHT-milk   \n",
              "0                1000_2014-06-24                   0.0      0.0   \n",
              "1                1000_2015-03-15                   0.0      0.0   \n",
              "2                1000_2015-05-27                   0.0      0.0   \n",
              "3                1000_2015-07-24                   0.0      0.0   \n",
              "4                1000_2015-11-25                   0.0      0.0   \n",
              "\n",
              "                                                                       \\\n",
              "itemDescription abrasive cleaner artif. sweetener baby cosmetics bags   \n",
              "0                            0.0              0.0            0.0  0.0   \n",
              "1                            0.0              0.0            0.0  0.0   \n",
              "2                            0.0              0.0            0.0  0.0   \n",
              "3                            0.0              0.0            0.0  0.0   \n",
              "4                            0.0              0.0            0.0  0.0   \n",
              "\n",
              "                                                     ...                 \\\n",
              "itemDescription baking powder bathroom cleaner beef  ... turkey vinegar   \n",
              "0                         0.0              0.0  0.0  ...    0.0     0.0   \n",
              "1                         0.0              0.0  0.0  ...    0.0     0.0   \n",
              "2                         0.0              0.0  0.0  ...    0.0     0.0   \n",
              "3                         0.0              0.0  0.0  ...    0.0     0.0   \n",
              "4                         0.0              0.0  0.0  ...    0.0     0.0   \n",
              "\n",
              "                                                                          \\\n",
              "itemDescription waffles whipped/sour cream whisky white bread white wine   \n",
              "0                   0.0                0.0    0.0         0.0        0.0   \n",
              "1                   0.0                0.0    0.0         0.0        0.0   \n",
              "2                   0.0                0.0    0.0         0.0        0.0   \n",
              "3                   0.0                0.0    0.0         0.0        0.0   \n",
              "4                   0.0                0.0    0.0         0.0        0.0   \n",
              "\n",
              "                                             \n",
              "itemDescription whole milk  yogurt zwieback  \n",
              "0                   1000.0     0.0      0.0  \n",
              "1                   1000.0  1000.0      0.0  \n",
              "2                      0.0     0.0      0.0  \n",
              "3                      0.0     0.0      0.0  \n",
              "4                      0.0     0.0      0.0  \n",
              "\n",
              "[5 rows x 168 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e23fb8bd-ed77-491e-9951-a2d322ead235\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>single_trans</th>\n",
              "      <th colspan=\"20\" halign=\"left\">Member_number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>itemDescription</th>\n",
              "      <th></th>\n",
              "      <th>Instant food products</th>\n",
              "      <th>UHT-milk</th>\n",
              "      <th>abrasive cleaner</th>\n",
              "      <th>artif. sweetener</th>\n",
              "      <th>baby cosmetics</th>\n",
              "      <th>bags</th>\n",
              "      <th>baking powder</th>\n",
              "      <th>bathroom cleaner</th>\n",
              "      <th>beef</th>\n",
              "      <th>...</th>\n",
              "      <th>turkey</th>\n",
              "      <th>vinegar</th>\n",
              "      <th>waffles</th>\n",
              "      <th>whipped/sour cream</th>\n",
              "      <th>whisky</th>\n",
              "      <th>white bread</th>\n",
              "      <th>white wine</th>\n",
              "      <th>whole milk</th>\n",
              "      <th>yogurt</th>\n",
              "      <th>zwieback</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000_2014-06-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000_2015-03-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000_2015-05-27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000_2015-07-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000_2015-11-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 168 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e23fb8bd-ed77-491e-9951-a2d322ead235')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "\n",
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              "        document.querySelector('#df-e23fb8bd-ed77-491e-9951-a2d322ead235 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-e23fb8bd-ed77-491e-9951-a2d322ead235');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "bas.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bas1 = pd.crosstab(grocery['single_trans'], grocery['itemDescription'])"
      ],
      "metadata": {
        "id": "aIgz_vNMMg2u"
      },
      "id": "aIgz_vNMMg2u",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "05c8f51a",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "05c8f51a",
        "outputId": "312e7ad3-18a3-4139-c442-7021bd025424"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "itemDescription  Instant food products  UHT-milk  abrasive cleaner  \\\n",
              "single_trans                                                         \n",
              "1000_2014-06-24                      0         0                 0   \n",
              "1000_2015-03-15                      0         0                 0   \n",
              "1000_2015-05-27                      0         0                 0   \n",
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              "1000_2015-11-25                      0         0                 0   \n",
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              "5000_2015-10-02                      0         0                 0   \n",
              "\n",
              "itemDescription  artif. sweetener  baby cosmetics  bags  baking powder  \\\n",
              "single_trans                                                             \n",
              "1000_2014-06-24                 0               0     0              0   \n",
              "1000_2015-03-15                 0               0     0              0   \n",
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              "1000_2015-11-25                 0               0     0              0   \n",
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              "5000_2015-10-02                 0               0     0              0   \n",
              "\n",
              "itemDescription  bathroom cleaner  beef  berries  ...  turkey  vinegar  \\\n",
              "single_trans                                      ...                    \n",
              "1000_2014-06-24                 0     0        0  ...       0        0   \n",
              "1000_2015-03-15                 0     0        0  ...       0        0   \n",
              "1000_2015-05-27                 0     0        0  ...       0        0   \n",
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              "5000_2014-09-03                 0     0        0  ...       0        0   \n",
              "5000_2014-11-16                 0     0        0  ...       0        0   \n",
              "5000_2015-10-02                 0     0        0  ...       0        0   \n",
              "\n",
              "itemDescription  waffles  whipped/sour cream  whisky  white bread  white wine  \\\n",
              "single_trans                                                                    \n",
              "1000_2014-06-24        0                   0       0            0           0   \n",
              "1000_2015-03-15        0                   0       0            0           0   \n",
              "1000_2015-05-27        0                   0       0            0           0   \n",
              "1000_2015-07-24        0                   0       0            0           0   \n",
              "1000_2015-11-25        0                   0       0            0           0   \n",
              "...                  ...                 ...     ...          ...         ...   \n",
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              "5000_2014-11-16        0                   0       0            0           0   \n",
              "5000_2015-10-02        0                   0       0            0           0   \n",
              "\n",
              "itemDescription  whole milk  yogurt  zwieback  \n",
              "single_trans                                   \n",
              "1000_2014-06-24           1       0         0  \n",
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              "[14963 rows x 167 columns]"
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-3c568b00-9f85-40f7-9437-0381310375ff 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-3c568b00-9f85-40f7-9437-0381310375ff');\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",
              "\n",
              "\n",
              "<div id=\"df-3e754de6-4ea9-4643-811f-f268ccc6dfec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e754de6-4ea9-4643-811f-f268ccc6dfec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3e754de6-4ea9-4643-811f-f268ccc6dfec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "#encode function converts the collection of strings in the dataframe to bytes like 1s and 0s\n",
        "#the values need to be encoded so the values in our matrix is 1’s and 0’s. This is done in the following manner:\n",
        "\n",
        "#Set the value to 0 if it is less than or equal to 0.\n",
        "#Set the value to 1 if it is greater than or equal to 1.\n",
        "\n",
        "def encode_units(x):\n",
        "    if x <= 0:\n",
        "        return 0\n",
        "    if x >= 1:\n",
        "        return 1\n",
        "\n",
        "basket_set= bas1.applymap(encode_units)\n",
        "\n",
        "basket_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45414025",
      "metadata": {
        "id": "45414025"
      },
      "outputs": [],
      "source": [
        "# importing the apriori library from mlxtend\n",
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7b2cab9",
      "metadata": {
        "id": "e7b2cab9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "4773fdfe-20af-4167-bcb3-b62c967e910f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       antecedents      consequents  antecedent support  consequent support  \\\n",
              "0     (whole milk)   (bottled beer)            0.157923            0.045312   \n",
              "1   (bottled beer)     (whole milk)            0.045312            0.157923   \n",
              "2     (whole milk)    (canned beer)            0.157923            0.046916   \n",
              "3    (canned beer)     (whole milk)            0.046916            0.157923   \n",
              "4     (whole milk)   (citrus fruit)            0.157923            0.053131   \n",
              "5   (citrus fruit)     (whole milk)            0.053131            0.157923   \n",
              "6     (whole milk)  (domestic eggs)            0.157923            0.037091   \n",
              "7  (domestic eggs)     (whole milk)            0.037091            0.157923   \n",
              "\n",
              "    support  confidence      lift  leverage  conviction  zhangs_metric  \n",
              "0  0.007151    0.045281  0.999330 -0.000005    0.999968      -0.000795  \n",
              "1  0.007151    0.157817  0.999330 -0.000005    0.999874      -0.000702  \n",
              "2  0.006015    0.038087  0.811821 -0.001394    0.990822      -0.215852  \n",
              "3  0.006015    0.128205  0.811821 -0.001394    0.965912      -0.195630  \n",
              "4  0.007151    0.045281  0.852259 -0.001240    0.991778      -0.170718  \n",
              "5  0.007151    0.134591  0.852259 -0.001240    0.973040      -0.154748  \n",
              "6  0.005280    0.033432  0.901341 -0.000578    0.996214      -0.115033  \n",
              "7  0.005280    0.142342  0.901341 -0.000578    0.981834      -0.102072  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e62ef32d-9b9e-4364-a0e7-83c8d6287e4e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>zhangs_metric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>(bottled beer)</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.045312</td>\n",
              "      <td>0.007151</td>\n",
              "      <td>0.045281</td>\n",
              "      <td>0.999330</td>\n",
              "      <td>-0.000005</td>\n",
              "      <td>0.999968</td>\n",
              "      <td>-0.000795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(bottled beer)</td>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>0.045312</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.007151</td>\n",
              "      <td>0.157817</td>\n",
              "      <td>0.999330</td>\n",
              "      <td>-0.000005</td>\n",
              "      <td>0.999874</td>\n",
              "      <td>-0.000702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>(canned beer)</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.046916</td>\n",
              "      <td>0.006015</td>\n",
              "      <td>0.038087</td>\n",
              "      <td>0.811821</td>\n",
              "      <td>-0.001394</td>\n",
              "      <td>0.990822</td>\n",
              "      <td>-0.215852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(canned beer)</td>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>0.046916</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.006015</td>\n",
              "      <td>0.128205</td>\n",
              "      <td>0.811821</td>\n",
              "      <td>-0.001394</td>\n",
              "      <td>0.965912</td>\n",
              "      <td>-0.195630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>(citrus fruit)</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.053131</td>\n",
              "      <td>0.007151</td>\n",
              "      <td>0.045281</td>\n",
              "      <td>0.852259</td>\n",
              "      <td>-0.001240</td>\n",
              "      <td>0.991778</td>\n",
              "      <td>-0.170718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(citrus fruit)</td>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>0.053131</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.007151</td>\n",
              "      <td>0.134591</td>\n",
              "      <td>0.852259</td>\n",
              "      <td>-0.001240</td>\n",
              "      <td>0.973040</td>\n",
              "      <td>-0.154748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>(domestic eggs)</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.037091</td>\n",
              "      <td>0.005280</td>\n",
              "      <td>0.033432</td>\n",
              "      <td>0.901341</td>\n",
              "      <td>-0.000578</td>\n",
              "      <td>0.996214</td>\n",
              "      <td>-0.115033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(domestic eggs)</td>\n",
              "      <td>(whole milk)</td>\n",
              "      <td>0.037091</td>\n",
              "      <td>0.157923</td>\n",
              "      <td>0.005280</td>\n",
              "      <td>0.142342</td>\n",
              "      <td>0.901341</td>\n",
              "      <td>-0.000578</td>\n",
              "      <td>0.981834</td>\n",
              "      <td>-0.102072</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e62ef32d-9b9e-4364-a0e7-83c8d6287e4e')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e62ef32d-9b9e-4364-a0e7-83c8d6287e4e 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-e62ef32d-9b9e-4364-a0e7-83c8d6287e4e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c13e8078-dc83-448d-a18d-2f12aa12768c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#compute frequent itemsets\n",
        "frequent_itemsets = apriori(basket_set, min_support=0.005, use_colnames=True)\n",
        "\n",
        "#compute standard metric\n",
        "rules = association_rules(frequent_itemsets, metric=\"lift\")\n",
        "#rules.sort_values([\"support\", \"confidence\",\"lift\"],axis = 0, ascending = False)\n",
        "rules.sort_values(by='zhangs_metric', ascending=False)\n",
        "rules.head(8)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to identify the natural threshold for pruning\n",
        "sns.scatterplot(x='antecedent support', y= 'consequent support', size='zhangs_metric', data=rules)"
      ],
      "metadata": {
        "id": "qN7I1M_thHBQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "7ba46864-4f7b-4b12-eb24-31542f597cb9"
      },
      "id": "qN7I1M_thHBQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='antecedent support', ylabel='consequent support'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the scatterplot visualization, it can be deduced that the highest degree of association in relation to zhangs metric as regards the consequent support\n",
        " and antecedent support of the variables are 0.06 and 0.12"
      ],
      "metadata": {
        "id": "T_VZZ5OkKHIX"
      },
      "id": "T_VZZ5OkKHIX"
    },
    {
      "cell_type": "code",
      "source": [
        "#Tranforming antecedent, consequent, and zhangs_metric into a matrix in preparation for heatmap chart\n",
        "matrix= rules.pivot(index= 'antecedents', columns= 'consequents', values= 'zhangs_metric')\n",
        "\n",
        "#generate heatmap\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(matrix, annot=True, cbar= False, cmap='ocean', fmt= '.1f', linewidths= .5)\n",
        "plt.title('Items frequently purchased together(Association rules)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "bm_eVduDMbBk",
        "outputId": "1ecb9db3-ad78-4a0d-e635-477afd16476c"
      },
      "id": "bm_eVduDMbBk",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bRIBwiJ6BXM2"
      },
      "id": "bRIBwiJ6BXM2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " The occurence of negative zhangs values suggest a degree a dissociation between the items. While the positive zhangs values suggest a strong degree of\n",
        " association between the items and so it can be predicted that these items will likely be purchased together.\n",
        " for instance, sausage and yoghurt has a zhang metric of 0.1, which indicates a degree of association and so these items are predicted to be purchased in the future.\n",
        " While milk and citrus friut has a zhang metric of -0.2 and this indicates a degree of dissociation and this suggests these items will most likely not be purchased together.\n"
      ],
      "metadata": {
        "id": "zkyHGB4MHG6z"
      },
      "id": "zkyHGB4MHG6z"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pCkZJ7iYJ3sj"
      },
      "id": "pCkZJ7iYJ3sj",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}