EDA_V2_Preprocessing-pipelines.ipynb

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          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
            "Collecting sklearn\n",
            "  Downloading sklearn-0.0.post12.tar.gz (2.6 kB)\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for details.\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.48.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
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            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Mounted at /content/drive\n"
          ]
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      "source": [
        "!pip install pandas\n",
        "import pandas as pd\n",
        "!pip install sklearn\n",
        "import sklearn as sk\n",
        "!pip install matplotlib\n",
        "import matplotlib as plt\n",
        "import seaborn as sns\n",
        "import csv\n",
        "from google.colab import files\n",
        "import re\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load bank data from CSV files in Google Drive\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/bank_marketing_dataset.csv\")\n",
        "drive.mount(\"/content/drive\", force_remount=True)"
      ],
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "### EDA"
      ],
      "metadata": {
        "id": "46Dc1whyHsiV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First let's display the first few rows to understand data's structure\n",
        "pd.set_option('display.max_rows', None)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "9WAv-Il_F5zo",
        "outputId": "ff4576b5-d3e1-4191-e36c-661a4295304e"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "   age        job  marital    education  default housing loan    contact  \\\n",
              "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
              "1   57   services  married  high.school  unknown      no   no  telephone   \n",
              "2   37   services  married  high.school       no     yes   no  telephone   \n",
              "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
              "4   56   services  married  high.school       no      no  yes  telephone   \n",
              "\n",
              "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
              "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "\n",
              "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \n",
              "0          93.994          -36.4      4.857       5191.0          no  \n",
              "1          93.994          -36.4      4.857       5191.0          no  \n",
              "2          93.994          -36.4      4.857       5191.0          no  \n",
              "3          93.994          -36.4      4.857       5191.0          no  \n",
              "4          93.994          -36.4      4.857       5191.0          no  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "  <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-bba55118-44d4-4905-94da-6966c35dadc5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary stats for numerical features\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "LtsoxO3UGoMu",
        "outputId": "f8d15977-9c6e-451b-890d-fa115ca2ef0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age      duration      campaign         pdays      previous  \\\n",
              "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
              "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
              "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
              "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
              "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
              "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
              "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
              "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
              "\n",
              "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
              "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
              "mean       0.081886       93.575664     -40.502600      3.621291   5167.035911  \n",
              "std        1.570960        0.578840       4.628198      1.734447     72.251528  \n",
              "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
              "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
              "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
              "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
              "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9700680d-5365-49cd-82f1-906b8a09650e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41188.00000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.02406</td>\n",
              "      <td>258.285010</td>\n",
              "      <td>2.567593</td>\n",
              "      <td>962.475454</td>\n",
              "      <td>0.172963</td>\n",
              "      <td>0.081886</td>\n",
              "      <td>93.575664</td>\n",
              "      <td>-40.502600</td>\n",
              "      <td>3.621291</td>\n",
              "      <td>5167.035911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.42125</td>\n",
              "      <td>259.279249</td>\n",
              "      <td>2.770014</td>\n",
              "      <td>186.910907</td>\n",
              "      <td>0.494901</td>\n",
              "      <td>1.570960</td>\n",
              "      <td>0.578840</td>\n",
              "      <td>4.628198</td>\n",
              "      <td>1.734447</td>\n",
              "      <td>72.251528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.400000</td>\n",
              "      <td>92.201000</td>\n",
              "      <td>-50.800000</td>\n",
              "      <td>0.634000</td>\n",
              "      <td>4963.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.00000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.800000</td>\n",
              "      <td>93.075000</td>\n",
              "      <td>-42.700000</td>\n",
              "      <td>1.344000</td>\n",
              "      <td>5099.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.00000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>93.749000</td>\n",
              "      <td>-41.800000</td>\n",
              "      <td>4.857000</td>\n",
              "      <td>5191.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>47.00000</td>\n",
              "      <td>319.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>93.994000</td>\n",
              "      <td>-36.400000</td>\n",
              "      <td>4.961000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.00000</td>\n",
              "      <td>4918.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>94.767000</td>\n",
              "      <td>-26.900000</td>\n",
              "      <td>5.045000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9700680d-5365-49cd-82f1-906b8a09650e')\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-9700680d-5365-49cd-82f1-906b8a09650e 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-9700680d-5365-49cd-82f1-906b8a09650e');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "    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",
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              "\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",
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              "    30% {\n",
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              "      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-7fccee6e-f7e9-479d-b693-c6ce2daf7ec6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "  </div>\n"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14547.915389437558,\n        \"min\": 10.421249980934048,\n        \"max\": 41188.0,\n        \"samples\": [\n          40.02406040594348,\n          38.0,\n          41188.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14353.429834977824,\n        \"min\": 0.0,\n        \"max\": 41188.0,\n        \"samples\": [\n          258.2850101971448,\n          180.0,\n          41188.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14558.717737799894,\n        \"min\": 1.0,\n        \"max\": 41188.0,\n        \"samples\": [\n          41188.0,\n          2.567592502670681,\n          3.0\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14308.123597767704,\n        \"min\": 0.0,\n        \"max\": 41188.0,\n        \"samples\": [\n          962.4754540157328,\n          999.0,\n          186.9109073447418\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14561.769966637723,\n        \"min\": 0.0,\n        \"max\": 41188.0,\n        \"samples\": [\n          0.17296299893172767,\n          7.0,\n          0.4949010798392897\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"emp.var.rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14562.139340189238,\n        \"min\": -3.4,\n        \"max\": 41188.0,\n        \"samples\": [\n          41188.0,\n          0.08188550063125165,\n          1.1\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cons.price.idx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14533.81120862695,\n        \"min\": 0.5788400489541262,\n        \"max\": 41188.0,\n        \"samples\": [\n          93.57566436826262,\n          93.749,\n          41188.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cons.conf.idx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14574.009636834653,\n        \"min\": -50.8,\n        \"max\": 41188.0,\n        \"samples\": [\n          -40.50260027192386,\n          -41.8,\n          41188.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"euribor3m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14561.036053130754,\n        \"min\": 0.634,\n        \"max\": 41188.0,\n        \"samples\": [\n          3.621290812858114,\n          4.857,\n          41188.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nr.employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13119.9618027212,\n        \"min\": 72.25152766826108,\n        \"max\": 41188.0,\n        \"samples\": [\n          41188.0,\n          5167.035910944936,\n          5191.0\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of categorical features\n",
        "categorical_features = data.select_dtypes(include=['object']).columns\n",
        "data[categorical_features].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "5KWUrfJtG258",
        "outputId": "e9c6d777-ba86-41a1-f6be-aab61c659620"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           job  marital          education default housing   loan   contact  \\\n",
              "count    41188    41188              41188   41188   41188  41188     41188   \n",
              "unique      12        4                  8       3       3      3         2   \n",
              "top     admin.  married  university.degree      no     yes     no  cellular   \n",
              "freq     10422    24928              12168   32588   21576  33950     26144   \n",
              "\n",
              "        month day_of_week     poutcome subscribed  \n",
              "count   41188       41188        41188      41188  \n",
              "unique     10           5            3          2  \n",
              "top       may         thu  nonexistent         no  \n",
              "freq    13769        8623        35563      36548  "
            ],
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              "      <td>41188</td>\n",
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              "      <td>yes</td>\n",
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              "      <td>may</td>\n",
              "      <td>thu</td>\n",
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              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>10422</td>\n",
              "      <td>24928</td>\n",
              "      <td>12168</td>\n",
              "      <td>32588</td>\n",
              "      <td>21576</td>\n",
              "      <td>33950</td>\n",
              "      <td>26144</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[categorical_features]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          12,\n          \"10422\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          4,\n          \"24928\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          8,\n          \"12168\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          3,\n          \"32588\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          3,\n          \"21576\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          3,\n          \"33950\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contact\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          2,\n          \"26144\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          10,\n          \"13769\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          5,\n          \"8623\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poutcome\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          3,\n          \"35563\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subscribed\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          2,\n          \"36548\",\n          \"41188\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0qZZMzrG4tn",
        "outputId": "1d5827b0-a242-4305-f049-2e52c6fa1194"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "job               0\n",
              "marital           0\n",
              "education         0\n",
              "default           0\n",
              "housing           0\n",
              "loan              0\n",
              "contact           0\n",
              "month             0\n",
              "day_of_week       0\n",
              "duration          0\n",
              "campaign          0\n",
              "pdays             0\n",
              "previous          0\n",
              "poutcome          0\n",
              "emp.var.rate      0\n",
              "cons.price.idx    0\n",
              "cons.conf.idx     0\n",
              "euribor3m         0\n",
              "nr.employed       0\n",
              "subscribed        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use seaborn whitegrid style for plots\n",
        "sns.set_style(\"whitegrid\")"
      ],
      "metadata": {
        "id": "-QQwGs8oG75f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Have to load pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Age\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(data['age'], bins=30, kde=True)\n",
        "plt.title('Distribution of Age')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "QruyTPu4HM40",
        "outputId": "a8867e7e-b55e-4c70-c406-5b4178d99450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Job Type\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(y=data['job'], order = data['job'].value_counts().index)\n",
        "plt.title('Job Distribution')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Job Type')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "9D9B_PCBHQIb",
        "outputId": "e967164f-5dc3-4465-8167-79ad9b2bd8a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Marital Status\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(data['marital'], order = data['marital'].value_counts().index)\n",
        "plt.title('Marital Status Distribution')\n",
        "plt.xlabel('Marital Status')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Vi5Np7ugHeQn",
        "outputId": "457c1a06-7d9c-4cc4-87b2-c6373aee69c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Education\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(y=data['education'], order = data['education'].value_counts().index)\n",
        "plt.title('Education Level Distribution')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Education Level')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "oGBUz6YlHg_s",
        "outputId": "8c243974-f1da-4d15-b595-0bd56b3654ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Subscription Status\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(data['subscribed'])\n",
        "plt.title('Subscription Status')\n",
        "plt.xlabel('Subscribed')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "UuOjaQR0Hjpg",
        "outputId": "2ec06f72-4c66-43f1-959e-a4be15f1eb0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loan\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(data['loan'])\n",
        "plt.title('Loan Frequency')\n",
        "plt.xlabel('Loan')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ezQdZr-dJFyl",
        "outputId": "98beffcd-2f43-42c8-c752-37008cd3f704"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Heatmap\n",
        "plt.figure(figsize=(12, 10))\n",
        "correlation_matrix = data.select_dtypes(include=['int64', 'float64']).corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Heatmap for Numerical Features')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "rJugGBNZHnKA",
        "outputId": "5bfb7a61-5196-4a77-a8a8-35e2e3436ccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocessing"
      ],
      "metadata": {
        "id": "jswYDwyeIHFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# There are no missing values as seen before\n",
        "data.isnull().sum()\n",
        "# This means theres no need for imputation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rm912IfIIh7",
        "outputId": "69aa291c-9a46-49d9-be57-a285a1ec6383"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "job               0\n",
              "marital           0\n",
              "education         0\n",
              "default           0\n",
              "housing           0\n",
              "loan              0\n",
              "contact           0\n",
              "month             0\n",
              "day_of_week       0\n",
              "duration          0\n",
              "campaign          0\n",
              "pdays             0\n",
              "previous          0\n",
              "poutcome          0\n",
              "emp.var.rate      0\n",
              "cons.price.idx    0\n",
              "cons.conf.idx     0\n",
              "euribor3m         0\n",
              "nr.employed       0\n",
              "subscribed        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "\n",
        "# Identify numerical and categorical columns\n",
        "numerical_cols = data.select_dtypes(include=['int64', 'float64']).columns\n",
        "categorical_cols = data.select_dtypes(include=['object']).columns.drop('subscribed')  # Exclude target variable (CHANGE WHEN WE KNOW WHATS TARGET)"
      ],
      "metadata": {
        "id": "b9DBD8V-IOpm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Numerical data preprocessing\n",
        "# Directly scale numerical features since there are no missing values\n",
        "scaler = StandardScaler()\n",
        "data_num_scaled = scaler.fit_transform(data[numerical_cols])"
      ],
      "metadata": {
        "id": "RPJgFlt0NxdW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorical data preprocessing\n",
        "# Again directly apply one-hot encoding since there are no missing values\n",
        "onehot = OneHotEncoder(handle_unknown='ignore')\n",
        "data_cat_onehot = onehot.fit_transform(data[categorical_cols]).toarray()"
      ],
      "metadata": {
        "id": "pmP_S3AXNziJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine numerical and categorical features back into dataset\n",
        "X_preprocessed = np.hstack((data_num_scaled, data_cat_onehot))"
      ],
      "metadata": {
        "id": "qmGUsrhMN1pm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set target variable as y\n",
        "y = data['subscribed'].map({'yes': 1, 'no': 0})  # Converts target variable to numerical"
      ],
      "metadata": {
        "id": "kzGa678JOQhF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into training and validation sets\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X_preprocessed, y, train_size=0.8, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "VnDlZKG0N7Dw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check shape of the directly preprocessed training data to make sure its good\n",
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8eNg4ErOMe8",
        "outputId": "cbda919e-b57c-4cb4-ba17-a79763e5809e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32950, 63)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocessing with Pipelines"
      ],
      "metadata": {
        "id": "PquXQQKDOfvY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "metadata": {
        "id": "eJkDLD2cNvLB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing for numerical data: Scaling only\n",
        "numerical_transformer = Pipeline(steps=[\n",
        "    ('scaler', StandardScaler())\n",
        "])"
      ],
      "metadata": {
        "id": "tJiK3NP8Pmw6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing for categorical data: OneHotEncoding only\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
        "])"
      ],
      "metadata": {
        "id": "DzRK-7h-P0SS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Bundle preprocessing for numerical and categorical data\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numerical_transformer, numerical_cols),\n",
        "        ('cat', categorical_transformer, categorical_cols)\n",
        "    ])"
      ],
      "metadata": {
        "id": "U5MTXXPZP2DE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set target variable as y\n",
        "y = data['subscribed'].map({'yes': 1, 'no': 0})  # Converts target variable to numerical"
      ],
      "metadata": {
        "id": "LK2c8-4rQfen"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into training and validation sets\n",
        "X_train, X_valid_pi, y_train, y_valid = train_test_split(data.drop('subscribed', axis=1), y, train_size=0.8, test_size=0.2,\n",
        "                                                      random_state=0)"
      ],
      "metadata": {
        "id": "uDAG7u7JQQZb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply preprocessing to data\n",
        "X_train_pipeline = preprocessor.fit_transform(X_train)\n",
        "X_valid_pipeline = preprocessor.transform(X_valid)"
      ],
      "metadata": {
        "id": "2Kwi1FhgP3wv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check shape of pipeline training data\n",
        "X_train_pipeline.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4d3njYeEP6zy",
        "outputId": "f6d989ad-521d-4097-e897-09094f70beea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32950, 63)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    }
  ]
}