_Employee_Churn_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "86a63fe61c734f09a3de9c8078043c5c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_a31a922c736a4762ac510941b3be5358",
              "IPY_MODEL_b43c62eb489f418f8cb46f955480c2cb",
              "IPY_MODEL_98861fc7f3bd427ea04f397a0789ef9b"
            ],
            "layout": "IPY_MODEL_0de6bb8d9c444c5b82fc0bcaea89799b"
          }
        },
        "a31a922c736a4762ac510941b3be5358": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f899726ccf974067b945a7490f320ac5",
            "placeholder": "​",
            "style": "IPY_MODEL_9759aec4e03c411f99475a5ec459505b",
            "value": "Processing: 100%"
          }
        },
        "b43c62eb489f418f8cb46f955480c2cb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a3767f5e036942dfb5d8709a819acf19",
            "max": 65,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e5622ce41e2a4fd29893303af2201965",
            "value": 65
          }
        },
        "98861fc7f3bd427ea04f397a0789ef9b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7889f35fa4024cc99ffc7c4bf283c110",
            "placeholder": "​",
            "style": "IPY_MODEL_857dea4dfec749a38a18278be7a16206",
            "value": " 65/65 [01:01&lt;00:00,  1.38it/s]"
          }
        },
        "0de6bb8d9c444c5b82fc0bcaea89799b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "f899726ccf974067b945a7490f320ac5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9759aec4e03c411f99475a5ec459505b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a3767f5e036942dfb5d8709a819acf19": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e5622ce41e2a4fd29893303af2201965": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "7889f35fa4024cc99ffc7c4bf283c110": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "857dea4dfec749a38a18278be7a16206": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0370911d39764e98aabd384febc9597e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_609a8888742b4d23b4cf90defbc2746d",
              "IPY_MODEL_8f8a81588efa4af68ebc96927e395fe0",
              "IPY_MODEL_0e6c63ef57ae4745902757fabc3997f3"
            ],
            "layout": "IPY_MODEL_6913830874a44f02be1b6e9ccef73b26"
          }
        },
        "609a8888742b4d23b4cf90defbc2746d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e8485203d95a4ea38bc794b00aee893c",
            "placeholder": "​",
            "style": "IPY_MODEL_bcc147f96056425ab70955c10f28d329",
            "value": "Processing: 100%"
          }
        },
        "8f8a81588efa4af68ebc96927e395fe0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c259b5be801f48e7b5e35eaf10f87b02",
            "max": 4,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_cd6625c857894e0c9e2ea7cba9ee0546",
            "value": 4
          }
        },
        "0e6c63ef57ae4745902757fabc3997f3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_959d898edf054bbe91b8a776670dfba6",
            "placeholder": "​",
            "style": "IPY_MODEL_e36cd7c6c4ec4302bcf0419208c28ad0",
            "value": " 4/4 [00:11&lt;00:00,  2.71s/it]"
          }
        },
        "6913830874a44f02be1b6e9ccef73b26": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "e8485203d95a4ea38bc794b00aee893c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bcc147f96056425ab70955c10f28d329": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c259b5be801f48e7b5e35eaf10f87b02": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cd6625c857894e0c9e2ea7cba9ee0546": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "959d898edf054bbe91b8a776670dfba6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e36cd7c6c4ec4302bcf0419208c28ad0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Connecting to big query"
      ],
      "metadata": {
        "id": "1jlAOJ6K5hOG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YeP93fnS5RgY"
      },
      "outputs": [],
      "source": [
        "#importing libraries\n",
        "from google.cloud import bigquery\n",
        "from google.colab import auth\n",
        "\n",
        "#authentication\n",
        "auth.authenticate_user()\n",
        "\n",
        "#initialise client for bigquery\n",
        "project_id = 'replace with your project id'\n",
        "client = bigquery.Client(project = project_id, location = 'US')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get the dataset and the tables\n",
        "dataset_ref = client.dataset('employeedata', project = project_id)"
      ],
      "metadata": {
        "id": "I_ePjp5N7Pem"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = client.get_dataset(dataset_ref)"
      ],
      "metadata": {
        "id": "5kSYvVek8CIc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table_ref = dataset.table(\"tbl_hr\")"
      ],
      "metadata": {
        "id": "cmLhl7F-8QfZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table = client.get_table(table_ref)"
      ],
      "metadata": {
        "id": "CZtALX328yGM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table.schema"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ry7wkZT687rA",
        "outputId": "7461d4bb-9893-4aae-eb32-a57eee238a1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SchemaField('satisfaction_level', 'FLOAT', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('last_evaluation', 'FLOAT', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('number_project', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('average_montly_hours', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('time_spend_company', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('Work_accident', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('Quit_the_Company', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('promotion_last_5years', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('Departments', 'STRING', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('salary', 'STRING', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('employee_id', 'STRING', 'NULLABLE', None, None, (), None)]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "table_new = dataset.table(\"til_new_employees\")"
      ],
      "metadata": {
        "id": "PQ2CVH5h9CFG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "New_tb = client.get_table(table_new)"
      ],
      "metadata": {
        "id": "rx06ZVgc9TSb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "New_tb.schema"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wY8yVpjt9hif",
        "outputId": "c66d3489-867c-4ac2-88f9-ad22ad4700d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SchemaField('satisfaction_level', 'FLOAT', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('last_evaluation', 'FLOAT', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('number_project', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('average_montly_hours', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('time_spend_company', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('Work_accident', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('Quit_the_Company', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('promotion_last_5years', 'INTEGER', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('Departments', 'STRING', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('salary', 'STRING', 'NULLABLE', None, None, (), None),\n",
              " SchemaField('employee_id', 'STRING', 'NULLABLE', None, None, (), None)]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting to dataframes\n",
        "df = client.list_rows(table=table).to_dataframe()\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "n9yaYkI39q5S",
        "outputId": "6d2459c0-fbbd-4baf-e23b-d3cd67ce0690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
              "0                0.36             0.56               2                   132   \n",
              "1                0.74             0.99               2                   277   \n",
              "2                0.45             0.53               2                   155   \n",
              "3                0.40             0.53               2                   151   \n",
              "4                0.36             0.51               2                   155   \n",
              "\n",
              "   time_spend_company  Work_accident  Quit_the_Company  promotion_last_5years  \\\n",
              "0                   3              0                 1                      0   \n",
              "1                   3              0                 1                      0   \n",
              "2                   3              0                 1                      0   \n",
              "3                   3              0                 1                      0   \n",
              "4                   3              0                 1                      0   \n",
              "\n",
              "  Departments  salary employee_id  \n",
              "0          IT  medium    EMP00064  \n",
              "1          IT  medium    EMP00071  \n",
              "2          IT     low    EMP00142  \n",
              "3          IT     low    EMP00215  \n",
              "4          IT     low    EMP00216  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-58f3c51c-5221-4950-b3b0-fc9272549be0\" 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>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>Quit_the_Company</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "      <th>Departments</th>\n",
              "      <th>salary</th>\n",
              "      <th>employee_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.56</td>\n",
              "      <td>2</td>\n",
              "      <td>132</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>medium</td>\n",
              "      <td>EMP00064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2</td>\n",
              "      <td>277</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>medium</td>\n",
              "      <td>EMP00071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.45</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2</td>\n",
              "      <td>155</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>EMP00142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2</td>\n",
              "      <td>151</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>EMP00215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.51</td>\n",
              "      <td>2</td>\n",
              "      <td>155</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>EMP00216</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-58f3c51c-5221-4950-b3b0-fc9272549be0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\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-58f3c51c-5221-4950-b3b0-fc9272549be0 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-58f3c51c-5221-4950-b3b0-fc9272549be0');\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-be8c7930-c865-40ac-85d9-5c6d40dcccb7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be8c7930-c865-40ac-85d9-5c6d40dcccb7')\"\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",
              "    }\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-be8c7930-c865-40ac-85d9-5c6d40dcccb7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15004,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24865287659164126,\n        \"min\": 0.09,\n        \"max\": 1.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.27,\n          0.64,\n          0.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17118140618019914,\n        \"min\": 0.36,\n        \"max\": 1.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          1.0,\n          0.62,\n          0.56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2\",\n          \"3\",\n          \"7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 215,\n        \"samples\": [\n          \"291\",\n          \"309\",\n          \"206\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"4\",\n          \"7\",\n          \"3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quit_the_Company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Departments\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"management\",\n          \"hr\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"medium\",\n          \"low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"employee_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15004,\n        \"samples\": [\n          \"EMP03226\",\n          \"EMP00597\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting to dataframes\n",
        "df2 = client.list_rows(table=New_tb).to_dataframe()\n",
        "df2.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "QjRlVspb-SmO",
        "outputId": "04d73b97-2ac2-4333-db3c-1a1fc332f6dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
              "0            0.331690         0.847953               6                   151   \n",
              "1            0.468434         0.169659               5                   303   \n",
              "2            0.858448         0.918311               4                   162   \n",
              "3            0.056211         0.322600               2                   229   \n",
              "4            0.382648         0.434348               4                   260   \n",
              "\n",
              "   time_spend_company  Work_accident  Quit_the_Company  promotion_last_5years  \\\n",
              "0                   4              0                 0                      0   \n",
              "1                   4              0                 0                      0   \n",
              "2                   6              1                 0                      0   \n",
              "3                   5              1                 0                      0   \n",
              "4                   3              1                 0                      0   \n",
              "\n",
              "  Departments salary employee_id  \n",
              "0          IT    low     NE15014  \n",
              "1          IT    low     NE15015  \n",
              "2          IT    low     NE15064  \n",
              "3          IT    low     NE15086  \n",
              "4          hr    low     NE15021  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10f6da6e-433b-4752-9506-e9f3cfe0b52f\" 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>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>Quit_the_Company</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "      <th>Departments</th>\n",
              "      <th>salary</th>\n",
              "      <th>employee_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.331690</td>\n",
              "      <td>0.847953</td>\n",
              "      <td>6</td>\n",
              "      <td>151</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.468434</td>\n",
              "      <td>0.169659</td>\n",
              "      <td>5</td>\n",
              "      <td>303</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.858448</td>\n",
              "      <td>0.918311</td>\n",
              "      <td>4</td>\n",
              "      <td>162</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.056211</td>\n",
              "      <td>0.322600</td>\n",
              "      <td>2</td>\n",
              "      <td>229</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.382648</td>\n",
              "      <td>0.434348</td>\n",
              "      <td>4</td>\n",
              "      <td>260</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>hr</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15021</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-10f6da6e-433b-4752-9506-e9f3cfe0b52f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\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-10f6da6e-433b-4752-9506-e9f3cfe0b52f 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-10f6da6e-433b-4752-9506-e9f3cfe0b52f');\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-e94fd2a9-cd33-427a-9588-59a81c890c66\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e94fd2a9-cd33-427a-9588-59a81c890c66')\"\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",
              "    }\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-e94fd2a9-cd33-427a-9588-59a81c890c66 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2721197555873272,\n        \"min\": 0.028847924,\n        \"max\": 0.998786609,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.772948978,\n          0.153666741,\n          0.974857752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30039121518901296,\n        \"min\": 0.009858842,\n        \"max\": 0.998393833,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.800485463,\n          0.07138621,\n          0.103933433\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"6\",\n          \"5\",\n          \"3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"260\",\n          \"187\",\n          \"240\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"6\",\n          \"10\",\n          \"4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quit_the_Company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Departments\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"product_mng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"employee_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"NE15061\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building Model"
      ],
      "metadata": {
        "id": "eHag8sn2AZs1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pycaret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1rM9SZn-znh",
        "outputId": "fb886057-5113-47a2-a6eb-f20bf5df25e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pycaret\n",
            "  Downloading pycaret-3.3.0-py3-none-any.whl (485 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/485.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.6/485.9 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.9/485.9 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.34.0)\n",
            "Requirement already satisfied: ipywidgets>=7.6.5 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.7.1)\n",
            "Requirement already satisfied: tqdm>=4.62.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (4.66.2)\n",
            "Requirement already satisfied: numpy<1.27,>=1.21 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.25.2)\n",
            "Requirement already satisfied: pandas<2.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.5.3)\n",
            "Requirement already satisfied: jinja2>=3 in /usr/local/lib/python3.10/dist-packages (from pycaret) (3.1.3)\n",
            "Requirement already satisfied: scipy<=1.11.4,>=1.6.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.3.2)\n",
            "Collecting scikit-learn>1.4.0 (from pycaret)\n",
            "  Downloading scikit_learn-1.4.1.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.1/12.1 MB\u001b[0m \u001b[31m96.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pyod>=1.1.3 (from pycaret)\n",
            "  Downloading pyod-1.1.3.tar.gz (160 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m160.5/160.5 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting imbalanced-learn>=0.12.0 (from pycaret)\n",
            "  Downloading imbalanced_learn-0.12.0-py3-none-any.whl (257 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m257.7/257.7 kB\u001b[0m \u001b[31m29.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting category-encoders>=2.4.0 (from pycaret)\n",
            "  Downloading category_encoders-2.6.3-py2.py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.9/81.9 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: lightgbm>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (4.1.0)\n",
            "Requirement already satisfied: numba>=0.55.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.58.1)\n",
            "Requirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.31.0)\n",
            "Requirement already satisfied: psutil>=5.9.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (5.9.5)\n",
            "Requirement already satisfied: markupsafe>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.1.5)\n",
            "Requirement already satisfied: importlib-metadata>=4.12.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.0.1)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (5.9.2)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.2.1)\n",
            "Collecting deprecation>=2.1.0 (from pycaret)\n",
            "  Downloading deprecation-2.1.0-py2.py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from pycaret) (3.4.1)\n",
            "Requirement already satisfied: matplotlib<3.8.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (3.7.1)\n",
            "Collecting scikit-plot>=0.3.7 (from pycaret)\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: yellowbrick>=1.4 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.5)\n",
            "Requirement already satisfied: plotly>=5.14.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (5.15.0)\n",
            "Collecting kaleido>=0.2.1 (from pycaret)\n",
            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting schemdraw==0.15 (from pycaret)\n",
            "  Downloading schemdraw-0.15-py3-none-any.whl (106 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.8/106.8 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting plotly-resampler>=0.8.3.1 (from pycaret)\n",
            "  Downloading plotly_resampler-0.9.2-py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.1/81.1 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: statsmodels>=0.12.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.14.1)\n",
            "Collecting sktime>=0.26.0 (from pycaret)\n",
            "  Downloading sktime-0.27.0-py3-none-any.whl (21.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.9/21.9 MB\u001b[0m \u001b[31m78.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tbats>=1.1.3 (from pycaret)\n",
            "  Downloading tbats-1.1.3-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.0/44.0 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pmdarima>=2.0.4 (from pycaret)\n",
            "  Downloading pmdarima-2.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m87.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting wurlitzer (from pycaret)\n",
            "  Downloading wurlitzer-3.0.3-py3-none-any.whl (7.3 kB)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category-encoders>=2.4.0->pycaret) (0.5.6)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from deprecation>=2.1.0->pycaret) (23.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn>=0.12.0->pycaret) (3.3.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=4.12.0->pycaret) (3.17.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (67.7.2)\n",
            "Collecting jedi>=0.16 (from ipython>=5.5.0->pycaret)\n",
            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m71.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (3.0.43)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (2.16.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret) (4.9.0)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.5->pycaret) (5.5.6)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.5->pycaret) (0.2.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.5->pycaret) (3.6.6)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.5->pycaret) (3.0.10)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.8.0->pycaret) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.8.0->pycaret) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.8.0->pycaret) (4.49.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.8.0->pycaret) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.8.0->pycaret) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.8.0->pycaret) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.8.0->pycaret) (2.8.2)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=4.2.0->pycaret) (2.19.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=4.2.0->pycaret) (4.19.2)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.10/dist-packages (from nbformat>=4.2.0->pycaret) (5.7.1)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.55.0->pycaret) (0.41.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2.2.0->pycaret) (2023.4)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=5.14.0->pycaret) (8.2.3)\n",
            "Collecting dash>=2.9.0 (from plotly-resampler>=0.8.3.1->pycaret)\n",
            "  Downloading dash-2.15.0-py3-none-any.whl (10.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.2/10.2 MB\u001b[0m \u001b[31m106.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting orjson<4.0.0,>=3.8.0 (from plotly-resampler>=0.8.3.1->pycaret)\n",
            "  Downloading orjson-3.9.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (138 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.5/138.5 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tsdownsample==0.1.2 (from plotly-resampler>=0.8.3.1->pycaret)\n",
            "  Downloading tsdownsample-0.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m78.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.10/dist-packages (from pmdarima>=2.0.4->pycaret) (3.0.8)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pmdarima>=2.0.4->pycaret) (2.0.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from pyod>=1.1.3->pycaret) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->pycaret) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->pycaret) (3.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->pycaret) (2024.2.2)\n",
            "Collecting scikit-base<0.8.0 (from sktime>=0.26.0->pycaret)\n",
            "  Downloading scikit_base-0.7.2-py3-none-any.whl (127 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.2/127.2 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: Flask<3.1,>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (2.2.5)\n",
            "Requirement already satisfied: Werkzeug<3.1 in /usr/local/lib/python3.10/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (3.0.1)\n",
            "Collecting dash-html-components==2.0.0 (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret)\n",
            "  Downloading dash_html_components-2.0.0-py3-none-any.whl (4.1 kB)\n",
            "Collecting dash-core-components==2.0.0 (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret)\n",
            "  Downloading dash_core_components-2.0.0-py3-none-any.whl (3.8 kB)\n",
            "Collecting dash-table==5.0.0 (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret)\n",
            "  Downloading dash_table-5.0.0-py3-none-any.whl (3.9 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.1.1 in /usr/local/lib/python3.10/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (4.9.0)\n",
            "Collecting retrying (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret)\n",
            "  Downloading retrying-1.3.4-py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.10/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (1.6.0)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.5->pycaret) (6.1.12)\n",
            "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.5->pycaret) (6.3.2)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=5.5.0->pycaret) (0.8.3)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (23.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (0.33.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (0.18.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=5.5.0->pycaret) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.5.0->pycaret) (0.2.13)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.10/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.5.5)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core->nbformat>=4.2.0->pycaret) (4.2.0)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask<3.1,>=1.0.4->dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (2.1.2)\n",
            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from Flask<3.1,>=1.0.4->dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (8.1.7)\n",
            "Requirement already satisfied: pyzmq<25,>=17 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (23.2.1)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (23.1.0)\n",
            "Requirement already satisfied: nbconvert>=5 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.5.4)\n",
            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.8.2)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.18.0)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.20.0)\n",
            "Requirement already satisfied: nbclassic>=0.4.7 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.0.0)\n",
            "Requirement already satisfied: jupyter-server>=1.8 in /usr/local/lib/python3.10/dist-packages (from nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.24.0)\n",
            "Requirement already satisfied: notebook-shim>=0.2.3 in /usr/local/lib/python3.10/dist-packages (from nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.2.4)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.9.4)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.12.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.1.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.7.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.4)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.3.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.8.4)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.9.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.5.1)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.1)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.10/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (21.2.0)\n",
            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (3.7.1)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.7.0)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.16.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.5)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.5.1)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.21)\n",
            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-1.1.3-py3-none-any.whl size=190251 sha256=781952087f6deb85a69cb69b2360c36700062a2190e782b209e0a15e14dce4cc\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/f8/db/124d43bec122d6ec0ab3713fadfe25ebed8af52ec561682b4e\n",
            "Successfully built pyod\n",
            "Installing collected packages: kaleido, dash-table, dash-html-components, dash-core-components, wurlitzer, tsdownsample, scikit-base, schemdraw, retrying, orjson, jedi, deprecation, scikit-learn, sktime, scikit-plot, pyod, imbalanced-learn, dash, pmdarima, plotly-resampler, category-encoders, tbats, pycaret\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: imbalanced-learn\n",
            "    Found existing installation: imbalanced-learn 0.10.1\n",
            "    Uninstalling imbalanced-learn-0.10.1:\n",
            "      Successfully uninstalled imbalanced-learn-0.10.1\n",
            "Successfully installed category-encoders-2.6.3 dash-2.15.0 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecation-2.1.0 imbalanced-learn-0.12.0 jedi-0.19.1 kaleido-0.2.1 orjson-3.9.15 plotly-resampler-0.9.2 pmdarima-2.0.4 pycaret-3.3.0 pyod-1.1.3 retrying-1.3.4 schemdraw-0.15 scikit-base-0.7.2 scikit-learn-1.4.1.post1 scikit-plot-0.3.7 sktime-0.27.0 tbats-1.1.3 tsdownsample-0.1.2 wurlitzer-3.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#training model\n",
        "from pycaret.classification import *"
      ],
      "metadata": {
        "id": "_xr7PpTdAlwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAk18u9MCkDl",
        "outputId": "9d43bd0b-dfe4-4857-9e38-00cc75064f49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15004 entries, 0 to 15003\n",
            "Data columns (total 11 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   satisfaction_level     15004 non-null  float64\n",
            " 1   last_evaluation        15004 non-null  float64\n",
            " 2   number_project         14999 non-null  Int64  \n",
            " 3   average_montly_hours   15004 non-null  Int64  \n",
            " 4   time_spend_company     14999 non-null  Int64  \n",
            " 5   Work_accident          15000 non-null  Int64  \n",
            " 6   Quit_the_Company       15004 non-null  Int64  \n",
            " 7   promotion_last_5years  15004 non-null  Int64  \n",
            " 8   Departments            15004 non-null  object \n",
            " 9   salary                 15004 non-null  object \n",
            " 10  employee_id            15004 non-null  object \n",
            "dtypes: Int64(6), float64(2), object(3)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#setting up model\n",
        "setup(df, target = 'Quit_the_Company',\n",
        "      session_id = 123,\n",
        "      ignore_features=['employee_id'],\n",
        "      categorical_features=['salary','Departments'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0iYEEpjhBsqY",
        "outputId": "33c4014b-d4e5-4c22-c2c7-19ff26411d7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d00cb3f1a20>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_1f1ba_row11_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_1f1ba\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_1f1ba_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_1f1ba_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_1f1ba_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_1f1ba_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_1f1ba_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_1f1ba_row1_col1\" class=\"data row1 col1\" >Quit_the_Company</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_1f1ba_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_1f1ba_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_1f1ba_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_1f1ba_row3_col1\" class=\"data row3 col1\" >(15004, 11)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_1f1ba_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_1f1ba_row4_col1\" class=\"data row4 col1\" >(15004, 21)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_1f1ba_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_1f1ba_row5_col1\" class=\"data row5 col1\" >(10502, 21)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_1f1ba_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_1f1ba_row6_col1\" class=\"data row6 col1\" >(4502, 21)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_1f1ba_row7_col0\" class=\"data row7 col0\" >Ignore features</td>\n",
              "      <td id=\"T_1f1ba_row7_col1\" class=\"data row7 col1\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_1f1ba_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
              "      <td id=\"T_1f1ba_row8_col1\" class=\"data row8 col1\" >7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_1f1ba_row9_col0\" class=\"data row9 col0\" >Categorical features</td>\n",
              "      <td id=\"T_1f1ba_row9_col1\" class=\"data row9 col1\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_1f1ba_row10_col0\" class=\"data row10 col0\" >Rows with missing values</td>\n",
              "      <td id=\"T_1f1ba_row10_col1\" class=\"data row10 col1\" >0.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_1f1ba_row11_col0\" class=\"data row11 col0\" >Preprocess</td>\n",
              "      <td id=\"T_1f1ba_row11_col1\" class=\"data row11 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_1f1ba_row12_col0\" class=\"data row12 col0\" >Imputation type</td>\n",
              "      <td id=\"T_1f1ba_row12_col1\" class=\"data row12 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_1f1ba_row13_col0\" class=\"data row13 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_1f1ba_row13_col1\" class=\"data row13 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_1f1ba_row14_col0\" class=\"data row14 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_1f1ba_row14_col1\" class=\"data row14 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_1f1ba_row15_col0\" class=\"data row15 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_1f1ba_row15_col1\" class=\"data row15 col1\" >25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_1f1ba_row16_col0\" class=\"data row16 col0\" >Encoding method</td>\n",
              "      <td id=\"T_1f1ba_row16_col1\" class=\"data row16 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_1f1ba_row17_col0\" class=\"data row17 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_1f1ba_row17_col1\" class=\"data row17 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_1f1ba_row18_col0\" class=\"data row18 col0\" >Fold Number</td>\n",
              "      <td id=\"T_1f1ba_row18_col1\" class=\"data row18 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_1f1ba_row19_col0\" class=\"data row19 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_1f1ba_row19_col1\" class=\"data row19 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_1f1ba_row20_col0\" class=\"data row20 col0\" >Use GPU</td>\n",
              "      <td id=\"T_1f1ba_row20_col1\" class=\"data row20 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_1f1ba_row21_col0\" class=\"data row21 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_1f1ba_row21_col1\" class=\"data row21 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_1f1ba_row22_col0\" class=\"data row22 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_1f1ba_row22_col1\" class=\"data row22 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1f1ba_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "      <td id=\"T_1f1ba_row23_col0\" class=\"data row23 col0\" >USI</td>\n",
              "      <td id=\"T_1f1ba_row23_col1\" class=\"data row23 col1\" >a4dc</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pycaret.classification.oop.ClassificationExperiment at 0x7d00e57b4520>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896,
          "referenced_widgets": [
            "86a63fe61c734f09a3de9c8078043c5c",
            "a31a922c736a4762ac510941b3be5358",
            "b43c62eb489f418f8cb46f955480c2cb",
            "98861fc7f3bd427ea04f397a0789ef9b",
            "0de6bb8d9c444c5b82fc0bcaea89799b",
            "f899726ccf974067b945a7490f320ac5",
            "9759aec4e03c411f99475a5ec459505b",
            "a3767f5e036942dfb5d8709a819acf19",
            "e5622ce41e2a4fd29893303af2201965",
            "7889f35fa4024cc99ffc7c4bf283c110",
            "857dea4dfec749a38a18278be7a16206"
          ]
        },
        "id": "U_dELHGNDVQM",
        "outputId": "6761368e-b303-470f-b160-7214978bb787"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d00e5333c10>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_9a180 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_9a180_row0_col0, #T_9a180_row1_col0, #T_9a180_row1_col1, #T_9a180_row1_col3, #T_9a180_row1_col4, #T_9a180_row1_col5, #T_9a180_row1_col6, #T_9a180_row1_col7, #T_9a180_row2_col0, #T_9a180_row2_col1, #T_9a180_row2_col4, #T_9a180_row2_col5, #T_9a180_row2_col6, #T_9a180_row2_col7, #T_9a180_row3_col0, #T_9a180_row3_col1, #T_9a180_row3_col3, #T_9a180_row3_col4, #T_9a180_row3_col5, #T_9a180_row3_col6, #T_9a180_row3_col7, #T_9a180_row4_col0, #T_9a180_row4_col1, #T_9a180_row4_col3, #T_9a180_row4_col4, #T_9a180_row4_col5, #T_9a180_row4_col6, #T_9a180_row4_col7, #T_9a180_row5_col0, #T_9a180_row5_col1, #T_9a180_row5_col3, #T_9a180_row5_col4, #T_9a180_row5_col5, #T_9a180_row5_col6, #T_9a180_row5_col7, #T_9a180_row6_col0, #T_9a180_row6_col1, #T_9a180_row6_col3, #T_9a180_row6_col4, #T_9a180_row6_col5, #T_9a180_row6_col6, #T_9a180_row6_col7, #T_9a180_row7_col0, #T_9a180_row7_col1, #T_9a180_row7_col3, #T_9a180_row7_col4, #T_9a180_row7_col5, #T_9a180_row7_col6, #T_9a180_row7_col7, #T_9a180_row8_col0, #T_9a180_row8_col1, #T_9a180_row8_col3, #T_9a180_row8_col4, #T_9a180_row8_col5, #T_9a180_row8_col6, #T_9a180_row8_col7, #T_9a180_row9_col0, #T_9a180_row9_col1, #T_9a180_row9_col3, #T_9a180_row9_col4, #T_9a180_row9_col5, #T_9a180_row9_col6, #T_9a180_row9_col7, #T_9a180_row10_col0, #T_9a180_row10_col1, #T_9a180_row10_col3, #T_9a180_row10_col4, #T_9a180_row10_col5, #T_9a180_row10_col6, #T_9a180_row10_col7, #T_9a180_row11_col0, #T_9a180_row11_col1, #T_9a180_row11_col3, #T_9a180_row11_col4, #T_9a180_row11_col5, #T_9a180_row11_col6, #T_9a180_row11_col7, #T_9a180_row12_col0, #T_9a180_row12_col1, #T_9a180_row12_col3, #T_9a180_row12_col4, #T_9a180_row12_col5, #T_9a180_row12_col6, #T_9a180_row12_col7, #T_9a180_row13_col0, #T_9a180_row13_col1, #T_9a180_row13_col3, #T_9a180_row13_col4, #T_9a180_row13_col5, #T_9a180_row13_col6, #T_9a180_row13_col7, #T_9a180_row14_col0, #T_9a180_row14_col1, #T_9a180_row14_col3, #T_9a180_row14_col4, #T_9a180_row14_col5, #T_9a180_row14_col6, #T_9a180_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_9a180_row0_col1, #T_9a180_row0_col2, #T_9a180_row0_col3, #T_9a180_row0_col4, #T_9a180_row0_col5, #T_9a180_row0_col6, #T_9a180_row0_col7, #T_9a180_row1_col2, #T_9a180_row2_col2, #T_9a180_row2_col3, #T_9a180_row3_col2, #T_9a180_row4_col2, #T_9a180_row5_col2, #T_9a180_row6_col2, #T_9a180_row7_col2, #T_9a180_row8_col2, #T_9a180_row9_col2, #T_9a180_row10_col2, #T_9a180_row11_col2, #T_9a180_row12_col2, #T_9a180_row13_col2, #T_9a180_row14_col2 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_9a180_row0_col8, #T_9a180_row1_col8, #T_9a180_row2_col8, #T_9a180_row3_col8, #T_9a180_row4_col8, #T_9a180_row5_col8, #T_9a180_row6_col8, #T_9a180_row7_col8, #T_9a180_row8_col8, #T_9a180_row9_col8, #T_9a180_row10_col8, #T_9a180_row11_col8, #T_9a180_row13_col8, #T_9a180_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_9a180_row12_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_9a180\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_9a180_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_9a180_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_9a180_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_9a180_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_9a180_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_9a180_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_9a180_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_9a180_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_9a180_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row0\" class=\"row_heading level0 row0\" >rf</th>\n",
              "      <td id=\"T_9a180_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_9a180_row0_col1\" class=\"data row0 col1\" >0.9886</td>\n",
              "      <td id=\"T_9a180_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row0_col3\" class=\"data row0 col3\" >0.9584</td>\n",
              "      <td id=\"T_9a180_row0_col4\" class=\"data row0 col4\" >0.9934</td>\n",
              "      <td id=\"T_9a180_row0_col5\" class=\"data row0 col5\" >0.9756</td>\n",
              "      <td id=\"T_9a180_row0_col6\" class=\"data row0 col6\" >0.9681</td>\n",
              "      <td id=\"T_9a180_row0_col7\" class=\"data row0 col7\" >0.9684</td>\n",
              "      <td id=\"T_9a180_row0_col8\" class=\"data row0 col8\" >0.5930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
              "      <td id=\"T_9a180_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_9a180_row1_col1\" class=\"data row1 col1\" >0.9856</td>\n",
              "      <td id=\"T_9a180_row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row1_col3\" class=\"data row1 col3\" >0.9536</td>\n",
              "      <td id=\"T_9a180_row1_col4\" class=\"data row1 col4\" >0.9856</td>\n",
              "      <td id=\"T_9a180_row1_col5\" class=\"data row1 col5\" >0.9693</td>\n",
              "      <td id=\"T_9a180_row1_col6\" class=\"data row1 col6\" >0.9599</td>\n",
              "      <td id=\"T_9a180_row1_col7\" class=\"data row1 col7\" >0.9602</td>\n",
              "      <td id=\"T_9a180_row1_col8\" class=\"data row1 col8\" >0.9500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row2\" class=\"row_heading level0 row2\" >xgboost</th>\n",
              "      <td id=\"T_9a180_row2_col0\" class=\"data row2 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_9a180_row2_col1\" class=\"data row2 col1\" >0.9854</td>\n",
              "      <td id=\"T_9a180_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row2_col3\" class=\"data row2 col3\" >0.9584</td>\n",
              "      <td id=\"T_9a180_row2_col4\" class=\"data row2 col4\" >0.9801</td>\n",
              "      <td id=\"T_9a180_row2_col5\" class=\"data row2 col5\" >0.9691</td>\n",
              "      <td id=\"T_9a180_row2_col6\" class=\"data row2 col6\" >0.9596</td>\n",
              "      <td id=\"T_9a180_row2_col7\" class=\"data row2 col7\" >0.9597</td>\n",
              "      <td id=\"T_9a180_row2_col8\" class=\"data row2 col8\" >0.1890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row3\" class=\"row_heading level0 row3\" >et</th>\n",
              "      <td id=\"T_9a180_row3_col0\" class=\"data row3 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_9a180_row3_col1\" class=\"data row3 col1\" >0.9832</td>\n",
              "      <td id=\"T_9a180_row3_col2\" class=\"data row3 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row3_col3\" class=\"data row3 col3\" >0.9461</td>\n",
              "      <td id=\"T_9a180_row3_col4\" class=\"data row3 col4\" >0.9831</td>\n",
              "      <td id=\"T_9a180_row3_col5\" class=\"data row3 col5\" >0.9641</td>\n",
              "      <td id=\"T_9a180_row3_col6\" class=\"data row3 col6\" >0.9532</td>\n",
              "      <td id=\"T_9a180_row3_col7\" class=\"data row3 col7\" >0.9536</td>\n",
              "      <td id=\"T_9a180_row3_col8\" class=\"data row3 col8\" >0.5910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row4\" class=\"row_heading level0 row4\" >gbc</th>\n",
              "      <td id=\"T_9a180_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_9a180_row4_col1\" class=\"data row4 col1\" >0.9766</td>\n",
              "      <td id=\"T_9a180_row4_col2\" class=\"data row4 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row4_col3\" class=\"data row4 col3\" >0.9313</td>\n",
              "      <td id=\"T_9a180_row4_col4\" class=\"data row4 col4\" >0.9694</td>\n",
              "      <td id=\"T_9a180_row4_col5\" class=\"data row4 col5\" >0.9499</td>\n",
              "      <td id=\"T_9a180_row4_col6\" class=\"data row4 col6\" >0.9346</td>\n",
              "      <td id=\"T_9a180_row4_col7\" class=\"data row4 col7\" >0.9350</td>\n",
              "      <td id=\"T_9a180_row4_col8\" class=\"data row4 col8\" >0.7070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
              "      <td id=\"T_9a180_row5_col0\" class=\"data row5 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_9a180_row5_col1\" class=\"data row5 col1\" >0.9747</td>\n",
              "      <td id=\"T_9a180_row5_col2\" class=\"data row5 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row5_col3\" class=\"data row5 col3\" >0.9560</td>\n",
              "      <td id=\"T_9a180_row5_col4\" class=\"data row5 col4\" >0.9392</td>\n",
              "      <td id=\"T_9a180_row5_col5\" class=\"data row5 col5\" >0.9474</td>\n",
              "      <td id=\"T_9a180_row5_col6\" class=\"data row5 col6\" >0.9307</td>\n",
              "      <td id=\"T_9a180_row5_col7\" class=\"data row5 col7\" >0.9309</td>\n",
              "      <td id=\"T_9a180_row5_col8\" class=\"data row5 col8\" >0.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
              "      <td id=\"T_9a180_row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_9a180_row6_col1\" class=\"data row6 col1\" >0.9572</td>\n",
              "      <td id=\"T_9a180_row6_col2\" class=\"data row6 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row6_col3\" class=\"data row6 col3\" >0.9061</td>\n",
              "      <td id=\"T_9a180_row6_col4\" class=\"data row6 col4\" >0.9138</td>\n",
              "      <td id=\"T_9a180_row6_col5\" class=\"data row6 col5\" >0.9097</td>\n",
              "      <td id=\"T_9a180_row6_col6\" class=\"data row6 col6\" >0.8816</td>\n",
              "      <td id=\"T_9a180_row6_col7\" class=\"data row6 col7\" >0.8818</td>\n",
              "      <td id=\"T_9a180_row6_col8\" class=\"data row6 col8\" >0.3970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row7\" class=\"row_heading level0 row7\" >knn</th>\n",
              "      <td id=\"T_9a180_row7_col0\" class=\"data row7 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_9a180_row7_col1\" class=\"data row7 col1\" >0.9333</td>\n",
              "      <td id=\"T_9a180_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row7_col3\" class=\"data row7 col3\" >0.9185</td>\n",
              "      <td id=\"T_9a180_row7_col4\" class=\"data row7 col4\" >0.8230</td>\n",
              "      <td id=\"T_9a180_row7_col5\" class=\"data row7 col5\" >0.8680</td>\n",
              "      <td id=\"T_9a180_row7_col6\" class=\"data row7 col6\" >0.8236</td>\n",
              "      <td id=\"T_9a180_row7_col7\" class=\"data row7 col7\" >0.8259</td>\n",
              "      <td id=\"T_9a180_row7_col8\" class=\"data row7 col8\" >0.1910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row8\" class=\"row_heading level0 row8\" >qda</th>\n",
              "      <td id=\"T_9a180_row8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_9a180_row8_col1\" class=\"data row8 col1\" >0.8583</td>\n",
              "      <td id=\"T_9a180_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row8_col3\" class=\"data row8 col3\" >0.8337</td>\n",
              "      <td id=\"T_9a180_row8_col4\" class=\"data row8 col4\" >0.6671</td>\n",
              "      <td id=\"T_9a180_row8_col5\" class=\"data row8 col5\" >0.7387</td>\n",
              "      <td id=\"T_9a180_row8_col6\" class=\"data row8 col6\" >0.6437</td>\n",
              "      <td id=\"T_9a180_row8_col7\" class=\"data row8 col7\" >0.6534</td>\n",
              "      <td id=\"T_9a180_row8_col8\" class=\"data row8 col8\" >0.1020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row9\" class=\"row_heading level0 row9\" >lr</th>\n",
              "      <td id=\"T_9a180_row9_col0\" class=\"data row9 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_9a180_row9_col1\" class=\"data row9 col1\" >0.7844</td>\n",
              "      <td id=\"T_9a180_row9_col2\" class=\"data row9 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row9_col3\" class=\"data row9 col3\" >0.3244</td>\n",
              "      <td id=\"T_9a180_row9_col4\" class=\"data row9 col4\" >0.5873</td>\n",
              "      <td id=\"T_9a180_row9_col5\" class=\"data row9 col5\" >0.4174</td>\n",
              "      <td id=\"T_9a180_row9_col6\" class=\"data row9 col6\" >0.2985</td>\n",
              "      <td id=\"T_9a180_row9_col7\" class=\"data row9 col7\" >0.3185</td>\n",
              "      <td id=\"T_9a180_row9_col8\" class=\"data row9 col8\" >0.9360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
              "      <td id=\"T_9a180_row10_col0\" class=\"data row10 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_9a180_row10_col1\" class=\"data row10 col1\" >0.7752</td>\n",
              "      <td id=\"T_9a180_row10_col2\" class=\"data row10 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row10_col3\" class=\"data row10 col3\" >0.2417</td>\n",
              "      <td id=\"T_9a180_row10_col4\" class=\"data row10 col4\" >0.5684</td>\n",
              "      <td id=\"T_9a180_row10_col5\" class=\"data row10 col5\" >0.3386</td>\n",
              "      <td id=\"T_9a180_row10_col6\" class=\"data row10 col6\" >0.2288</td>\n",
              "      <td id=\"T_9a180_row10_col7\" class=\"data row10 col7\" >0.2596</td>\n",
              "      <td id=\"T_9a180_row10_col8\" class=\"data row10 col8\" >0.1740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row11\" class=\"row_heading level0 row11\" >lda</th>\n",
              "      <td id=\"T_9a180_row11_col0\" class=\"data row11 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_9a180_row11_col1\" class=\"data row11 col1\" >0.7719</td>\n",
              "      <td id=\"T_9a180_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row11_col3\" class=\"data row11 col3\" >0.2952</td>\n",
              "      <td id=\"T_9a180_row11_col4\" class=\"data row11 col4\" >0.5399</td>\n",
              "      <td id=\"T_9a180_row11_col5\" class=\"data row11 col5\" >0.3807</td>\n",
              "      <td id=\"T_9a180_row11_col6\" class=\"data row11 col6\" >0.2558</td>\n",
              "      <td id=\"T_9a180_row11_col7\" class=\"data row11 col7\" >0.2736</td>\n",
              "      <td id=\"T_9a180_row11_col8\" class=\"data row11 col8\" >0.1110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row12\" class=\"row_heading level0 row12\" >dummy</th>\n",
              "      <td id=\"T_9a180_row12_col0\" class=\"data row12 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_9a180_row12_col1\" class=\"data row12 col1\" >0.7617</td>\n",
              "      <td id=\"T_9a180_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row12_col3\" class=\"data row12 col3\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row12_col4\" class=\"data row12 col4\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row12_col5\" class=\"data row12 col5\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row12_col6\" class=\"data row12 col6\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row12_col7\" class=\"data row12 col7\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row12_col8\" class=\"data row12 col8\" >0.0990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row13\" class=\"row_heading level0 row13\" >svm</th>\n",
              "      <td id=\"T_9a180_row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_9a180_row13_col1\" class=\"data row13 col1\" >0.6888</td>\n",
              "      <td id=\"T_9a180_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row13_col3\" class=\"data row13 col3\" >0.2681</td>\n",
              "      <td id=\"T_9a180_row13_col4\" class=\"data row13 col4\" >0.2022</td>\n",
              "      <td id=\"T_9a180_row13_col5\" class=\"data row13 col5\" >0.1770</td>\n",
              "      <td id=\"T_9a180_row13_col6\" class=\"data row13 col6\" >0.0763</td>\n",
              "      <td id=\"T_9a180_row13_col7\" class=\"data row13 col7\" >0.0907</td>\n",
              "      <td id=\"T_9a180_row13_col8\" class=\"data row13 col8\" >0.2530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a180_level0_row14\" class=\"row_heading level0 row14\" >nb</th>\n",
              "      <td id=\"T_9a180_row14_col0\" class=\"data row14 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_9a180_row14_col1\" class=\"data row14 col1\" >0.6691</td>\n",
              "      <td id=\"T_9a180_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_9a180_row14_col3\" class=\"data row14 col3\" >0.8134</td>\n",
              "      <td id=\"T_9a180_row14_col4\" class=\"data row14 col4\" >0.4040</td>\n",
              "      <td id=\"T_9a180_row14_col5\" class=\"data row14 col5\" >0.5397</td>\n",
              "      <td id=\"T_9a180_row14_col6\" class=\"data row14 col6\" >0.3245</td>\n",
              "      <td id=\"T_9a180_row14_col7\" class=\"data row14 col7\" >0.3732</td>\n",
              "      <td id=\"T_9a180_row14_col8\" class=\"data row14 col8\" >0.1060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86a63fe61c734f09a3de9c8078043c5c"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='sqrt',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_samples_leaf=1,\n",
              "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                       monotonic_cst=None, n_estimators=100, n_jobs=-1,\n",
              "                       oob_score=False, random_state=123, verbose=0,\n",
              "                       warm_start=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion=&#x27;gini&#x27;, max_depth=None, max_features=&#x27;sqrt&#x27;,\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_samples_leaf=1,\n",
              "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                       monotonic_cst=None, n_estimators=100, n_jobs=-1,\n",
              "                       oob_score=False, random_state=123, verbose=0,\n",
              "                       warm_start=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion=&#x27;gini&#x27;, max_depth=None, max_features=&#x27;sqrt&#x27;,\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_samples_leaf=1,\n",
              "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                       monotonic_cst=None, n_estimators=100, n_jobs=-1,\n",
              "                       oob_score=False, random_state=123, verbose=0,\n",
              "                       warm_start=False)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#choosing random forest since it's the best in terms of performance\n",
        "rf_model = create_model('rf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580,
          "referenced_widgets": [
            "0370911d39764e98aabd384febc9597e",
            "609a8888742b4d23b4cf90defbc2746d",
            "8f8a81588efa4af68ebc96927e395fe0",
            "0e6c63ef57ae4745902757fabc3997f3",
            "6913830874a44f02be1b6e9ccef73b26",
            "e8485203d95a4ea38bc794b00aee893c",
            "bcc147f96056425ab70955c10f28d329",
            "c259b5be801f48e7b5e35eaf10f87b02",
            "cd6625c857894e0c9e2ea7cba9ee0546",
            "959d898edf054bbe91b8a776670dfba6",
            "e36cd7c6c4ec4302bcf0419208c28ad0"
          ]
        },
        "id": "TjpIrD1JEBE8",
        "outputId": "e2c7cc4d-ae91-4b96-dec1-84a08777d346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d00e57b5f00>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_0afe3_row10_col0, #T_0afe3_row10_col1, #T_0afe3_row10_col2, #T_0afe3_row10_col3, #T_0afe3_row10_col4, #T_0afe3_row10_col5, #T_0afe3_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_0afe3\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_0afe3_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_0afe3_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_0afe3_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_0afe3_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_0afe3_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_0afe3_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_0afe3_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_0afe3_row0_col0\" class=\"data row0 col0\" >0.9914</td>\n",
              "      <td id=\"T_0afe3_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row0_col2\" class=\"data row0 col2\" >0.9681</td>\n",
              "      <td id=\"T_0afe3_row0_col3\" class=\"data row0 col3\" >0.9959</td>\n",
              "      <td id=\"T_0afe3_row0_col4\" class=\"data row0 col4\" >0.9818</td>\n",
              "      <td id=\"T_0afe3_row0_col5\" class=\"data row0 col5\" >0.9762</td>\n",
              "      <td id=\"T_0afe3_row0_col6\" class=\"data row0 col6\" >0.9764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_0afe3_row1_col0\" class=\"data row1 col0\" >0.9933</td>\n",
              "      <td id=\"T_0afe3_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row1_col2\" class=\"data row1 col2\" >0.9721</td>\n",
              "      <td id=\"T_0afe3_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_0afe3_row1_col4\" class=\"data row1 col4\" >0.9859</td>\n",
              "      <td id=\"T_0afe3_row1_col5\" class=\"data row1 col5\" >0.9815</td>\n",
              "      <td id=\"T_0afe3_row1_col6\" class=\"data row1 col6\" >0.9817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_0afe3_row2_col0\" class=\"data row2 col0\" >0.9895</td>\n",
              "      <td id=\"T_0afe3_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row2_col2\" class=\"data row2 col2\" >0.9680</td>\n",
              "      <td id=\"T_0afe3_row2_col3\" class=\"data row2 col3\" >0.9878</td>\n",
              "      <td id=\"T_0afe3_row2_col4\" class=\"data row2 col4\" >0.9778</td>\n",
              "      <td id=\"T_0afe3_row2_col5\" class=\"data row2 col5\" >0.9709</td>\n",
              "      <td id=\"T_0afe3_row2_col6\" class=\"data row2 col6\" >0.9710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_0afe3_row3_col0\" class=\"data row3 col0\" >0.9857</td>\n",
              "      <td id=\"T_0afe3_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row3_col2\" class=\"data row3 col2\" >0.9520</td>\n",
              "      <td id=\"T_0afe3_row3_col3\" class=\"data row3 col3\" >0.9876</td>\n",
              "      <td id=\"T_0afe3_row3_col4\" class=\"data row3 col4\" >0.9695</td>\n",
              "      <td id=\"T_0afe3_row3_col5\" class=\"data row3 col5\" >0.9601</td>\n",
              "      <td id=\"T_0afe3_row3_col6\" class=\"data row3 col6\" >0.9604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_0afe3_row4_col0\" class=\"data row4 col0\" >0.9895</td>\n",
              "      <td id=\"T_0afe3_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row4_col2\" class=\"data row4 col2\" >0.9600</td>\n",
              "      <td id=\"T_0afe3_row4_col3\" class=\"data row4 col3\" >0.9959</td>\n",
              "      <td id=\"T_0afe3_row4_col4\" class=\"data row4 col4\" >0.9776</td>\n",
              "      <td id=\"T_0afe3_row4_col5\" class=\"data row4 col5\" >0.9708</td>\n",
              "      <td id=\"T_0afe3_row4_col6\" class=\"data row4 col6\" >0.9710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_0afe3_row5_col0\" class=\"data row5 col0\" >0.9886</td>\n",
              "      <td id=\"T_0afe3_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row5_col2\" class=\"data row5 col2\" >0.9560</td>\n",
              "      <td id=\"T_0afe3_row5_col3\" class=\"data row5 col3\" >0.9958</td>\n",
              "      <td id=\"T_0afe3_row5_col4\" class=\"data row5 col4\" >0.9755</td>\n",
              "      <td id=\"T_0afe3_row5_col5\" class=\"data row5 col5\" >0.9681</td>\n",
              "      <td id=\"T_0afe3_row5_col6\" class=\"data row5 col6\" >0.9684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_0afe3_row6_col0\" class=\"data row6 col0\" >0.9886</td>\n",
              "      <td id=\"T_0afe3_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row6_col2\" class=\"data row6 col2\" >0.9560</td>\n",
              "      <td id=\"T_0afe3_row6_col3\" class=\"data row6 col3\" >0.9958</td>\n",
              "      <td id=\"T_0afe3_row6_col4\" class=\"data row6 col4\" >0.9755</td>\n",
              "      <td id=\"T_0afe3_row6_col5\" class=\"data row6 col5\" >0.9681</td>\n",
              "      <td id=\"T_0afe3_row6_col6\" class=\"data row6 col6\" >0.9684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_0afe3_row7_col0\" class=\"data row7 col0\" >0.9819</td>\n",
              "      <td id=\"T_0afe3_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row7_col2\" class=\"data row7 col2\" >0.9440</td>\n",
              "      <td id=\"T_0afe3_row7_col3\" class=\"data row7 col3\" >0.9793</td>\n",
              "      <td id=\"T_0afe3_row7_col4\" class=\"data row7 col4\" >0.9613</td>\n",
              "      <td id=\"T_0afe3_row7_col5\" class=\"data row7 col5\" >0.9495</td>\n",
              "      <td id=\"T_0afe3_row7_col6\" class=\"data row7 col6\" >0.9498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_0afe3_row8_col0\" class=\"data row8 col0\" >0.9857</td>\n",
              "      <td id=\"T_0afe3_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row8_col2\" class=\"data row8 col2\" >0.9440</td>\n",
              "      <td id=\"T_0afe3_row8_col3\" class=\"data row8 col3\" >0.9958</td>\n",
              "      <td id=\"T_0afe3_row8_col4\" class=\"data row8 col4\" >0.9692</td>\n",
              "      <td id=\"T_0afe3_row8_col5\" class=\"data row8 col5\" >0.9599</td>\n",
              "      <td id=\"T_0afe3_row8_col6\" class=\"data row8 col6\" >0.9605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_0afe3_row9_col0\" class=\"data row9 col0\" >0.9914</td>\n",
              "      <td id=\"T_0afe3_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row9_col2\" class=\"data row9 col2\" >0.9641</td>\n",
              "      <td id=\"T_0afe3_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
              "      <td id=\"T_0afe3_row9_col4\" class=\"data row9 col4\" >0.9817</td>\n",
              "      <td id=\"T_0afe3_row9_col5\" class=\"data row9 col5\" >0.9761</td>\n",
              "      <td id=\"T_0afe3_row9_col6\" class=\"data row9 col6\" >0.9764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_0afe3_row10_col0\" class=\"data row10 col0\" >0.9886</td>\n",
              "      <td id=\"T_0afe3_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row10_col2\" class=\"data row10 col2\" >0.9584</td>\n",
              "      <td id=\"T_0afe3_row10_col3\" class=\"data row10 col3\" >0.9934</td>\n",
              "      <td id=\"T_0afe3_row10_col4\" class=\"data row10 col4\" >0.9756</td>\n",
              "      <td id=\"T_0afe3_row10_col5\" class=\"data row10 col5\" >0.9681</td>\n",
              "      <td id=\"T_0afe3_row10_col6\" class=\"data row10 col6\" >0.9684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0afe3_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_0afe3_row11_col0\" class=\"data row11 col0\" >0.0032</td>\n",
              "      <td id=\"T_0afe3_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_0afe3_row11_col2\" class=\"data row11 col2\" >0.0094</td>\n",
              "      <td id=\"T_0afe3_row11_col3\" class=\"data row11 col3\" >0.0062</td>\n",
              "      <td id=\"T_0afe3_row11_col4\" class=\"data row11 col4\" >0.0069</td>\n",
              "      <td id=\"T_0afe3_row11_col5\" class=\"data row11 col5\" >0.0090</td>\n",
              "      <td id=\"T_0afe3_row11_col6\" class=\"data row11 col6\" >0.0089</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0370911d39764e98aabd384febc9597e"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = predict_model(rf_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "GcT5xPDNH5vh",
        "outputId": "3394d932-4e26-4d3b-89ee-7727d506a1ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d00cb20b8e0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_0f839\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_0f839_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_0f839_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_0f839_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_0f839_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_0f839_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_0f839_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_0f839_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_0f839_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_0f839_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_0f839_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_0f839_row0_col1\" class=\"data row0 col1\" >0.9904</td>\n",
              "      <td id=\"T_0f839_row0_col2\" class=\"data row0 col2\" >0.9909</td>\n",
              "      <td id=\"T_0f839_row0_col3\" class=\"data row0 col3\" >0.9664</td>\n",
              "      <td id=\"T_0f839_row0_col4\" class=\"data row0 col4\" >0.9933</td>\n",
              "      <td id=\"T_0f839_row0_col5\" class=\"data row0 col5\" >0.9797</td>\n",
              "      <td id=\"T_0f839_row0_col6\" class=\"data row0 col6\" >0.9734</td>\n",
              "      <td id=\"T_0f839_row0_col7\" class=\"data row0 col7\" >0.9736</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "qH6SDaDnIWS6",
        "outputId": "3e23a29b-9b9e-4d17-8743-1598ac83bd6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      satisfaction_level  last_evaluation  number_project  \\\n",
              "6949                0.42             0.56               2   \n",
              "3760                0.62             0.52               3   \n",
              "3460                0.37             0.45               2   \n",
              "5785                0.78             0.98               5   \n",
              "697                 0.36             0.62               4   \n",
              "\n",
              "      average_montly_hours  time_spend_company  Work_accident  \\\n",
              "6949                   143                   3              0   \n",
              "3760                   148                   3              0   \n",
              "3460                   149                   3              0   \n",
              "5785                   263                   6              0   \n",
              "697                    111                   6              0   \n",
              "\n",
              "      promotion_last_5years Departments  salary  Quit_the_Company  \\\n",
              "6949                      0     support  medium                 1   \n",
              "3760                      0       sales  medium                 0   \n",
              "3460                      0       sales     low                 1   \n",
              "5785                      0       sales     low                 1   \n",
              "697                       0          IT     low                 0   \n",
              "\n",
              "      prediction_label  prediction_score  \n",
              "6949                 1              1.00  \n",
              "3760                 0              1.00  \n",
              "3460                 1              1.00  \n",
              "5785                 1              1.00  \n",
              "697                  0              0.97  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ed0a27e-a486-4fcc-a71e-dc0c41710430\" 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>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "      <th>Departments</th>\n",
              "      <th>salary</th>\n",
              "      <th>Quit_the_Company</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>prediction_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6949</th>\n",
              "      <td>0.42</td>\n",
              "      <td>0.56</td>\n",
              "      <td>2</td>\n",
              "      <td>143</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>support</td>\n",
              "      <td>medium</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3760</th>\n",
              "      <td>0.62</td>\n",
              "      <td>0.52</td>\n",
              "      <td>3</td>\n",
              "      <td>148</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>medium</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3460</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2</td>\n",
              "      <td>149</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5785</th>\n",
              "      <td>0.78</td>\n",
              "      <td>0.98</td>\n",
              "      <td>5</td>\n",
              "      <td>263</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.62</td>\n",
              "      <td>4</td>\n",
              "      <td>111</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.97</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-4ed0a27e-a486-4fcc-a71e-dc0c41710430')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\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-4ed0a27e-a486-4fcc-a71e-dc0c41710430 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-4ed0a27e-a486-4fcc-a71e-dc0c41710430');\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-27052484-703c-4024-a417-b1a42cf7ff97\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27052484-703c-4024-a417-b1a42cf7ff97')\"\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",
              "    }\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-27052484-703c-4024-a417-b1a42cf7ff97 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_df",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 4502,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.9300000071525574,\n          0.5600000023841858,\n          0.5099999904632568\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 65,\n        \"samples\": [\n          0.6100000143051147,\n          0.41999998688697815,\n          0.5600000023841858\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2\",\n          \"3\",\n          \"7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 214,\n        \"samples\": [\n          \"175\",\n          \"292\",\n          \"205\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"6\",\n          \"4\",\n          \"3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Departments\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"hr\",\n          \"sales\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"medium\",\n          \"low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quit_the_Company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05814773118341507,\n        \"min\": 0.51,\n        \"max\": 1.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          0.71,\n          0.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fOf5iKLIacE",
        "outputId": "8e6f0e67-2e8e-4d4d-d11c-3637d680364d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4502 entries, 6949 to 14769\n",
            "Data columns (total 12 columns):\n",
            " #   Column                 Non-Null Count  Dtype   \n",
            "---  ------                 --------------  -----   \n",
            " 0   satisfaction_level     4502 non-null   float32 \n",
            " 1   last_evaluation        4502 non-null   float32 \n",
            " 2   number_project         4502 non-null   Int64   \n",
            " 3   average_montly_hours   4502 non-null   Int64   \n",
            " 4   time_spend_company     4502 non-null   Int64   \n",
            " 5   Work_accident          4502 non-null   Int64   \n",
            " 6   promotion_last_5years  4502 non-null   Int64   \n",
            " 7   Departments            4502 non-null   category\n",
            " 8   salary                 4502 non-null   category\n",
            " 9   Quit_the_Company       4502 non-null   Int64   \n",
            " 10  prediction_label       4502 non-null   int64   \n",
            " 11  prediction_score       4502 non-null   float64 \n",
            "dtypes: Int64(6), category(2), float32(2), float64(1), int64(1)\n",
            "memory usage: 387.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_predictions = predict_model(rf_model, data= df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "9GJIbta0Ir-G",
        "outputId": "006776d5-9e08-43ff-ed73-8449e03fd410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d010452fdc0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_11c88\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_11c88_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_11c88_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_11c88_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_11c88_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_11c88_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_11c88_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_11c88_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_11c88_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_11c88_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_11c88_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_11c88_row0_col1\" class=\"data row0 col1\" >0.9300</td>\n",
              "      <td id=\"T_11c88_row0_col2\" class=\"data row0 col2\" >0</td>\n",
              "      <td id=\"T_11c88_row0_col3\" class=\"data row0 col3\" >0.0000</td>\n",
              "      <td id=\"T_11c88_row0_col4\" class=\"data row0 col4\" >0.0000</td>\n",
              "      <td id=\"T_11c88_row0_col5\" class=\"data row0 col5\" >0.0000</td>\n",
              "      <td id=\"T_11c88_row0_col6\" class=\"data row0 col6\" >0.0000</td>\n",
              "      <td id=\"T_11c88_row0_col7\" class=\"data row0 col7\" >0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_predictions.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "OKjD51lJJQAk",
        "outputId": "e5b55551-bc7c-4833-9721-a11a479876a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
              "0            0.331690         0.847953               6                   151   \n",
              "1            0.468434         0.169659               5                   303   \n",
              "2            0.858448         0.918311               4                   162   \n",
              "3            0.056211         0.322600               2                   229   \n",
              "4            0.382648         0.434348               4                   260   \n",
              "\n",
              "   time_spend_company  Work_accident  promotion_last_5years Departments  \\\n",
              "0                   4              0                      0          IT   \n",
              "1                   4              0                      0          IT   \n",
              "2                   6              1                      0          IT   \n",
              "3                   5              1                      0          IT   \n",
              "4                   3              1                      0          hr   \n",
              "\n",
              "  salary employee_id  Quit_the_Company  prediction_label  prediction_score  \n",
              "0    low     NE15014                 0                 0              0.82  \n",
              "1    low     NE15015                 0                 1              0.52  \n",
              "2    low     NE15064                 0                 0              0.82  \n",
              "3    low     NE15086                 0                 0              0.64  \n",
              "4    low     NE15021                 0                 0              0.91  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a192139-c650-4ad3-ad7d-2979674d3a14\" 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>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "      <th>Departments</th>\n",
              "      <th>salary</th>\n",
              "      <th>employee_id</th>\n",
              "      <th>Quit_the_Company</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>prediction_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.331690</td>\n",
              "      <td>0.847953</td>\n",
              "      <td>6</td>\n",
              "      <td>151</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15014</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.468434</td>\n",
              "      <td>0.169659</td>\n",
              "      <td>5</td>\n",
              "      <td>303</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15015</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.858448</td>\n",
              "      <td>0.918311</td>\n",
              "      <td>4</td>\n",
              "      <td>162</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15064</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.056211</td>\n",
              "      <td>0.322600</td>\n",
              "      <td>2</td>\n",
              "      <td>229</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>IT</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15086</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.382648</td>\n",
              "      <td>0.434348</td>\n",
              "      <td>4</td>\n",
              "      <td>260</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>hr</td>\n",
              "      <td>low</td>\n",
              "      <td>NE15021</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.91</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-5a192139-c650-4ad3-ad7d-2979674d3a14')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\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-5a192139-c650-4ad3-ad7d-2979674d3a14 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-5a192139-c650-4ad3-ad7d-2979674d3a14');\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-f5733c83-7a56-4aac-9285-e228168e4134\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f5733c83-7a56-4aac-9285-e228168e4134')\"\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",
              "    }\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-f5733c83-7a56-4aac-9285-e228168e4134 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_predictions",
              "summary": "{\n  \"name\": \"new_predictions\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.7729489803314209,\n          0.15366673469543457,\n          0.974857747554779\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.8004854917526245,\n          0.07138621062040329,\n          0.1039334312081337\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"6\",\n          \"5\",\n          \"3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"260\",\n          \"187\",\n          \"240\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"6\",\n          \"10\",\n          \"4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Departments\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"product_mng\",\n          \"hr\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"low\",\n          \"high\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"employee_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"NE15061\",\n          \"NE15081\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quit_the_Company\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1482600772462044,\n        \"min\": 0.52,\n        \"max\": 1.0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#writing predicitons back to big query\n",
        "new_predictions.to_gbq('employeedata.pilot_predictions',project_id,chunksize=None, if_exists = 'replace')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmPoGAmfJWAm",
        "outputId": "8900f41f-7d56-4d35-8862-6cf0ae3506d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1/1 [00:00<00:00, 7516.67it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(rf_model, plot='feature')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "iPtN1l4aKchp",
        "outputId": "eca700be-c7e4-4ef9-cd48-4db5ece3da76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a feature table\n",
        "rf_model.feature_names_in_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfDPE_uWPDRS",
        "outputId": "7731b244-62a2-458c-f102-fdae91f641cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['satisfaction_level', 'last_evaluation', 'number_project',\n",
              "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
              "       'promotion_last_5years', 'Departments_accounting',\n",
              "       'Departments_support', 'Departments_technical',\n",
              "       'Departments_sales', 'Departments_RandD', 'Departments_IT',\n",
              "       'Departments_hr', 'Departments_product_mng',\n",
              "       'Departments_marketing', 'Departments_management', 'salary_medium',\n",
              "       'salary_low', 'salary_high'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model.feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VH95RaB0PkJ_",
        "outputId": "201d4454-6f94-4183-d982-d608f3e68983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.30110658, 0.1259243 , 0.17920058, 0.15511409, 0.18978115,\n",
              "       0.0088762 , 0.0015364 , 0.00194865, 0.00290416, 0.00367545,\n",
              "       0.00352838, 0.00186797, 0.00168919, 0.00183491, 0.00148181,\n",
              "       0.00135336, 0.00152958, 0.00396984, 0.00693197, 0.00574544])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "nEcP1rPlPrFT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_table = pd.DataFrame(zip(rf_model.feature_names_in_,rf_model.feature_importances_),\n",
        "                             columns = ['Feature','Importance'])"
      ],
      "metadata": {
        "id": "FRM0uo_sP0co"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "xFWJU1K2QXId",
        "outputId": "7ed398c4-bdc8-444a-dffd-08bea727bda6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Feature  Importance\n",
              "0        satisfaction_level    0.301107\n",
              "1           last_evaluation    0.125924\n",
              "2            number_project    0.179201\n",
              "3      average_montly_hours    0.155114\n",
              "4        time_spend_company    0.189781\n",
              "5             Work_accident    0.008876\n",
              "6     promotion_last_5years    0.001536\n",
              "7    Departments_accounting    0.001949\n",
              "8       Departments_support    0.002904\n",
              "9     Departments_technical    0.003675\n",
              "10        Departments_sales    0.003528\n",
              "11        Departments_RandD    0.001868\n",
              "12           Departments_IT    0.001689\n",
              "13           Departments_hr    0.001835\n",
              "14  Departments_product_mng    0.001482\n",
              "15    Departments_marketing    0.001353\n",
              "16   Departments_management    0.001530\n",
              "17            salary_medium    0.003970\n",
              "18               salary_low    0.006932\n",
              "19              salary_high    0.005745"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2054aef6-ff34-4cbc-a13a-cda8bf90a065\" 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>Feature</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>satisfaction_level</td>\n",
              "      <td>0.301107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>last_evaluation</td>\n",
              "      <td>0.125924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>number_project</td>\n",
              "      <td>0.179201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>average_montly_hours</td>\n",
              "      <td>0.155114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>time_spend_company</td>\n",
              "      <td>0.189781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Work_accident</td>\n",
              "      <td>0.008876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>promotion_last_5years</td>\n",
              "      <td>0.001536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Departments_accounting</td>\n",
              "      <td>0.001949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Departments_support</td>\n",
              "      <td>0.002904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Departments_technical</td>\n",
              "      <td>0.003675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Departments_sales</td>\n",
              "      <td>0.003528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Departments_RandD</td>\n",
              "      <td>0.001868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Departments_IT</td>\n",
              "      <td>0.001689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Departments_hr</td>\n",
              "      <td>0.001835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Departments_product_mng</td>\n",
              "      <td>0.001482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Departments_marketing</td>\n",
              "      <td>0.001353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Departments_management</td>\n",
              "      <td>0.001530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>salary_medium</td>\n",
              "      <td>0.003970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>salary_low</td>\n",
              "      <td>0.006932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>salary_high</td>\n",
              "      <td>0.005745</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-2054aef6-ff34-4cbc-a13a-cda8bf90a065')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\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-2054aef6-ff34-4cbc-a13a-cda8bf90a065 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-2054aef6-ff34-4cbc-a13a-cda8bf90a065');\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-a50671f7-2ef1-4adc-b357-2331f01c6bbb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a50671f7-2ef1-4adc-b357-2331f01c6bbb')\"\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",
              "    }\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-a50671f7-2ef1-4adc-b357-2331f01c6bbb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_table",
              "summary": "{\n  \"name\": \"feature_table\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"satisfaction_level\",\n          \"salary_medium\",\n          \"Departments_marketing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08853915219444704,\n        \"min\": 0.0013533573725860418,\n        \"max\": 0.301106575706333,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.301106575706333,\n          0.003969844831372118,\n          0.0013533573725860418\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_table.to_gbq('employeedata.feature_table',\n",
        "                      project_id,\n",
        "                      chunksize=None,\n",
        "                      if_exists = 'replace')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6mRUVfAQYZM",
        "outputId": "86c7e1ba-b3cf-4ccf-a6fe-331385a6325c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1/1 [00:00<00:00, 8144.28it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YaUnlEYUR8My"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}