Previsão_preço_carros_PHV.ipynb

{
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "sourceId": 741735,
          "sourceType": "datasetVersion",
          "datasetId": 383055
        }
      ],
      "dockerImageVersionId": 30396,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "642821481c7f4fe5b53548f0246fc62b": {
          "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_3a0c1f63f7e24255bf6214f5bc0c8ef4",
              "IPY_MODEL_ab78e6b3f58149ee8e956de3f28d3396",
              "IPY_MODEL_a1dad115a2934474af5c9b7a617e9e36"
            ],
            "layout": "IPY_MODEL_a395a739f3bf4adbb1d582c7d6e099b3"
          }
        },
        "3a0c1f63f7e24255bf6214f5bc0c8ef4": {
          "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_5ad3651695644a48954f6389d0baa56c",
            "placeholder": "​",
            "style": "IPY_MODEL_94be97c83bfd4d789e8d08fd412fe9bf",
            "value": "Processing: 100%"
          }
        },
        "ab78e6b3f58149ee8e956de3f28d3396": {
          "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_a6cddb549eb242e9a206709f687b96d5",
            "max": 85,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_7c19f1eb2e504eab8c474eb7c7ce17ff",
            "value": 85
          }
        },
        "a1dad115a2934474af5c9b7a617e9e36": {
          "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_d9c36d4de1ef423daae00cd6382cb6d1",
            "placeholder": "​",
            "style": "IPY_MODEL_b4760b18ca90482e8f903836b783f7e0",
            "value": " 85/85 [01:23&lt;00:00,  1.06s/it]"
          }
        },
        "a395a739f3bf4adbb1d582c7d6e099b3": {
          "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
          }
        },
        "5ad3651695644a48954f6389d0baa56c": {
          "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
          }
        },
        "94be97c83bfd4d789e8d08fd412fe9bf": {
          "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": ""
          }
        },
        "a6cddb549eb242e9a206709f687b96d5": {
          "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
          }
        },
        "7c19f1eb2e504eab8c474eb7c7ce17ff": {
          "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": ""
          }
        },
        "d9c36d4de1ef423daae00cd6382cb6d1": {
          "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
          }
        },
        "b4760b18ca90482e8f903836b783f7e0": {
          "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": ""
          }
        },
        "4e1b54a0ea5c40dba2681a3cd19168fb": {
          "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_e2ce61ef16e748f0a8eae41f778fa990",
              "IPY_MODEL_a6363a1d4a284e9b86ee5fef7781f80d",
              "IPY_MODEL_d0379b3ceb9641ba91149d03ee072cd7"
            ],
            "layout": "IPY_MODEL_216a611881184d94ba7eb89cb98f2905"
          }
        },
        "e2ce61ef16e748f0a8eae41f778fa990": {
          "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_2429a041551e4068a943628bdb720db1",
            "placeholder": "​",
            "style": "IPY_MODEL_366b78279b6e4069afcec972f6457489",
            "value": "Processing: 100%"
          }
        },
        "a6363a1d4a284e9b86ee5fef7781f80d": {
          "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_e1c635fb6211491080d6839bcac1e43d",
            "max": 4,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_5c6eae138a734cea8d76645cc78ed239",
            "value": 4
          }
        },
        "d0379b3ceb9641ba91149d03ee072cd7": {
          "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_adebae5782f54053b95ff4987c714db9",
            "placeholder": "​",
            "style": "IPY_MODEL_393be709f1324a12a1d8ffcba27cae8a",
            "value": " 4/4 [00:01&lt;00:00,  2.27it/s]"
          }
        },
        "216a611881184d94ba7eb89cb98f2905": {
          "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
          }
        },
        "2429a041551e4068a943628bdb720db1": {
          "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
          }
        },
        "366b78279b6e4069afcec972f6457489": {
          "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": ""
          }
        },
        "e1c635fb6211491080d6839bcac1e43d": {
          "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
          }
        },
        "5c6eae138a734cea8d76645cc78ed239": {
          "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": ""
          }
        },
        "adebae5782f54053b95ff4987c714db9": {
          "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
          }
        },
        "393be709f1324a12a1d8ffcba27cae8a": {
          "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": ""
          }
        },
        "b29f87ca323d4f8298d5aeff23da0742": {
          "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_b6f8d40f3a204fa4b4d95f4cc62ca09e",
              "IPY_MODEL_f8c68960145c43c79e4ba9b735f2c042",
              "IPY_MODEL_480bf6a8f7d445bb8aa5e83038aca53c"
            ],
            "layout": "IPY_MODEL_517d90412b4847e08f2c58ac18cf62cf"
          }
        },
        "b6f8d40f3a204fa4b4d95f4cc62ca09e": {
          "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_c44806a1276c47b49516279230213683",
            "placeholder": "​",
            "style": "IPY_MODEL_6f40799620db414d94c68f295c77a8f5",
            "value": "Processing: 100%"
          }
        },
        "f8c68960145c43c79e4ba9b735f2c042": {
          "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_1811635d839944ed966e7bbfff79edd0",
            "max": 7,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_74ffab70830d4368820194734f55cf2b",
            "value": 7
          }
        },
        "480bf6a8f7d445bb8aa5e83038aca53c": {
          "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_9a2865621d6741d5bc1c1e9fb982c9df",
            "placeholder": "​",
            "style": "IPY_MODEL_919e18045b424cfebb91578df649eb1c",
            "value": " 7/7 [00:05&lt;00:00,  1.51it/s]"
          }
        },
        "517d90412b4847e08f2c58ac18cf62cf": {
          "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
          }
        },
        "c44806a1276c47b49516279230213683": {
          "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
          }
        },
        "6f40799620db414d94c68f295c77a8f5": {
          "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": ""
          }
        },
        "1811635d839944ed966e7bbfff79edd0": {
          "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
          }
        },
        "74ffab70830d4368820194734f55cf2b": {
          "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": ""
          }
        },
        "9a2865621d6741d5bc1c1e9fb982c9df": {
          "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
          }
        },
        "919e18045b424cfebb91578df649eb1c": {
          "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": ""
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pedrohvel/Previs-o-de-pre-o-de-carros/blob/main/Previs%C3%A3o_pre%C3%A7o_carros_PHV.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Previsão de Preços de Carros.jpg](data:image/jpeg;base64,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)\n",
        "# Previsão de Preço de Carros\n",
        "\n",
        "## Introdução\n",
        "\n",
        "O projeto \"Previsão_preço_carros_PHV.ipynb\" tem como objetivo realizar a previsão de preços de carros com base em um conjunto de dados que abrange diversas informações relevantes sobre as características de diferentes veículos. Utilizando técnicas de regressão linear, uma abordagem clássica em aprendizado de máquina para prever valores numéricos, exploramos a biblioteca scikit-learn para criar, treinar e avaliar o modelo de regressão linear.\n",
        "\n",
        "## Conjunto de Dados\n",
        "\n",
        "O conjunto de dados utilizado é composto por informações como símbolo de classificação, nome do carro, tipo de combustível, aspiração, número de portas, estilo do carro, tipo de tração, localização do motor, entre outros. A variável alvo é o preço do carro, e nossa tarefa é prever esse valor com base nas características fornecidas.\n",
        "\n",
        "## Método\n",
        "\n",
        "Optamos pela regressão linear como método para a previsão de preços. Esta técnica estatística busca estabelecer uma relação linear entre as variáveis independentes (características do carro) e a variável dependente (preço do carro). Utilizaremos a biblioteca scikit-learn para criar, treinar e avaliar o modelo de regressão linear.\n",
        "\n",
        "## Fluxo do Projeto\n",
        "\n",
        "1. **Carregamento e Exploração do Conjunto de Dados:** Inicialmente, o conjunto de dados é carregado e explorado para entender sua estrutura e características.\n",
        "  \n",
        "2. **Análise Descritiva:** Realizamos uma análise descritiva para compreender a distribuição das variáveis e identificar possíveis padrões nos dados.\n",
        "\n",
        "3. **Preparação dos Dados:** Os dados são preparados para o treinamento do modelo, incluindo tratamento de valores ausentes, codificação de variáveis categóricas e divisão do conjunto de dados em treino e teste.\n",
        "\n",
        "4. **Treinamento do Modelo:** Criamos e treinamos um modelo de regressão linear utilizando as variáveis selecionadas.\n",
        "\n",
        "5. **Avaliação do Modelo:** Avaliamos o desempenho do modelo utilizando métricas como o Erro Quadrático Médio (MSE) para determinar a precisão das previsões.\n",
        "\n",
        "6. **Visualização dos Resultados:** Visualizamos os resultados, incluindo um gráfico de dispersão que compara as previsões do modelo com os valores reais.\n",
        "\n",
        "## Conclusão\n",
        "\n",
        "O projeto visa oferecer uma compreensão prática de como aplicar a regressão linear para prever preços de carros com base em características específicas. A conclusão incluirá insights sobre o desempenho do modelo e possíveis melhorias para futuras iterações.\n"
      ],
      "metadata": {
        "id": "pta4TrfxXyBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Preparação do ambiente de execução**"
      ],
      "metadata": {
        "id": "VdV4zzhSutZy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instalar a versão específica do scipy\n",
        "!pip install scipy==1.7.3\n",
        "\n",
        "# Instalar pacotes necessários\n",
        "!pip install shap\n",
        "!pip install interpret\n",
        "!pip install pycaret[full]\n",
        "\n",
        "# Importações\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from pycaret.regression import *\n",
        "\n",
        "# Configurar paleta de cores azul piscina/turquesa forte\n",
        "turquoise_strong = [\"#00CED1\", \"#00BFFF\", \"#00FFFF\", \"#00FFFF\", \"#00CED1\", \"#00BFFF\"]\n",
        "\n",
        "# Configurar o estilo do seaborn\n",
        "sns.set(style=\"darkgrid\", palette=turquoise_strong, rc={\n",
        "    \"axes.facecolor\": \"#282828\",\n",
        "    \"grid.color\": \"#4d4d4d\",\n",
        "    \"text.color\": \"white\",\n",
        "    \"font.family\": \"sans-serif\",\n",
        "    \"font.sans-serif\": \"DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\",\n",
        "    \"font.weight\": \"bold\",\n",
        "    \"axes.labelweight\": \"bold\",\n",
        "    \"axes.titleweight\": \"bold\",\n",
        "    \"grid.linewidth\": 1,\n",
        "    \"axes.edgecolor\": \"black\"\n",
        "})\n"
      ],
      "metadata": {
        "id": "4JnNwb8Euxy0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7537
        },
        "outputId": "498a96bc-3b87-4c53-bb91-ebbf2abbbc4c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scipy==1.7.3\n",
            "  Downloading scipy-1.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting numpy<1.23.0,>=1.16.5 (from scipy==1.7.3)\n",
            "  Downloading numpy-1.22.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.8/16.8 MB\u001b[0m \u001b[31m81.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: numpy, scipy\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.23.5\n",
            "    Uninstalling numpy-1.23.5:\n",
            "      Successfully uninstalled numpy-1.23.5\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.10.1\n",
            "    Uninstalling scipy-1.10.1:\n",
            "      Successfully uninstalled scipy-1.10.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "arviz 0.15.1 requires scipy>=1.8.0, but you have scipy 1.7.3 which is incompatible.\n",
            "jax 0.4.23 requires scipy>=1.9, but you have scipy 1.7.3 which is incompatible.\n",
            "jaxlib 0.4.23+cuda12.cudnn89 requires scipy>=1.9, but you have scipy 1.7.3 which is incompatible.\n",
            "plotnine 0.12.4 requires numpy>=1.23.0, but you have numpy 1.22.4 which is incompatible.\n",
            "pycaret 3.2.0 requires scipy~=1.10.1, but you have scipy 1.7.3 which is incompatible.\n",
            "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.22.4 which is incompatible.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed numpy-1.22.4 scipy-1.7.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: shap in /usr/local/lib/python3.10/dist-packages (0.44.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from shap) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from shap) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from shap) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from shap) (1.5.3)\n",
            "Requirement already satisfied: tqdm>=4.27.0 in /usr/local/lib/python3.10/dist-packages (from shap) (4.66.1)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.10/dist-packages (from shap) (21.3)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.10/dist-packages (from shap) (0.0.7)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from shap) (0.58.1)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from shap) (2.2.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from packaging>20.9->shap) (3.1.1)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->shap) (0.41.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2022.7.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (3.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n",
            "Requirement already satisfied: interpret in /usr/local/lib/python3.10/dist-packages (0.5.0)\n",
            "Requirement already satisfied: interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0 in /usr/local/lib/python3.10/dist-packages (from interpret) (0.5.0)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.22.4)\n",
            "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.7.3)\n",
            "Requirement already satisfied: pandas>=0.19.2 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=0.18.1 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.2.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.3.2)\n",
            "Requirement already satisfied: SALib>=1.3.3 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.4.7)\n",
            "Requirement already satisfied: psutil>=5.6.2 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.9.5)\n",
            "Requirement already satisfied: dash>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.14.2)\n",
            "Requirement already satisfied: dash-core-components>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.0.0)\n",
            "Requirement already satisfied: dash-html-components>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.0.0)\n",
            "Requirement already satisfied: dash-table>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.0.0)\n",
            "Requirement already satisfied: dash-cytoscape>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.3.0)\n",
            "Requirement already satisfied: gevent>=1.3.6 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (23.9.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.31.0)\n",
            "Requirement already satisfied: shap>=0.28.5 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.44.0)\n",
            "Requirement already satisfied: dill>=0.2.5 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.3.7)\n",
            "Requirement already satisfied: ipykernel>=4.10.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.5.6)\n",
            "Requirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (7.34.0)\n",
            "Requirement already satisfied: plotly>=3.8.1 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.15.0)\n",
            "Requirement already satisfied: Flask<3.1,>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.2.3)\n",
            "Requirement already satisfied: Werkzeug<3.1 in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.3.8)\n",
            "Requirement already satisfied: typing-extensions>=4.1.1 in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (4.9.0)\n",
            "Requirement already satisfied: retrying in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.3.4)\n",
            "Requirement already satisfied: ansi2html in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.9.1)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.5.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (67.7.2)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.10/dist-packages (from dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.2.0)\n",
            "Requirement already satisfied: zope.event in /usr/local/lib/python3.10/dist-packages (from gevent>=1.3.6->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.0)\n",
            "Requirement already satisfied: zope.interface in /usr/local/lib/python3.10/dist-packages (from gevent>=1.3.6->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (6.1)\n",
            "Requirement already satisfied: greenlet>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from gevent>=1.3.6->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.0.2)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.2.0)\n",
            "Requirement already satisfied: traitlets>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.7.1)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (6.1.12)\n",
            "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (6.3.2)\n",
            "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.19.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.7.5)\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->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.0.43)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.16.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (4.9.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19.2->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19.2->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2022.7.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=3.8.1->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (8.2.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly>=3.8.1->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (21.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2023.11.17)\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.10/dist-packages (from SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.6.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.70.15)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18.1->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.2.0)\n",
            "Requirement already satisfied: tqdm>=4.27.0 in /usr/local/lib/python3.10/dist-packages (from shap>=0.28.5->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (4.66.1)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.10/dist-packages (from shap>=0.28.5->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.0.7)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from shap>=0.28.5->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.58.1)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from shap>=0.28.5->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.2.1)\n",
            "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from Flask<3.1,>=1.0.4->dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.1.2)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask<3.1,>=1.0.4->dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (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>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (8.1.7)\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->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.8.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (4.46.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->SALib>=1.3.3->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.1.1)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=5.5.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (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->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.2.12)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.19.2->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from Werkzeug<3.1->dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (2.1.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->dash>=1.0.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (3.17.0)\n",
            "Requirement already satisfied: jupyter-core>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-client->ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (5.5.1)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.10/dist-packages (from jupyter-client->ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (23.2.1)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->shap>=0.28.5->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (0.41.1)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.6.0->jupyter-client->ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret) (4.1.0)\n",
            "Requirement already satisfied: pycaret[full] in /usr/local/lib/python3.10/dist-packages (3.2.0)\n",
            "Requirement already satisfied: category-encoders>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.6.3)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.2.1)\n",
            "Requirement already satisfied: deprecation>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.1.0)\n",
            "Requirement already satisfied: imbalanced-learn>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.10.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.12.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (5.2.0)\n",
            "Requirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (7.34.0)\n",
            "Requirement already satisfied: ipywidgets>=7.6.5 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (7.7.1)\n",
            "Requirement already satisfied: jinja2>=1.2 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (3.1.2)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.3.2)\n",
            "Requirement already satisfied: kaleido>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.2.1)\n",
            "Requirement already satisfied: lightgbm>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (4.1.0)\n",
            "Requirement already satisfied: markupsafe>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.1.3)\n",
            "Requirement already satisfied: matplotlib<=3.6,>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (3.6.0)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (5.9.2)\n",
            "Requirement already satisfied: numba>=0.55.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.58.1)\n",
            "Requirement already satisfied: numpy<1.27,>=1.21 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.22.4)\n",
            "Requirement already satisfied: pandas<2.0.0,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.5.3)\n",
            "Requirement already satisfied: plotly-resampler>=0.8.3.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.9.1)\n",
            "Requirement already satisfied: plotly>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (5.15.0)\n",
            "Requirement already satisfied: pmdarima!=1.8.1,<3.0.0,>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.0.4)\n",
            "Requirement already satisfied: psutil>=5.9.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (5.9.5)\n",
            "Requirement already satisfied: pyod>=1.0.8 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.1.2)\n",
            "Requirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.31.0)\n",
            "Requirement already satisfied: schemdraw==0.15 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.15)\n",
            "Requirement already satisfied: scikit-learn<1.3.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.2.2)\n",
            "Requirement already satisfied: scikit-plot>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.3.7)\n",
            "Collecting scipy~=1.10.1 (from pycaret[full])\n",
            "  Using cached scipy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.4 MB)\n",
            "Requirement already satisfied: sktime!=0.17.1,!=0.17.2,!=0.18.0,<0.22.0,>=0.16.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.21.1)\n",
            "Requirement already satisfied: statsmodels>=0.12.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.14.1)\n",
            "Requirement already satisfied: tbats>=1.1.3 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.1.3)\n",
            "Requirement already satisfied: tqdm>=4.62.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (4.66.1)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (3.4.1)\n",
            "Requirement already satisfied: yellowbrick>=1.4 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.5)\n",
            "Requirement already satisfied: wurlitzer in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (3.0.3)\n",
            "Requirement already satisfied: Flask==2.2.3 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.2.3)\n",
            "Requirement already satisfied: Werkzeug<3.0,>=2.2 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.3.8)\n",
            "Requirement already satisfied: boto3>=1.24.56 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.34.7)\n",
            "Requirement already satisfied: dash[testing] in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.14.2)\n",
            "Requirement already satisfied: evidently<0.3,>=0.1.45.dev0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.2.8)\n",
            "Requirement already satisfied: explainerdashboard>=0.3.8 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.4.5)\n",
            "Requirement already satisfied: fairlearn==0.7.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.7.0)\n",
            "Requirement already satisfied: fastapi>=0.75.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.106.0)\n",
            "Requirement already satisfied: fugue>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.8.6)\n",
            "Requirement already satisfied: gradio>=2.8.10 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (3.50.2)\n",
            "Requirement already satisfied: hyperopt>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.2.7)\n",
            "Requirement already satisfied: interpret>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.5.0)\n",
            "Requirement already satisfied: kmodes>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.12.2)\n",
            "Requirement already satisfied: m2cgen>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.10.0)\n",
            "Requirement already satisfied: mlflow<2.0.0,>=1.24.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.30.1)\n",
            "Requirement already satisfied: mlxtend>=0.19.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.22.0)\n",
            "Requirement already satisfied: moto>=3.0.7 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (4.2.12)\n",
            "Requirement already satisfied: optuna>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (3.5.0)\n",
            "Requirement already satisfied: pytest>=7.0.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (7.4.3)\n",
            "Requirement already satisfied: scikit-optimize>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.9.0)\n",
            "Requirement already satisfied: shap>=0.38.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.44.0)\n",
            "Requirement already satisfied: statsforecast<1.6.0,>=0.5.5 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.5.0)\n",
            "Requirement already satisfied: triad<=0.9.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.9.1)\n",
            "Requirement already satisfied: umap-learn>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.5.5)\n",
            "Requirement already satisfied: uvicorn>=0.17.6 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.25.0)\n",
            "Requirement already satisfied: ydata-profiling>=4.3.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (4.6.0)\n",
            "Requirement already satisfied: scikit-learn-intelex>=2023.0.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2024.0.1)\n",
            "Requirement already satisfied: catboost>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (1.2.2)\n",
            "Requirement already satisfied: ray[tune]>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.9.0)\n",
            "Requirement already satisfied: tune-sklearn>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (0.5.0)\n",
            "Requirement already satisfied: xgboost>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from pycaret[full]) (2.0.2)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask==2.2.3->pycaret[full]) (2.1.2)\n",
            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from Flask==2.2.3->pycaret[full]) (8.1.7)\n",
            "Requirement already satisfied: botocore<1.35.0,>=1.34.7 in /usr/local/lib/python3.10/dist-packages (from boto3>=1.24.56->pycaret[full]) (1.34.7)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from boto3>=1.24.56->pycaret[full]) (1.0.1)\n",
            "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from boto3>=1.24.56->pycaret[full]) (0.10.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost>=0.23.2->pycaret[full]) (0.20.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost>=0.23.2->pycaret[full]) (1.16.0)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category-encoders>=2.4.0->pycaret[full]) (0.5.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from deprecation>=2.1.0->pycaret[full]) (21.3)\n",
            "Requirement already satisfied: nltk>=3.6.7 in /usr/local/lib/python3.10/dist-packages (from evidently<0.3,>=0.1.45.dev0->pycaret[full]) (3.8.1)\n",
            "Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.10/dist-packages (from evidently<0.3,>=0.1.45.dev0->pycaret[full]) (6.0.1)\n",
            "Requirement already satisfied: pydantic<2 in /usr/local/lib/python3.10/dist-packages (from evidently<0.3,>=0.1.45.dev0->pycaret[full]) (1.10.13)\n",
            "Requirement already satisfied: dash-auth in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (2.0.0)\n",
            "Requirement already satisfied: dash-bootstrap-components>=1 in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (1.5.0)\n",
            "Requirement already satisfied: dtreeviz>=2.1 in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (2.2.2)\n",
            "Requirement already satisfied: flask-simplelogin in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (0.1.2)\n",
            "Requirement already satisfied: Flask-WTF>=1.1 in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (1.2.1)\n",
            "Requirement already satisfied: jupyter-dash>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (0.4.2)\n",
            "Requirement already satisfied: oyaml in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (1.0)\n",
            "Requirement already satisfied: waitress in /usr/local/lib/python3.10/dist-packages (from explainerdashboard>=0.3.8->pycaret[full]) (2.1.2)\n",
            "Requirement already satisfied: anyio<4.0.0,>=3.7.1 in /usr/local/lib/python3.10/dist-packages (from fastapi>=0.75.0->pycaret[full]) (3.7.1)\n",
            "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in /usr/local/lib/python3.10/dist-packages (from fastapi>=0.75.0->pycaret[full]) (0.27.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from fastapi>=0.75.0->pycaret[full]) (4.9.0)\n",
            "Requirement already satisfied: adagio>=0.2.4 in /usr/local/lib/python3.10/dist-packages (from fugue>=0.8.0->pycaret[full]) (0.2.4)\n",
            "Requirement already satisfied: pyarrow>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from fugue>=0.8.0->pycaret[full]) (10.0.1)\n",
            "Requirement already satisfied: qpd>=0.4.4 in /usr/local/lib/python3.10/dist-packages (from fugue>=0.8.0->pycaret[full]) (0.4.4)\n",
            "Requirement already satisfied: fugue-sql-antlr>=0.1.6 in /usr/local/lib/python3.10/dist-packages (from fugue>=0.8.0->pycaret[full]) (0.2.0)\n",
            "Requirement already satisfied: sqlglot in /usr/local/lib/python3.10/dist-packages (from fugue>=0.8.0->pycaret[full]) (17.16.2)\n",
            "Requirement already satisfied: dask[dataframe,distributed]<2023.7.1,>=2022.9.0 in /usr/local/lib/python3.10/dist-packages (from fugue>=0.8.0->pycaret[full]) (2023.7.0)\n",
            "Requirement already satisfied: aiofiles<24.0,>=22.0 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (23.2.1)\n",
            "Requirement already satisfied: altair<6.0,>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (4.2.2)\n",
            "Requirement already satisfied: ffmpy in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (0.3.1)\n",
            "Requirement already satisfied: gradio-client==0.6.1 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (0.6.1)\n",
            "Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (0.26.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (0.19.4)\n",
            "Requirement already satisfied: importlib-resources<7.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (6.1.1)\n",
            "Requirement already satisfied: orjson~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (3.9.10)\n",
            "Requirement already satisfied: pillow<11.0,>=8.0 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (9.4.0)\n",
            "Requirement already satisfied: pydub in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (0.25.1)\n",
            "Requirement already satisfied: python-multipart in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (0.0.6)\n",
            "Requirement already satisfied: semantic-version~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (2.10.0)\n",
            "Requirement already satisfied: websockets<12.0,>=10.0 in /usr/local/lib/python3.10/dist-packages (from gradio>=2.8.10->pycaret[full]) (11.0.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from gradio-client==0.6.1->gradio>=2.8.10->pycaret[full]) (2023.6.0)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from hyperopt>=0.2.7->pycaret[full]) (3.2.1)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from hyperopt>=0.2.7->pycaret[full]) (0.18.3)\n",
            "Requirement already satisfied: py4j in /usr/local/lib/python3.10/dist-packages (from hyperopt>=0.2.7->pycaret[full]) (0.10.9.7)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn>=0.8.1->pycaret[full]) (3.2.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=4.12.0->pycaret[full]) (3.17.0)\n",
            "Requirement already satisfied: interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0 in /usr/local/lib/python3.10/dist-packages (from interpret>=0.2.7->pycaret[full]) (0.5.0)\n",
            "Requirement already satisfied: dash-core-components>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (2.0.0)\n",
            "Requirement already satisfied: dash-html-components>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (2.0.0)\n",
            "Requirement already satisfied: dash-table>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (5.0.0)\n",
            "Requirement already satisfied: dash-cytoscape>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (0.3.0)\n",
            "Requirement already satisfied: gevent>=1.3.6 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (23.9.1)\n",
            "Requirement already satisfied: dill>=0.2.5 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (0.3.7)\n",
            "Requirement already satisfied: SALib>=1.3.3 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (1.4.7)\n",
            "Requirement already satisfied: ipykernel>=4.10.0 in /usr/local/lib/python3.10/dist-packages (from interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (5.5.6)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (67.7.2)\n",
            "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (0.19.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (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[full]) (3.0.43)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (2.16.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython>=5.5.0->pycaret[full]) (4.9.0)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets>=7.6.5->pycaret[full]) (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[full]) (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[full]) (3.0.9)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<=3.6,>=3.3.0->pycaret[full]) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<=3.6,>=3.3.0->pycaret[full]) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<=3.6,>=3.3.0->pycaret[full]) (4.46.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<=3.6,>=3.3.0->pycaret[full]) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<=3.6,>=3.3.0->pycaret[full]) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<=3.6,>=3.3.0->pycaret[full]) (2.8.2)\n",
            "Requirement already satisfied: databricks-cli<1,>=0.8.7 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (0.18.0)\n",
            "Requirement already satisfied: entrypoints<1 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (0.4)\n",
            "Requirement already satisfied: gitpython<4,>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (3.1.40)\n",
            "Requirement already satisfied: protobuf<5,>=3.12.0 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (3.20.3)\n",
            "Requirement already satisfied: pytz<2023 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (2022.7.1)\n",
            "Requirement already satisfied: sqlparse<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (0.4.4)\n",
            "Requirement already satisfied: alembic<2 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (1.13.1)\n",
            "Requirement already satisfied: docker<7,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (6.1.3)\n",
            "Requirement already satisfied: prometheus-flask-exporter<1 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (0.23.0)\n",
            "Requirement already satisfied: querystring-parser<2 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (1.2.4)\n",
            "Requirement already satisfied: sqlalchemy<2,>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (1.4.50)\n",
            "Requirement already satisfied: gunicorn<21 in /usr/local/lib/python3.10/dist-packages (from mlflow<2.0.0,>=1.24.0->pycaret[full]) (20.1.0)\n",
            "Requirement already satisfied: cryptography>=3.3.1 in /usr/local/lib/python3.10/dist-packages (from moto>=3.0.7->pycaret[full]) (41.0.7)\n",
            "Requirement already satisfied: xmltodict in /usr/local/lib/python3.10/dist-packages (from moto>=3.0.7->pycaret[full]) (0.13.0)\n",
            "Requirement already satisfied: responses>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from moto>=3.0.7->pycaret[full]) (0.24.1)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=4.2.0->pycaret[full]) (2.19.0)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=4.2.0->pycaret[full]) (4.19.2)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.10/dist-packages (from nbformat>=4.2.0->pycaret[full]) (5.5.1)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.55.0->pycaret[full]) (0.41.1)\n",
            "Requirement already satisfied: colorlog in /usr/local/lib/python3.10/dist-packages (from optuna>=3.0.0->pycaret[full]) (6.8.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=5.0.0->pycaret[full]) (8.2.3)\n",
            "Requirement already satisfied: trace-updater>=0.0.8 in /usr/local/lib/python3.10/dist-packages (from plotly-resampler>=0.8.3.1->pycaret[full]) (0.0.9.1)\n",
            "Requirement already satisfied: tsdownsample==0.1.2 in /usr/local/lib/python3.10/dist-packages (from plotly-resampler>=0.8.3.1->pycaret[full]) (0.1.2)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.10/dist-packages (from pmdarima!=1.8.1,<3.0.0,>=1.8.0->pycaret[full]) (3.0.6)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pmdarima!=1.8.1,<3.0.0,>=1.8.0->pycaret[full]) (1.26.18)\n",
            "Requirement already satisfied: iniconfig in /usr/local/lib/python3.10/dist-packages (from pytest>=7.0.1->pycaret[full]) (2.0.0)\n",
            "Requirement already satisfied: pluggy<2.0,>=0.12 in /usr/local/lib/python3.10/dist-packages (from pytest>=7.0.1->pycaret[full]) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /usr/local/lib/python3.10/dist-packages (from pytest>=7.0.1->pycaret[full]) (1.2.0)\n",
            "Requirement already satisfied: tomli>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pytest>=7.0.1->pycaret[full]) (2.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from ray[tune]>=1.0.0->pycaret[full]) (3.13.1)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ray[tune]>=1.0.0->pycaret[full]) (1.0.7)\n",
            "Requirement already satisfied: aiosignal in /usr/local/lib/python3.10/dist-packages (from ray[tune]>=1.0.0->pycaret[full]) (1.3.1)\n",
            "Requirement already satisfied: frozenlist in /usr/local/lib/python3.10/dist-packages (from ray[tune]>=1.0.0->pycaret[full]) (1.4.1)\n",
            "Requirement already satisfied: tensorboardX>=1.9 in /usr/local/lib/python3.10/dist-packages (from ray[tune]>=1.0.0->pycaret[full]) (2.6.2.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->pycaret[full]) (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[full]) (3.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27.1->pycaret[full]) (2023.11.17)\n",
            "Requirement already satisfied: daal4py==2024.0.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn-intelex>=2023.0.1->pycaret[full]) (2024.0.1)\n",
            "Requirement already satisfied: daal==2024.0.1 in /usr/local/lib/python3.10/dist-packages (from daal4py==2024.0.1->scikit-learn-intelex>=2023.0.1->pycaret[full]) (2024.0.1)\n",
            "Requirement already satisfied: tbb==2021.* in /usr/local/lib/python3.10/dist-packages (from daal==2024.0.1->daal4py==2024.0.1->scikit-learn-intelex>=2023.0.1->pycaret[full]) (2021.11.0)\n",
            "Requirement already satisfied: pyaml>=16.9 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize>=0.9.0->pycaret[full]) (23.12.0)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.10/dist-packages (from shap>=0.38.0->pycaret[full]) (0.0.7)\n",
            "Requirement already satisfied: deprecated>=1.2.13 in /usr/local/lib/python3.10/dist-packages (from sktime!=0.17.1,!=0.17.2,!=0.18.0,<0.22.0,>=0.16.1->pycaret[full]) (1.2.14)\n",
            "Requirement already satisfied: scikit-base<0.6.0 in /usr/local/lib/python3.10/dist-packages (from sktime!=0.17.1,!=0.17.2,!=0.18.0,<0.22.0,>=0.16.1->pycaret[full]) (0.5.2)\n",
            "Requirement already satisfied: fs in /usr/local/lib/python3.10/dist-packages (from triad<=0.9.1->pycaret[full]) (2.4.16)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.10/dist-packages (from umap-learn>=0.5.2->pycaret[full]) (0.5.11)\n",
            "Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.17.6->pycaret[full]) (0.14.0)\n",
            "Requirement already satisfied: visions[type_image_path]==0.7.5 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (0.7.5)\n",
            "Requirement already satisfied: htmlmin==0.1.12 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (0.1.12)\n",
            "Requirement already satisfied: phik<0.13,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (0.12.3)\n",
            "Requirement already satisfied: seaborn<0.13,>=0.10.1 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (0.12.2)\n",
            "Requirement already satisfied: multimethod<2,>=1.4 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (1.10)\n",
            "Requirement already satisfied: typeguard<5,>=4.1.2 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (4.1.5)\n",
            "Requirement already satisfied: imagehash==4.3.1 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (4.3.1)\n",
            "Requirement already satisfied: wordcloud>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (1.9.3)\n",
            "Requirement already satisfied: dacite>=1.8 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (1.8.1)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.10/dist-packages (from imagehash==4.3.1->ydata-profiling>=4.3.1->pycaret[full]) (1.5.0)\n",
            "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.10/dist-packages (from visions[type_image_path]==0.7.5->ydata-profiling>=4.3.1->pycaret[full]) (23.1.0)\n",
            "Requirement already satisfied: tangled-up-in-unicode>=0.0.4 in /usr/local/lib/python3.10/dist-packages (from visions[type_image_path]==0.7.5->ydata-profiling>=4.3.1->pycaret[full]) (0.2.0)\n",
            "Requirement already satisfied: retrying in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (1.3.4)\n",
            "Requirement already satisfied: ansi2html in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (1.9.1)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (1.5.8)\n",
            "Requirement already satisfied: beautifulsoup4>=4.8.2 in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (4.11.2)\n",
            "Requirement already satisfied: lxml>=4.6.2 in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (4.9.3)\n",
            "Requirement already satisfied: percy>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (2.0.2)\n",
            "Requirement already satisfied: selenium<=4.2.0,>=3.141.0 in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (4.2.0)\n",
            "Requirement already satisfied: multiprocess>=0.70.12 in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (0.70.15)\n",
            "Requirement already satisfied: dash-testing-stub>=0.0.2 in /usr/local/lib/python3.10/dist-packages (from dash[testing]->pycaret[full]) (0.0.2)\n",
            "Requirement already satisfied: Mako in /usr/local/lib/python3.10/dist-packages (from alembic<2->mlflow<2.0.0,>=1.24.0->pycaret[full]) (1.3.0)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio>=2.8.10->pycaret[full]) (0.12.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4.0.0,>=3.7.1->fastapi>=0.75.0->pycaret[full]) (1.3.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4>=4.8.2->dash[testing]->pycaret[full]) (2.5)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=3.3.1->moto>=3.0.7->pycaret[full]) (1.16.0)\n",
            "Requirement already satisfied: partd>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from dask[dataframe,distributed]<2023.7.1,>=2022.9.0->fugue>=0.8.0->pycaret[full]) (1.4.1)\n",
            "Requirement already satisfied: distributed==2023.7.0 in /usr/local/lib/python3.10/dist-packages (from dask[dataframe,distributed]<2023.7.1,>=2022.9.0->fugue>=0.8.0->pycaret[full]) (2023.7.0)\n",
            "Requirement already satisfied: locket>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from distributed==2023.7.0->dask[dataframe,distributed]<2023.7.1,>=2022.9.0->fugue>=0.8.0->pycaret[full]) (1.0.0)\n",
            "Requirement already satisfied: sortedcontainers>=2.0.5 in /usr/local/lib/python3.10/dist-packages (from distributed==2023.7.0->dask[dataframe,distributed]<2023.7.1,>=2022.9.0->fugue>=0.8.0->pycaret[full]) (2.4.0)\n",
            "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from distributed==2023.7.0->dask[dataframe,distributed]<2023.7.1,>=2022.9.0->fugue>=0.8.0->pycaret[full]) (3.0.0)\n",
            "Requirement already satisfied: tornado>=6.0.4 in /usr/local/lib/python3.10/dist-packages (from distributed==2023.7.0->dask[dataframe,distributed]<2023.7.1,>=2022.9.0->fugue>=0.8.0->pycaret[full]) (6.3.2)\n",
            "Requirement already satisfied: zict>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from distributed==2023.7.0->dask[dataframe,distributed]<2023.7.1,>=2022.9.0->fugue>=0.8.0->pycaret[full]) (3.0.0)\n",
            "Requirement already satisfied: pyjwt>=1.7.0 in /usr/lib/python3/dist-packages (from databricks-cli<1,>=0.8.7->mlflow<2.0.0,>=1.24.0->pycaret[full]) (2.3.0)\n",
            "Requirement already satisfied: oauthlib>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from databricks-cli<1,>=0.8.7->mlflow<2.0.0,>=1.24.0->pycaret[full]) (3.2.2)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from databricks-cli<1,>=0.8.7->mlflow<2.0.0,>=1.24.0->pycaret[full]) (0.9.0)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated>=1.2.13->sktime!=0.17.1,!=0.17.2,!=0.18.0,<0.22.0,>=0.16.1->pycaret[full]) (1.14.1)\n",
            "Requirement already satisfied: websocket-client>=0.32.0 in /usr/local/lib/python3.10/dist-packages (from docker<7,>=4.0.0->mlflow<2.0.0,>=1.24.0->pycaret[full]) (1.7.0)\n",
            "Requirement already satisfied: colour in /usr/local/lib/python3.10/dist-packages (from dtreeviz>=2.1->explainerdashboard>=0.3.8->pycaret[full]) (0.1.5)\n",
            "Requirement already satisfied: wtforms in /usr/local/lib/python3.10/dist-packages (from Flask-WTF>=1.1->explainerdashboard>=0.3.8->pycaret[full]) (3.1.1)\n",
            "Requirement already satisfied: antlr4-python3-runtime<4.12 in /usr/local/lib/python3.10/dist-packages (from fugue-sql-antlr>=0.1.6->fugue>=0.8.0->pycaret[full]) (4.11.1)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from gitpython<4,>=2.1.0->mlflow<2.0.0,>=1.24.0->pycaret[full]) (4.0.11)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.10.0->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (6.1.12)\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[full]) (0.8.3)\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[full]) (2023.11.2)\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[full]) (0.32.0)\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[full]) (0.15.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk>=3.6.7->evidently<0.3,>=0.1.45.dev0->pycaret[full]) (2023.6.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=5.5.0->pycaret[full]) (0.7.0)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.10/dist-packages (from prometheus-flask-exporter<1->mlflow<2.0.0,>=1.24.0->pycaret[full]) (0.19.0)\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[full]) (0.2.12)\n",
            "Requirement already satisfied: trio~=0.17 in /usr/local/lib/python3.10/dist-packages (from selenium<=4.2.0,>=3.141.0->dash[testing]->pycaret[full]) (0.23.2)\n",
            "Requirement already satisfied: trio-websocket~=0.9 in /usr/local/lib/python3.10/dist-packages (from selenium<=4.2.0,>=3.141.0->dash[testing]->pycaret[full]) (0.11.1)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy<2,>=1.4.0->mlflow<2.0.0,>=1.24.0->pycaret[full]) (3.0.2)\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[full]) (6.5.5)\n",
            "Requirement already satisfied: appdirs~=1.4.3 in /usr/local/lib/python3.10/dist-packages (from fs->triad<=0.9.1->pycaret[full]) (1.4.4)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->gradio>=2.8.10->pycaret[full]) (1.0.2)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core->nbformat>=4.2.0->pycaret[full]) (4.1.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=3.3.1->moto>=3.0.7->pycaret[full]) (2.21)\n",
            "Requirement already satisfied: zope.event in /usr/local/lib/python3.10/dist-packages (from gevent>=1.3.6->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (5.0)\n",
            "Requirement already satisfied: zope.interface in /usr/local/lib/python3.10/dist-packages (from gevent>=1.3.6->interpret-core[dash,debug,linear,notebook,plotly,sensitivity,shap]==0.5.0->interpret>=0.2.7->pycaret[full]) (6.1)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->gitpython<4,>=2.1.0->mlflow<2.0.0,>=1.24.0->pycaret[full]) (5.0.1)\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[full]) (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[full]) (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[full]) (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[full]) (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[full]) (0.18.0)\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[full]) (1.0.0)\n",
            "Requirement already satisfied: outcome in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium<=4.2.0,>=3.141.0->dash[testing]->pycaret[full]) (1.3.0.post0)\n",
            "Requirement already satisfied: wsproto>=0.14 in /usr/local/lib/python3.10/dist-packages (from trio-websocket~=0.9->selenium<=4.2.0,>=3.141.0->dash[testing]->pycaret[full]) (1.2.0)\n",
            "Requirement already satisfied: pyOpenSSL>=0.14 in /usr/local/lib/python3.10/dist-packages (from urllib3->pmdarima!=1.8.1,<3.0.0,>=1.8.0->pycaret[full]) (23.3.0)\n",
            "Requirement already satisfied: urllib3-secure-extra in /usr/local/lib/python3.10/dist-packages (from urllib3->pmdarima!=1.8.1,<3.0.0,>=1.8.0->pycaret[full]) (0.1.0)\n",
            "Requirement already satisfied: PySocks!=1.5.7,<2.0,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from urllib3->pmdarima!=1.8.1,<3.0.0,>=1.8.0->pycaret[full]) (1.7.1)\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[full]) (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[full]) (0.2.3)\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[full]) (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[full]) (0.7.1)\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[full]) (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[full]) (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[full]) (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[full]) (1.5.0)\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[full]) (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[full]) (21.2.0)\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[full]) (0.5.1)\n",
            "Installing collected packages: scipy\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.7.3\n",
            "    Uninstalling scipy-1.7.3:\n",
            "      Successfully uninstalled scipy-1.7.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "plotnine 0.12.4 requires numpy>=1.23.0, but you have numpy 1.22.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed scipy-1.10.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Preprocessamento do conjunto de dados**"
      ],
      "metadata": {
        "id": "-cCzvS23uNJb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/pedrohvel/Previs-o-de-pre-o-de-carros/main/CarPrice_Assignment.csv')"
      ],
      "metadata": {
        "id": "CP1OycJlmrb9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "x1-J_YXklwky"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['price'] = df['price'].astype('float')"
      ],
      "metadata": {
        "id": "R5HbUNg4up6h"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['price'] = (df['price'] - df['price'].mean()) / df['price'].std()"
      ],
      "metadata": {
        "id": "RMsTmHsEvT7w"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(df.head())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-12-14T04:31:13.350208Z",
          "iopub.execute_input": "2023-12-14T04:31:13.350494Z",
          "iopub.status.idle": "2023-12-14T04:31:13.388111Z",
          "shell.execute_reply.started": "2023-12-14T04:31:13.350469Z",
          "shell.execute_reply": "2023-12-14T04:31:13.386987Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "D9bopbGgept1",
        "outputId": "351fbd1f-d5d0-4a9c-b8d0-2f2fa2e2ca16"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
              "0       1          3        alfa-romero giulia      gas        std        two   \n",
              "1       2          3       alfa-romero stelvio      gas        std        two   \n",
              "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
              "3       4          2               audi 100 ls      gas        std       four   \n",
              "4       5          2                audi 100ls      gas        std       four   \n",
              "\n",
              "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
              "0  convertible        rwd          front       88.6  ...         130   \n",
              "1  convertible        rwd          front       88.6  ...         130   \n",
              "2    hatchback        rwd          front       94.5  ...         152   \n",
              "3        sedan        fwd          front       99.8  ...         109   \n",
              "4        sedan        4wd          front       99.4  ...         136   \n",
              "\n",
              "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
              "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
              "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
              "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
              "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
              "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
              "\n",
              "   highwaympg     price  \n",
              "0          27  0.027324  \n",
              "1          27  0.403473  \n",
              "2          26  0.403473  \n",
              "3          30  0.084279  \n",
              "4          22  0.522389  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d74c88e-b3bc-4d66-b1ff-b6a8be430f46\" 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>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>CarName</th>\n",
              "      <th>fueltype</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>doornumber</th>\n",
              "      <th>carbody</th>\n",
              "      <th>drivewheel</th>\n",
              "      <th>enginelocation</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>...</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>fuelsystem</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero giulia</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>0.027324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero stelvio</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>0.403473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>alfa-romero Quadrifoglio</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>...</td>\n",
              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>0.403473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100 ls</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>...</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>0.084279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100ls</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>...</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>0.522389</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d74c88e-b3bc-4d66-b1ff-b6a8be430f46')\"\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-5d74c88e-b3bc-4d66-b1ff-b6a8be430f46 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-5d74c88e-b3bc-4d66-b1ff-b6a8be430f46');\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-6b04c3a3-2cd2-4fbd-94f9-f3db578e513e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b04c3a3-2cd2-4fbd-94f9-f3db578e513e')\"\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-6b04c3a3-2cd2-4fbd-94f9-f3db578e513e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exibir as features do conjunto de dados\n",
        "features = df.columns\n",
        "print(\"Features:\", features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "C0nHSS2efp4r",
        "outputId": "5c66bdaa-d7d6-4ba1-abaa-eaf4fad00a4d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features: Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',\n",
            "       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',\n",
            "       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',\n",
            "       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',\n",
            "       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',\n",
            "       'price'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remover as colunas 'CarName' e 'car_ID'\n",
        "df = df.drop(columns=['CarName', 'car_ID'])"
      ],
      "metadata": {
        "id": "ZKZMKQaoroj3"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Análise exploratória**"
      ],
      "metadata": {
        "id": "AL2uQOirquyT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-12-14T04:31:13.389332Z",
          "iopub.execute_input": "2023-12-14T04:31:13.389635Z",
          "iopub.status.idle": "2023-12-14T04:31:13.397051Z",
          "shell.execute_reply.started": "2023-12-14T04:31:13.389610Z",
          "shell.execute_reply": "2023-12-14T04:31:13.395859Z"
        },
        "trusted": true,
        "id": "yqr651nzept4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bebc5dd3-2227-4109-9b23-027e918ff8e5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "symboling             int64\n",
            "fueltype             object\n",
            "aspiration           object\n",
            "doornumber           object\n",
            "carbody              object\n",
            "drivewheel           object\n",
            "enginelocation       object\n",
            "wheelbase           float64\n",
            "carlength           float64\n",
            "carwidth            float64\n",
            "carheight           float64\n",
            "curbweight            int64\n",
            "enginetype           object\n",
            "cylindernumber       object\n",
            "enginesize            int64\n",
            "fuelsystem           object\n",
            "boreratio           float64\n",
            "stroke              float64\n",
            "compressionratio    float64\n",
            "horsepower            int64\n",
            "peakrpm               int64\n",
            "citympg               int64\n",
            "highwaympg            int64\n",
            "price               float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-12-14T04:31:13.398507Z",
          "iopub.execute_input": "2023-12-14T04:31:13.399203Z",
          "iopub.status.idle": "2023-12-14T04:31:13.409949Z",
          "shell.execute_reply.started": "2023-12-14T04:31:13.399162Z",
          "shell.execute_reply": "2023-12-14T04:31:13.409189Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "w0QGYjlYept5",
        "outputId": "3db20487-830b-447c-88e2-d511bf4fd22c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "symboling           0\n",
            "fueltype            0\n",
            "aspiration          0\n",
            "doornumber          0\n",
            "carbody             0\n",
            "drivewheel          0\n",
            "enginelocation      0\n",
            "wheelbase           0\n",
            "carlength           0\n",
            "carwidth            0\n",
            "carheight           0\n",
            "curbweight          0\n",
            "enginetype          0\n",
            "cylindernumber      0\n",
            "enginesize          0\n",
            "fuelsystem          0\n",
            "boreratio           0\n",
            "stroke              0\n",
            "compressionratio    0\n",
            "horsepower          0\n",
            "peakrpm             0\n",
            "citympg             0\n",
            "highwaympg          0\n",
            "price               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "nU3z6EXxsIBz",
        "outputId": "1676b83e-69df-4968-b3be-d2efe3242e80"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  symboling  wheelbase  carlength  carwidth  carheight  \\\n",
              "symboling          1.000000  -0.531954  -0.357612 -0.232919  -0.541038   \n",
              "wheelbase         -0.531954   1.000000   0.874587  0.795144   0.589435   \n",
              "carlength         -0.357612   0.874587   1.000000  0.841118   0.491029   \n",
              "carwidth          -0.232919   0.795144   0.841118  1.000000   0.279210   \n",
              "carheight         -0.541038   0.589435   0.491029  0.279210   1.000000   \n",
              "curbweight        -0.227691   0.776386   0.877728  0.867032   0.295572   \n",
              "enginesize        -0.105790   0.569329   0.683360  0.735433   0.067149   \n",
              "boreratio         -0.130051   0.488750   0.606454  0.559150   0.171071   \n",
              "stroke            -0.008735   0.160959   0.129533  0.182942  -0.055307   \n",
              "compressionratio  -0.178515   0.249786   0.158414  0.181129   0.261214   \n",
              "horsepower         0.070873   0.353294   0.552623  0.640732  -0.108802   \n",
              "peakrpm            0.273606  -0.360469  -0.287242 -0.220012  -0.320411   \n",
              "citympg           -0.035823  -0.470414  -0.670909 -0.642704  -0.048640   \n",
              "highwaympg         0.034606  -0.544082  -0.704662 -0.677218  -0.107358   \n",
              "price             -0.079978   0.577816   0.682920  0.759325   0.119336   \n",
              "\n",
              "                  curbweight  enginesize  boreratio    stroke  \\\n",
              "symboling          -0.227691   -0.105790  -0.130051 -0.008735   \n",
              "wheelbase           0.776386    0.569329   0.488750  0.160959   \n",
              "carlength           0.877728    0.683360   0.606454  0.129533   \n",
              "carwidth            0.867032    0.735433   0.559150  0.182942   \n",
              "carheight           0.295572    0.067149   0.171071 -0.055307   \n",
              "curbweight          1.000000    0.850594   0.648480  0.168790   \n",
              "enginesize          0.850594    1.000000   0.583774  0.203129   \n",
              "boreratio           0.648480    0.583774   1.000000 -0.055909   \n",
              "stroke              0.168790    0.203129  -0.055909  1.000000   \n",
              "compressionratio    0.151362    0.028971   0.005197  0.186110   \n",
              "horsepower          0.750739    0.809769   0.573677  0.080940   \n",
              "peakrpm            -0.266243   -0.244660  -0.254976 -0.067964   \n",
              "citympg            -0.757414   -0.653658  -0.584532 -0.042145   \n",
              "highwaympg         -0.797465   -0.677470  -0.587012 -0.043931   \n",
              "price               0.835305    0.874145   0.553173  0.079443   \n",
              "\n",
              "                  compressionratio  horsepower   peakrpm   citympg  \\\n",
              "symboling                -0.178515    0.070873  0.273606 -0.035823   \n",
              "wheelbase                 0.249786    0.353294 -0.360469 -0.470414   \n",
              "carlength                 0.158414    0.552623 -0.287242 -0.670909   \n",
              "carwidth                  0.181129    0.640732 -0.220012 -0.642704   \n",
              "carheight                 0.261214   -0.108802 -0.320411 -0.048640   \n",
              "curbweight                0.151362    0.750739 -0.266243 -0.757414   \n",
              "enginesize                0.028971    0.809769 -0.244660 -0.653658   \n",
              "boreratio                 0.005197    0.573677 -0.254976 -0.584532   \n",
              "stroke                    0.186110    0.080940 -0.067964 -0.042145   \n",
              "compressionratio          1.000000   -0.204326 -0.435741  0.324701   \n",
              "horsepower               -0.204326    1.000000  0.131073 -0.801456   \n",
              "peakrpm                  -0.435741    0.131073  1.000000 -0.113544   \n",
              "citympg                   0.324701   -0.801456 -0.113544  1.000000   \n",
              "highwaympg                0.265201   -0.770544 -0.054275  0.971337   \n",
              "price                     0.067984    0.808139 -0.085267 -0.685751   \n",
              "\n",
              "                  highwaympg     price  \n",
              "symboling           0.034606 -0.079978  \n",
              "wheelbase          -0.544082  0.577816  \n",
              "carlength          -0.704662  0.682920  \n",
              "carwidth           -0.677218  0.759325  \n",
              "carheight          -0.107358  0.119336  \n",
              "curbweight         -0.797465  0.835305  \n",
              "enginesize         -0.677470  0.874145  \n",
              "boreratio          -0.587012  0.553173  \n",
              "stroke             -0.043931  0.079443  \n",
              "compressionratio    0.265201  0.067984  \n",
              "horsepower         -0.770544  0.808139  \n",
              "peakrpm            -0.054275 -0.085267  \n",
              "citympg             0.971337 -0.685751  \n",
              "highwaympg          1.000000 -0.697599  \n",
              "price              -0.697599  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dfab003a-3f61-4c39-a562-cfd9fdb0ccbf\" 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>symboling</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>carlength</th>\n",
              "      <th>carwidth</th>\n",
              "      <th>carheight</th>\n",
              "      <th>curbweight</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>symboling</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.531954</td>\n",
              "      <td>-0.357612</td>\n",
              "      <td>-0.232919</td>\n",
              "      <td>-0.541038</td>\n",
              "      <td>-0.227691</td>\n",
              "      <td>-0.105790</td>\n",
              "      <td>-0.130051</td>\n",
              "      <td>-0.008735</td>\n",
              "      <td>-0.178515</td>\n",
              "      <td>0.070873</td>\n",
              "      <td>0.273606</td>\n",
              "      <td>-0.035823</td>\n",
              "      <td>0.034606</td>\n",
              "      <td>-0.079978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wheelbase</th>\n",
              "      <td>-0.531954</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.874587</td>\n",
              "      <td>0.795144</td>\n",
              "      <td>0.589435</td>\n",
              "      <td>0.776386</td>\n",
              "      <td>0.569329</td>\n",
              "      <td>0.488750</td>\n",
              "      <td>0.160959</td>\n",
              "      <td>0.249786</td>\n",
              "      <td>0.353294</td>\n",
              "      <td>-0.360469</td>\n",
              "      <td>-0.470414</td>\n",
              "      <td>-0.544082</td>\n",
              "      <td>0.577816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carlength</th>\n",
              "      <td>-0.357612</td>\n",
              "      <td>0.874587</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.841118</td>\n",
              "      <td>0.491029</td>\n",
              "      <td>0.877728</td>\n",
              "      <td>0.683360</td>\n",
              "      <td>0.606454</td>\n",
              "      <td>0.129533</td>\n",
              "      <td>0.158414</td>\n",
              "      <td>0.552623</td>\n",
              "      <td>-0.287242</td>\n",
              "      <td>-0.670909</td>\n",
              "      <td>-0.704662</td>\n",
              "      <td>0.682920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carwidth</th>\n",
              "      <td>-0.232919</td>\n",
              "      <td>0.795144</td>\n",
              "      <td>0.841118</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.279210</td>\n",
              "      <td>0.867032</td>\n",
              "      <td>0.735433</td>\n",
              "      <td>0.559150</td>\n",
              "      <td>0.182942</td>\n",
              "      <td>0.181129</td>\n",
              "      <td>0.640732</td>\n",
              "      <td>-0.220012</td>\n",
              "      <td>-0.642704</td>\n",
              "      <td>-0.677218</td>\n",
              "      <td>0.759325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carheight</th>\n",
              "      <td>-0.541038</td>\n",
              "      <td>0.589435</td>\n",
              "      <td>0.491029</td>\n",
              "      <td>0.279210</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.295572</td>\n",
              "      <td>0.067149</td>\n",
              "      <td>0.171071</td>\n",
              "      <td>-0.055307</td>\n",
              "      <td>0.261214</td>\n",
              "      <td>-0.108802</td>\n",
              "      <td>-0.320411</td>\n",
              "      <td>-0.048640</td>\n",
              "      <td>-0.107358</td>\n",
              "      <td>0.119336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>curbweight</th>\n",
              "      <td>-0.227691</td>\n",
              "      <td>0.776386</td>\n",
              "      <td>0.877728</td>\n",
              "      <td>0.867032</td>\n",
              "      <td>0.295572</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.850594</td>\n",
              "      <td>0.648480</td>\n",
              "      <td>0.168790</td>\n",
              "      <td>0.151362</td>\n",
              "      <td>0.750739</td>\n",
              "      <td>-0.266243</td>\n",
              "      <td>-0.757414</td>\n",
              "      <td>-0.797465</td>\n",
              "      <td>0.835305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enginesize</th>\n",
              "      <td>-0.105790</td>\n",
              "      <td>0.569329</td>\n",
              "      <td>0.683360</td>\n",
              "      <td>0.735433</td>\n",
              "      <td>0.067149</td>\n",
              "      <td>0.850594</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.583774</td>\n",
              "      <td>0.203129</td>\n",
              "      <td>0.028971</td>\n",
              "      <td>0.809769</td>\n",
              "      <td>-0.244660</td>\n",
              "      <td>-0.653658</td>\n",
              "      <td>-0.677470</td>\n",
              "      <td>0.874145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>boreratio</th>\n",
              "      <td>-0.130051</td>\n",
              "      <td>0.488750</td>\n",
              "      <td>0.606454</td>\n",
              "      <td>0.559150</td>\n",
              "      <td>0.171071</td>\n",
              "      <td>0.648480</td>\n",
              "      <td>0.583774</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.055909</td>\n",
              "      <td>0.005197</td>\n",
              "      <td>0.573677</td>\n",
              "      <td>-0.254976</td>\n",
              "      <td>-0.584532</td>\n",
              "      <td>-0.587012</td>\n",
              "      <td>0.553173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stroke</th>\n",
              "      <td>-0.008735</td>\n",
              "      <td>0.160959</td>\n",
              "      <td>0.129533</td>\n",
              "      <td>0.182942</td>\n",
              "      <td>-0.055307</td>\n",
              "      <td>0.168790</td>\n",
              "      <td>0.203129</td>\n",
              "      <td>-0.055909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.186110</td>\n",
              "      <td>0.080940</td>\n",
              "      <td>-0.067964</td>\n",
              "      <td>-0.042145</td>\n",
              "      <td>-0.043931</td>\n",
              "      <td>0.079443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compressionratio</th>\n",
              "      <td>-0.178515</td>\n",
              "      <td>0.249786</td>\n",
              "      <td>0.158414</td>\n",
              "      <td>0.181129</td>\n",
              "      <td>0.261214</td>\n",
              "      <td>0.151362</td>\n",
              "      <td>0.028971</td>\n",
              "      <td>0.005197</td>\n",
              "      <td>0.186110</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.204326</td>\n",
              "      <td>-0.435741</td>\n",
              "      <td>0.324701</td>\n",
              "      <td>0.265201</td>\n",
              "      <td>0.067984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>horsepower</th>\n",
              "      <td>0.070873</td>\n",
              "      <td>0.353294</td>\n",
              "      <td>0.552623</td>\n",
              "      <td>0.640732</td>\n",
              "      <td>-0.108802</td>\n",
              "      <td>0.750739</td>\n",
              "      <td>0.809769</td>\n",
              "      <td>0.573677</td>\n",
              "      <td>0.080940</td>\n",
              "      <td>-0.204326</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.131073</td>\n",
              "      <td>-0.801456</td>\n",
              "      <td>-0.770544</td>\n",
              "      <td>0.808139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>peakrpm</th>\n",
              "      <td>0.273606</td>\n",
              "      <td>-0.360469</td>\n",
              "      <td>-0.287242</td>\n",
              "      <td>-0.220012</td>\n",
              "      <td>-0.320411</td>\n",
              "      <td>-0.266243</td>\n",
              "      <td>-0.244660</td>\n",
              "      <td>-0.254976</td>\n",
              "      <td>-0.067964</td>\n",
              "      <td>-0.435741</td>\n",
              "      <td>0.131073</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.113544</td>\n",
              "      <td>-0.054275</td>\n",
              "      <td>-0.085267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citympg</th>\n",
              "      <td>-0.035823</td>\n",
              "      <td>-0.470414</td>\n",
              "      <td>-0.670909</td>\n",
              "      <td>-0.642704</td>\n",
              "      <td>-0.048640</td>\n",
              "      <td>-0.757414</td>\n",
              "      <td>-0.653658</td>\n",
              "      <td>-0.584532</td>\n",
              "      <td>-0.042145</td>\n",
              "      <td>0.324701</td>\n",
              "      <td>-0.801456</td>\n",
              "      <td>-0.113544</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.971337</td>\n",
              "      <td>-0.685751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highwaympg</th>\n",
              "      <td>0.034606</td>\n",
              "      <td>-0.544082</td>\n",
              "      <td>-0.704662</td>\n",
              "      <td>-0.677218</td>\n",
              "      <td>-0.107358</td>\n",
              "      <td>-0.797465</td>\n",
              "      <td>-0.677470</td>\n",
              "      <td>-0.587012</td>\n",
              "      <td>-0.043931</td>\n",
              "      <td>0.265201</td>\n",
              "      <td>-0.770544</td>\n",
              "      <td>-0.054275</td>\n",
              "      <td>0.971337</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.697599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>-0.079978</td>\n",
              "      <td>0.577816</td>\n",
              "      <td>0.682920</td>\n",
              "      <td>0.759325</td>\n",
              "      <td>0.119336</td>\n",
              "      <td>0.835305</td>\n",
              "      <td>0.874145</td>\n",
              "      <td>0.553173</td>\n",
              "      <td>0.079443</td>\n",
              "      <td>0.067984</td>\n",
              "      <td>0.808139</td>\n",
              "      <td>-0.085267</td>\n",
              "      <td>-0.685751</td>\n",
              "      <td>-0.697599</td>\n",
              "      <td>1.000000</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-dfab003a-3f61-4c39-a562-cfd9fdb0ccbf')\"\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-dfab003a-3f61-4c39-a562-cfd9fdb0ccbf 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-dfab003a-3f61-4c39-a562-cfd9fdb0ccbf');\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-ef9d55d0-e90e-492c-89fc-0abbc01bedd5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef9d55d0-e90e-492c-89fc-0abbc01bedd5')\"\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-ef9d55d0-e90e-492c-89fc-0abbc01bedd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sumário estatístico das variáveis numéricas\n",
        "display(df.describe())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-12-14T04:31:13.411331Z",
          "iopub.execute_input": "2023-12-14T04:31:13.411647Z",
          "iopub.status.idle": "2023-12-14T04:31:13.464639Z",
          "shell.execute_reply.started": "2023-12-14T04:31:13.411622Z",
          "shell.execute_reply": "2023-12-14T04:31:13.463652Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "G38uaj6Gept8",
        "outputId": "d239ed8d-4a9a-4d60-c76c-47fabd0eb54a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        symboling   wheelbase   carlength    carwidth   carheight  \\\n",
              "count  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
              "mean     0.834146   98.756585  174.049268   65.907805   53.724878   \n",
              "std      1.245307    6.021776   12.337289    2.145204    2.443522   \n",
              "min     -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
              "25%      0.000000   94.500000  166.300000   64.100000   52.000000   \n",
              "50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n",
              "75%      2.000000  102.400000  183.100000   66.900000   55.500000   \n",
              "max      3.000000  120.900000  208.100000   72.300000   59.800000   \n",
              "\n",
              "        curbweight  enginesize   boreratio      stroke  compressionratio  \\\n",
              "count   205.000000  205.000000  205.000000  205.000000        205.000000   \n",
              "mean   2555.565854  126.907317    3.329756    3.255415         10.142537   \n",
              "std     520.680204   41.642693    0.270844    0.313597          3.972040   \n",
              "min    1488.000000   61.000000    2.540000    2.070000          7.000000   \n",
              "25%    2145.000000   97.000000    3.150000    3.110000          8.600000   \n",
              "50%    2414.000000  120.000000    3.310000    3.290000          9.000000   \n",
              "75%    2935.000000  141.000000    3.580000    3.410000          9.400000   \n",
              "max    4066.000000  326.000000    3.940000    4.170000         23.000000   \n",
              "\n",
              "       horsepower      peakrpm     citympg  highwaympg         price  \n",
              "count  205.000000   205.000000  205.000000  205.000000  2.050000e+02  \n",
              "mean   104.117073  5125.121951   25.219512   30.751220  1.733031e-16  \n",
              "std     39.544167   476.985643    6.542142    6.886443  1.000000e+00  \n",
              "min     48.000000  4150.000000   13.000000   16.000000 -1.021262e+00  \n",
              "25%     70.000000  4800.000000   19.000000   25.000000 -6.870462e-01  \n",
              "50%     95.000000  5200.000000   24.000000   30.000000 -3.732339e-01  \n",
              "75%    116.000000  5500.000000   30.000000   34.000000  4.038489e-01  \n",
              "max    288.000000  6600.000000   49.000000   54.000000  4.021014e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac6f647f-cb03-474e-a49f-71ca76361974\" 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>symboling</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>carlength</th>\n",
              "      <th>carwidth</th>\n",
              "      <th>carheight</th>\n",
              "      <th>curbweight</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>2.050000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.834146</td>\n",
              "      <td>98.756585</td>\n",
              "      <td>174.049268</td>\n",
              "      <td>65.907805</td>\n",
              "      <td>53.724878</td>\n",
              "      <td>2555.565854</td>\n",
              "      <td>126.907317</td>\n",
              "      <td>3.329756</td>\n",
              "      <td>3.255415</td>\n",
              "      <td>10.142537</td>\n",
              "      <td>104.117073</td>\n",
              "      <td>5125.121951</td>\n",
              "      <td>25.219512</td>\n",
              "      <td>30.751220</td>\n",
              "      <td>1.733031e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.245307</td>\n",
              "      <td>6.021776</td>\n",
              "      <td>12.337289</td>\n",
              "      <td>2.145204</td>\n",
              "      <td>2.443522</td>\n",
              "      <td>520.680204</td>\n",
              "      <td>41.642693</td>\n",
              "      <td>0.270844</td>\n",
              "      <td>0.313597</td>\n",
              "      <td>3.972040</td>\n",
              "      <td>39.544167</td>\n",
              "      <td>476.985643</td>\n",
              "      <td>6.542142</td>\n",
              "      <td>6.886443</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.000000</td>\n",
              "      <td>86.600000</td>\n",
              "      <td>141.100000</td>\n",
              "      <td>60.300000</td>\n",
              "      <td>47.800000</td>\n",
              "      <td>1488.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>2.540000</td>\n",
              "      <td>2.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>4150.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>-1.021262e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.500000</td>\n",
              "      <td>166.300000</td>\n",
              "      <td>64.100000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2145.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>4800.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>-6.870462e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>173.200000</td>\n",
              "      <td>65.500000</td>\n",
              "      <td>54.100000</td>\n",
              "      <td>2414.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>3.290000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>5200.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>-3.732339e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>102.400000</td>\n",
              "      <td>183.100000</td>\n",
              "      <td>66.900000</td>\n",
              "      <td>55.500000</td>\n",
              "      <td>2935.000000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>3.580000</td>\n",
              "      <td>3.410000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>5500.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>4.038489e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>120.900000</td>\n",
              "      <td>208.100000</td>\n",
              "      <td>72.300000</td>\n",
              "      <td>59.800000</td>\n",
              "      <td>4066.000000</td>\n",
              "      <td>326.000000</td>\n",
              "      <td>3.940000</td>\n",
              "      <td>4.170000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>6600.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>4.021014e+00</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-ac6f647f-cb03-474e-a49f-71ca76361974')\"\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-ac6f647f-cb03-474e-a49f-71ca76361974 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-ac6f647f-cb03-474e-a49f-71ca76361974');\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-14231b03-5906-46c0-83fd-eac6bd8b0e07\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14231b03-5906-46c0-83fd-eac6bd8b0e07')\"\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-14231b03-5906-46c0-83fd-eac6bd8b0e07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Contagem de valores únicos nas variáveis categóricas\n",
        "print(\"Valores Únicos:\")\n",
        "for col in df.select_dtypes(include=['object']).columns:\n",
        "    print(col, \":\", len(df[col].unique()))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-12-14T04:31:13.465739Z",
          "iopub.execute_input": "2023-12-14T04:31:13.465969Z",
          "iopub.status.idle": "2023-12-14T04:31:13.473314Z",
          "shell.execute_reply.started": "2023-12-14T04:31:13.465946Z",
          "shell.execute_reply": "2023-12-14T04:31:13.472690Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eOkS4shFept9",
        "outputId": "0611bf39-d79d-45a4-a12a-6d4cbd9eb740"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valores Únicos:\n",
            "fueltype : 2\n",
            "aspiration : 2\n",
            "doornumber : 2\n",
            "carbody : 5\n",
            "drivewheel : 3\n",
            "enginelocation : 2\n",
            "enginetype : 7\n",
            "cylindernumber : 7\n",
            "fuelsystem : 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualização de dados**"
      ],
      "metadata": {
        "id": "Gus4rOpjq0e5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gráfico 1: Histograma de 'wheelbase'**"
      ],
      "metadata": {
        "id": "EamWLEXLpX_8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.histplot(df['wheelbase'], bins=20, kde=True)\n",
        "\n",
        "# Adicionar os valores na parte superior do histograma\n",
        "for p in ax.patches:\n",
        "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
        "                ha='center', va='center', color='white', fontsize=10, fontweight='bold')\n",
        "\n",
        "plt.title('Histograma de Wheelbase')\n",
        "plt.xlabel('Wheelbase')\n",
        "plt.ylabel('Contagem')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-12-14T04:31:13.474095Z",
          "iopub.execute_input": "2023-12-14T04:31:13.474303Z",
          "iopub.status.idle": "2023-12-14T04:31:15.281116Z",
          "shell.execute_reply.started": "2023-12-14T04:31:13.474283Z",
          "shell.execute_reply": "2023-12-14T04:31:15.280106Z"
        },
        "trusted": true,
        "id": "t7iV657Uept-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "c3a33d2e-1fe1-4944-83e2-1a21dacc0911"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Histograma de uma Coluna Numérica (por exemplo, 'horsepower')**"
      ],
      "metadata": {
        "id": "1c9LGGRAWs6K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico: Histograma de 'horsepower'\n",
        "ax = sns.histplot(df['horsepower'], bins=20, kde=True)\n",
        "\n",
        "# Adicionar os valores na parte superior do histograma\n",
        "for p in ax.patches:\n",
        "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
        "                ha='center', va='center', color='white', fontsize=10, fontweight='bold')\n",
        "\n",
        "plt.title('Distribuição de Horsepower')\n",
        "plt.xlabel('Horsepower')\n",
        "plt.ylabel('Contagem')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dpY8N5K1Wbsi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "25c4691a-5a18-4d86-beed-4d843536bd8f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Contagem de Carros por Tipo de Combustível**"
      ],
      "metadata": {
        "id": "5yvpe0EOWq23"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico: Contagem de Carros por Tipo de Combustível\n",
        "ax = sns.countplot(x='fueltype', data=df)\n",
        "\n",
        "# Adicionar os valores no centro das colunas\n",
        "for p in ax.patches:\n",
        "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height() / 2),\n",
        "                ha='center', va='center', color='white', fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.title('Contagem de Carros por Tipo de Combustível')\n",
        "plt.xlabel('Tipo de Combustível')\n",
        "plt.ylabel('Contagem')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "uWQ9V-ALWbaO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "d1d43055-7433-486f-ea80-3a68c650d36a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dispersão entre Preço e Horsepower**"
      ],
      "metadata": {
        "id": "RWVGbMIuWn4i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico: Dispersão entre Preço e Horsepower com Linha de Regressão\n",
        "sns.regplot(x='horsepower', y='price', data=df, scatter_kws={'color': '#1f77b4'}, line_kws={'color': '#ff7f0e', 'linewidth': 2})\n",
        "plt.title('Dispersão entre Preço e Horsepower com Linha de Regressão')\n",
        "plt.xlabel('Horsepower')\n",
        "plt.ylabel('Preço')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZRMa3IAvWh1r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "9f6c447d-f767-487e-b599-956cf750ea26"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAIKCAYAAAAK8njVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAACo+0lEQVR4nOz9eZRkd33f/z/vvXVrvbV0Va+jmdGMZjQ7kmxkZAQII8AIMJjVYDAgFiE5sZOcJM7XNvEvtk8SvOTESZzYIwSYfTEIs1oyMmIxCANCSPKsmhnNPr1Vde3rrXvv74/q7qret+ra+v04hyOm1k/Vne551afe9/1WHMdxEEIIIYQQQqyK2u4FCCGEEEII0U0kQAshhBBCCLEGEqCFEEIIIYRYAwnQQgghhBBCrIEEaCGEEEIIIdZAArQQQgghhBBrIAFaCCGEEEKINZAALYQQQgghxBpIgBZCCCGEEGINJEALIYQQQgixBhKghRBCCCGEWAMJ0EIIIYQQQqyBBGghhBBCCCHWQAK0EEIIIYQQayABWgghhBBCiDWQAC2EEEIIIcQaSIAWQgghhBBiDSRACyGEEEIIsQYSoIUQQgghhFgDCdBCCCGEEEKsgQRoIYQQQggh1kACtBBCCCGEEGsgAVoIIYQQQog1kAAthBBCCCHEGkiAFkIIIYQQYg0kQAshhBBCCLEGEqCFEEIIIYRYAwnQQgghhBBCrIEEaCGEEEIIIdZAArQQQgghhBBr4Gr3ArpFJBKhXC4zMjLS7qUIIYQQQohFjI6O4vF4SKVSm/o8iuM4zqY+Q4/w+XyUSiV0XW/rOnRdxzTNtq5BNJcc094kx7X3yDHtPXJMe49pmni9XorF4qY+j+xAr9LIyAhXrlxhz549bVuDx+Nh586dXLp0iXK53LZ1iOaRY9qb5Lj2HjmmvUeOaW969tlnW1ItIDXQQgghhBBCrIEEaCGEEEIIIdZAArQQQgghhBBrIAFaCCGEEEKINZAALYQQQgghxBpIgBZCCCGEEGINJEALIYQQQgixBhKghRBCCCGEWAMJ0EIIIYQQQqyBBGghhBBCCCHWQAK0EEIIIYQQayABWgghhBBCiDWQAC2EEEIIIcQaSIAWQgghhBBiDSRACyGEEEIIsQYSoIUQQgghhFgDCdBCCCGEEEKsgavdCxBCCCGEEL1H0zT6+2MowNj4RLuX01QSoIUQQgghRFMFAgFGBqJEfQ4TObvdy2k6CdBCCCGEEKIpNE0jGo0yHPHiqmQp52zAaPeymk4CtBBCCCGE2DC/38/QQJSoxyafHCdbLBAK9/XkGXcSoIUQQgghxLqpqkosFmU44sdl5kiOxbHt3ivbaCQBWgghhBBCrIvP52NoIErM61BMT5DN59q9pJaQAC2EEEIIIdZEUZRarXNfAI+VJzkex7asdi+rZSRACyGEEEKIVfN4PAwPxIj5FUrpCaa2yK5zIwnQQgghhBBiVfr6+hiJBfE5BVLjcSyr2u4ltYUEaCGEEEIIsSy3283QQD/9ARUzGyeRzbR7SW0lAVoIIYQQQiwpEokwHAsRoER6YpJq1Wz3ktpOArQQQgghhFhA13UGB/sZDLio5qdIpFPtXlLHkAAthBBCCCHmCIVCDMfCBLUKmfhVzEql3UvqKBKghRBCCCEEAC6Xi4H+GENBN04xTWJyqt1L6kgSoIUQQgghBMFgkJH+CEGXSXbqGpVyud1L6lgSoIUQQgghtjBN0+jvjzEc8qCUM0zFp3Acp93L6mgSoIUQQgghtqhAIMDwQJSIXiWXHKNcKrZ7SV1BArQQQgghxBajqiqxWIzhiBdXJUtyfArbttu9rK4hAVoIIYQQYgvx+/0MDUSJemwKqQmyhXy7l9R1JEALIYQQQmwBiqLUdp37/OhmjuRYXHad10kCtBBCCCFEj/N6vQwPxoh5HYrpCZL5XLuX1NUkQAshhBBC9ChFUejr62MkauC1CyTHJ7Etq93L6noSoIUQQgghepDH42FoIEa/X6WcmSSRy7Z7ST1DArQQQgghRI+JRCKMxEL4KZKeiFOtmu1eUk+RAC2EEEII0SN0XWdwsJ/BgIaZS5DIpNu9pJ4kAVoIIYQQogeEw2GGYyEMpUxmchzTrLR7ST1LbfcCmuns2bMcOXKE/fv3s3//fj772c+2e0lCCCFWyfGGqO66HfPgK6nuuh3HG2r3koToCrqus21kmBuGQnjKKRLj1yQ8b7Ke2oH+oz/6I0xTanyEEKLbWEMHMfe/HBRl9rLqzlvRTz+CNn6yjSsTorOFQiGGY2GCWoVM/CpmRYJzK/RMgP7qV7/Kj3/8Y/x+P4VCod3LEUIIsUqON7QgPAOgKJj7X46avopSyrRncUJ0KJfLxUB/jKGgG6eUJjE51e4lbSk9UcKRy+X4sz/7M7xeL+95z3vavRwhhBBrYA0fWRieZyhK7XohxCzDMNi1fYRtQYXi1CjppITnVuuJHej/9b/+F5OTk/zbf/tvGR4e3tTn8ng8m/r4y9F1fc5/RfeTY9qb5LiujW1EUZWl93NUI9rW370gx7QXdeMx1TSNWCzKUMiDUsqQnprCcRx0t7vdS1uSy63jVj1t/xlutq4P0KdOneIzn/kMu3bt4n3vex9f//rXN+25dF1n586dm/b4qzUyMtLuJYgmk2Pam+S4rk7cr5FcJgD0+TX6O+B3L8gx7UXdckx9Ph+xSJCQVqGQmaKiuPH7NnfTsBkCwRB5rQ9HbU3kPHPmTEuep6sDtOM4/OEf/iGWZfEHf/AHuDf5E5hpmly6dGlTn2M5uq4zMjLC6OionCzZI+SY9iY5rmtjjyep/NwuWGwX2rHJnfoBhXJ7J6jJMe093XJMVVUlGu0jqNvkR8e4lpzCtu12L2vVguEiObXEpSvXWvJ81Wq1Jc/T1QH6hz/8IT/72c+45ZZbiMVinDx5kmvX6gdobGyM06dPs3///qY9Z7lcbtpjrZdpmh2xDtE8ckx7kxzXVSqX0U59c+GJhI6DfvoRzEy8fWubR45p7+nkY+rz+RgaiBLzOhTiVygW8u1e0ppVKyYVtdyx7/F6dXWAnum28eSTT/K6171uwfVHjx7l05/+NI8//niLVyaEEGIttPGTqOmrWMNHcHwhlGIGbeyYdN8QW5KiKESjUYb7AnisPMnxOLZltXtZokFXB2ghhBC9QyllcF14rN3LEKKtvF4vQ/1RYn6FUnqCqXyu3UsSi+jqAP2yl72M06dPz7nsS1/6Er/3e78HwB/+4R/y67/+6+1YmhBCCCHEmkSjUYajBj6nQGo8jmW1pp5XrF1XB2ghhBBCiG7ndrsZGuinP6BiZuMkslK61Ol6LkC/4Q1v4A1veEO7lyGEEEIIsaJIJMJILITPKZKZiFOtdm5HEFHXcwFaCCGEEKLT6brO4GA/gwGNan6KqXSq3UsSayABWgghhBCihSKRCEPRIIZSJjM5jmlW2r0ksUYSoIUQQgghWqC+6+yimk+SkF3nriUBWgghhBBik8muc2+RAC2EEEIIsUkad52tfIpEOtnuJYkmkAAthBBCCLEJwuEww7EQQbVCOn4VsyK7zr1CArQQQgghRBPpus7gQIxBQ8fKp4jLrnPPkQAthBBCCNEkM7vOhlohs4V3nTXF4dXXF3npjgyPJWP86eV2r6i5JEALIYQQQmzQ/F3nrVzrfEuswn2Hs1wftAC4PnCNP2UYUNq7sCaSAC2EEEIIsQHhcJihaIigtrV3nYd9FvccynL78NzX//mrI21a0eaRAC2EEEIIsQ66rjPQH2MoqGMX0iQmp9q9pLbwajZv2VvgjbsL6Fr98lNJF5+4uI2fFQaBa21b32aQAC2EEEIIsUay6wzg8JLryrznQI5+rz176VRJ5aOnAjx61Usw7AO1jUvcJBKghRBCCCFWSXada/aFTe49nOVQX3X2MtOCL5338/mzfopWD6bmBhKghRBCCCFWIRQKMRwLE9QqZBPXqJTL7V5Sy0XcNu8+kOOXd5TmXP7DMTcPnDQYLWyNaLk1XqUQQgghxDrJrjO4FIdf3V3kbXvz+HVn9vJLWY37Txg8Efe0cXWtJwFaCCGEEGIJM7vOIZdJZovuOv/CYJn3H8yx3bBmL8uZCp96JsDXL/qwnN5pT7daEqCFEEIIIebRdZ3+WJShoBunmCa+BXedrwtUufdQjl8YrJ8gaTvw8CUvn3jGIF3p7Trn5UiAFkIIIYRoMGfXeWrr7Tr7XTZvuzHPr+4q4mrIyP+S0Ln/hMG5jN6+xXUICdBCCCGEEIDL5ZqudXbjlNIk4kkcx1n5jj1CweHlO0rcvT9Hn6f+uieKKh85afC9UQ+9NE1wIyRACyGEEGLLCwaDjPRHCLpMsltw1/lgn8l9h7Lsi9Tb0pUt+OI5P184F6BsS3BuJAFaCCGEEFuWy+Ui2heZ3XWe2mK7zv1ei3cfyHHndXM/MHzvmoePnDKYKGpL3HNrkwAthBCiLRxvCGv4CI4vjFJMo40dQyll2r0ssYUEAgF2bx/GY+fJTY1SLpdWvlOP0FWHN95Q4C178ngb0uCzGRdHjxv8y5S7fYvrAhKghRBCtJw1dBBz/8tBqX8tXN15K/rpR9DGT7ZxZWIrcLlcDA8Nsj3qZ+zCFRKT41to19nh9qEK7zuUZcRfH7+dqSh84nSAhy77sLdgW7q1kgAthBCipRxvaEF4BkBRMPe/HDV9VXaixaYJBoMMx8LE/FBKjZFOTW2Z8Hy9UeW+w1lu6TdnL7Ns+MYlH598JkDO3Lpt6dZKArQQQoiWsoaPLAzPgKOo4PZTueUtaGPHpaRDNJXL5SIWizIc8qCUM0yN5/CMDLd7WS1h6Dbv2Jfn1TuLaA0Z+cm4ztHjQS7mJA6ulbxjQgghWsrxhRde5vKCNwQo2OERHI9fSjpE0xiGwXB/HxF3lWyiVuusu3u/xldVHF65s8g79+UJueu77KMFlQ+fCPLYuBtpS7c+EqCFEEK0lFJMz/mzo6iz4RlAsafHBUtJh9ggTdNqu85hL2o5Q2J065RrPCda4TcPZ9kdqo/fLlXh8+cCPPisH1Pa0m2IBGghhBAtpY0do7rz1noZh+6nvgvmgFms31hRsIaP4LrwWKuXKbpcIBBgeCBKRK+SS45RLhVXvlMPGPRZvO9gjheNzG1L9+hVDx89aZAoS1u6ZpAALYQQoqWUUgb99CP1EwnVmX/QndpOs2PNub3jC7V+kaJrqapKLBZjOOLFVcmSHJ/Ctu2V79jlPJrDr+3J88YbCngaMvIzKRdHTwQ5mZTx280kAVoIIUTLaeMnUdNXsYaPYA0fwgkO1Xae54VnAKUo5Rtidfx+P0MDUaIem0Jqgmwh3+4ltYDDi0fKvPdgjgFf/YNCsqzwsdMGj1z24kidc9NJgBZCCNEWSimD68JjaGPHKD/v7kU7c+A4aGPHWr420V0URantOvf50c0cybH4lth13hMyue9wjiPRelu6qg1fueDjM2cCFKrSlm6zSIAWQgjRVgtKOmY4DvrpR+QEQrEsr9fL8ECMqA9K6QmS+Vy7l7Tpwm6bd+3P8YodJdSGH5mfTLi5/4TB1bzEu80m77AQQoi2ayzpcHwhlGJG+kCLZSmKQl9fHyNRA49dIDU+iW0tLAHqJZri8Jrri7x9Xx5Dr3cTuZLT+NAJg59Metq4uq1FArQQQoiOMFPSIcRK3G43w4P99PtVyplJpnLZdi9p0/18f5l7D+XYGax/SCiYCp85G+Ar531UZfx2S0mAFkIIIUTXiEQijMRC+CmSnohTrZor36mLjfirvP9Qjl8cqsxeZjvwyBUvHz8dIClt6dpCArQQQgghOp6u6wwO9jMY0DBzCRKZ9Mp36mI+zebXbyzwut0F9IZzAU8kXRw9HuRMWtrStZMEaCGEEEJ0tHA4zHAshKGUyUyOY5qVle/UpRQcXnpdiXcfyBP11juJxEsqHz1l8O2rHmT8dvtJgBZCCCFER9J1nYH+GENBHSufIpFOtntJm2p/xOS+Q1kO9FVnLzMtePC8n8+f9VOypC1dp5AALYQQQoiOEwwGGemPEHKZZBLXqJTLK9+pS/V5LN5zIM/LtpfmXP7YmJsHTgQZK0qdc6eRAC2EEEKIjuFyuYjFogyHPFBKk4gncRxn5Tt2IV11eN2uAm+9sYDfVX+NF7MaR48HeTLhbuPqxHIkQAshhBCiIwQCAUYGokTcVbKJUcrl0sp36koOtw1WeP+hHNsC9bZ0WVPhU88E+PpFH7a0petoEqCFEEII0VaaphGNRhmOeHFVsiRGEz2767zDqHLvoRzPHaifCGk58PAlH584HSBjSp1zN5AALYQQQoi28fv9DA1EiXps8slxssVCu5e0KQIum9/Yl+c11xfRGjLyvyR0/vq4wfmstKXrJhKghRBCCNFyqqoSi8UYivjQzRzJsTi2ba98xy6j4vCKHSXetT9H2FPfVR8vqHz4pMH3x6QtXTeSAC2EEEKIlvL5fAwNRIl5HYrpCZL5XLuXtCkO91W473COveF6W7qSBV84G+DBZ/2UbQnO3UoCtBBCCCFaQlGUWq1zXwCvXSA5PoltWSvfscv0ey3eeyDHL103t/Xed695+MhJg8mStKXrdhKghRBCCLHpPB4PwwMxYn6VcmaSRC7b7iU1nVt1eNMNBd68N4+3ISOfS7s4esLg2JS0pesVEqCFEEIIsan6+voYiQXxOQVS43Esq7rynbqKwwuHy7zvYI4hf72OO11W+Phpg3+47MWWOueeIgFaCCGEEJvC7XYzNNBPf0DFzMZJZDPtXlLT7QpWue9wlptj5uxllg1fu+jj088EyFWlLV0vkgAthBBCiKaLRCIMRYMYSpnMZBzTrKx8py4S1G3euT/PK3cW0Ro2l5+Y1Ln/RJBLOYlYvUyOrhBCCCGaRtd1BgdiDBo61XySRDrV7iU1lao4vHpnkXfsyxN019vSXctrfOiEwY8m3EhbujrN5YLe604oAVoIIYQQzREKhRiOhQlqFTLxq5iV3tp1viVW4d7DWXYF651DilWFz5718+XzfkxpSzfLCIbwGGHytk4q0XulOxKghRBCCLEhLpeLgf4YQ0E3TjFNYnKq3UtqqmGfxfsO5XjB8Ny2dP94xcvfnAowVZa2dDMag/NYskAqNUm12msnjUqAFkIIIcQGGIbBcH8fYd0kNzVKuVxq95KaxqvZvGVvgTfsLuBuyMinUy6OHg9yKiXjt2dsleA8o6sD9He+8x0+/vGPc+bMGVKpFJqmsX37dl72spfx/ve/n0Ag0O4lCiGEED1J0zRisSjDYS9qOcPU2BSO46x8x67g8JJtZd5zMEe/t17AO1VS+ZvTAb51xYsjdc7AdHAORshbri0RnGd0dYD+2c9+xmOPPTb7Z9M0OXv2LGfPnuX48eN8+MMfbuPqhBBCiN7k9/sZHojS57bIJccol4rtXlLT3Bg2ufdQjsPRels604Yvn/fzubN+CtKWDpgbnEcTedLpiS0RnGd0dYC+6aab+H//7/9x0003EQqF+MEPfsC/+3f/jkqlwj/90z+RSqWIRCLtXqYQQgjRE1RVre06R/y4KlmS4wlsuzdaLETcNncfyPHy7SXUhs3lH427+dAJg2uFro5MTTMTnHNVjdFEYcsF5xld/bfhpS996YI/33jjjRw/fhyondQghBBCtJPjDWENH8HxhVGKabSxYyil7utK4PP5GB6MEfXYFFLjZAv5di+pKVyKw2t2F3jbjXkCer0E5XJO4/4TBj+d9LRxdZ1BURQCRnBecE5vyeA8o2cSZqlU4vvf/z5nzpwB4LWvfS2GYTT9eTye9v0g6bo+57+i+8kx7U1yXFfH9gQxBw9he0OopQz6xAnUcrbdy1rUeo+pObCf0p47QaltaTqAff0v4D33bfTJ081e5qZQFIVoNMpQxIe7mieVTGBbFrrb3e6lbYiuuzhipHj97eNc568Hwbyp8NlnQ3zjsoHlKOjd/TI3RFEU/EYQnxEmW3VxLVMgk5miWq2iaRqatnW7jyhOl1f8T05O8sIXvnDOZa985Sv58z//86b+43XDDTcwPj6+YNdbCCHE2mWMnYwPPJe5AydshiafIJS71K5lNZXp8nNhxytYfKiGza7L30SvFlq9rDXxeDz094UI6xal7BSlYmevd7UG3SXePHyJ5wTTs5fZDjyW6ucr49vJWlv7w6+iKPj8AdyBMAXbTSpfJpvNYlnWyndus29961sMDQ3x7LPPburz9MwOdKOHHnoIl8vF//gf/6Opj2uaJpcute8Xu67rjIyMMDo6immaK99BdDw5pr1JjuvybE+Q/LZXQ2Xhe3M5dITAuac6bid6Pce0vOM2Kou8xhkXzDCey6eatcSmUhSFvr4+DN2mOB5ndHrXudv5XTa/tjvDa3bm0BvOBTyRdPOh0xGezbqBeNvW1261Uo0QXiNEPFUlcekymUymq0o1WrXWrg/QAwMDnD59mmKxyNNPP81/+k//ibGxMb72ta9x9913c+TIkaY+X7lcXvlGm8w0zY5Yh2geOaa9SY7r4qojz8XGgSW+AC1G9+G68Nii17XbWo6pqQewnaVPsDN1P3Tg3w+v11urdfZCKXGVZD7X7iVtmILDy7eXuPtAjj5P/e9d0tT5yKkg37o0U4rQW5MTV0tRFALTfZxzVY1LY7ktX+O8kq4P0DN8Ph+33XYbr3jFK/j4xz8OwMWLF5seoIUQQmyM4wuvcH2oRSvZXEoxvcL1nXUi4Uyt83BfAK9dIDUex7K6P0AdiJj85uEs+yL111Kx4O8uBvlB6UYujE0gwTksJweuUVcH6A984AO85jWvYd++fQQCAY4dO8Y3v/nN2et37NjRxtUJIYRYTLcFy/XSxo5R3Xnr7AmEczgO2tix1i9qCR6Ph+GBGDG/SjkzSSLXWSU06xHzWLz7QI6Xbp+7y//9UQ8PnDRIWj62bduaJ8HND87X4nnS6XRX1Dh3iq4O0F/84hf54he/uOh1d955JzfddFOLVySEEGIl3RQsN0IpZdBPP4K5/+VzX6vjoJ9+pGNa2fX19TESC+JzemPXWVcd3rC7wFv2FvC56uUa5zMa958I8lSi1lZjK3bXkODcPF0doN/5znfy05/+lKtXr5LNZgkEAuzZs4dXv/rVvPWtb2338oQQQiyiW4JlM2jjJ1HTV6f7QIdQipmO6QPtdrsZGuinP6BiZuMksu1f08Y4PH+owj0Hs4wE6rXnmYrCJ54J8NAlH7azNcdvS3Buvq4O0B/4wAfavQQhhBDr0MnBstmUUqbjToqMRCKMxEL4KZKeiFOtdne3mJ1GlXsPZfn5gfrrsBz4xkUfn3wmQM5s//htx2NgDdyI4wmilLNok2dQypt7gmZjcM6aEpybqasDtBBCiO7VicGy1+m6zuBgP4MBDTOXIJFZvh690xkum9/Yl+dXri+iNWTkJ+M6958IciHbGTHH6t9Ddfftc75xsUaO4Dr/GFr8XNOfT4Lz5uuMv1lCCCGE2FThcJjhWAhDKZOZHMc0u7fzhIrDK3cWecf+PGF3vc55rKDywEmDx8Y8LD7ApvUcj7EgPAOgKFR3346aHW/aTrQE59aRAC2EEGJDHG9ouhQjjFJM92wpRrfSdZ2B/hhDQR0rnyKRTrZ7SRvynGiF+w7nuCFUP9mxVIXPnwvwpWf9VOzOCM4zrIEbFz9hFkBRsAZuxHXlZxt6DgnOrScBWgghxLpZQwcXnAxY3Xkr+ulH0MZPtnFlAiAYDDLSHyGoVcjEr2JWunfXedBn8d4DOe7YNrct3bevevjoKYN4qTNb0jme4ArXG+t+bEVRMEJh3IGQBOcWkwAthBBiXRxvaGEnDQBFwdz/ctT0VdmJbhOXy0V/LMpwyINTSpOYnGr3ktbNozq8eU+eN+0p4GnIyGfSLo4eNziR7Ox+dMoKY+nXU74xPzhfleDcchKghRBCrIs1fGT5r6aHj8hJgm1gGAbD/X1E3FWyiVHK5VK7l7RODneMlHnvwRyDvnpbulRZ4WOnDR657MXukDrn5WiTZ7BGlvhZcRy0yTOrfqz5wfnKZI5MJiPBuQ0kQAshRJt0e+3wVhnJ3S00TSMWizIc9qKWMyRGp3AcZ+U7dqAbQib3HcrxnFi9LV3Vhq9c8PGZMwEK1fa3pVstpZzDdf6xhScSOg6u84+tagdagnPnkQAthBBt0Au1w1tlJHc38Pv9DA9E6XNb5JJjlEvFdi9pXUK6zTv357hrZwmtIWv+ZMLNh04YXMl3Z2zR4udQs+PTfaANlHJuVX2gJTh3ru78myiEEF2sV2qHt8pI7k6mqmpt1znix1XJkhxPYNv2ynfsMJri8CvXF3n7vjxBvb5rfjWv8aETBj+ecNMpbenWSynnVt1tQ1VVAsGQBOcOJgFaCCFarFdqh7fSSO5O5Pf7GRqIEvXYFFLjZAv5di9pXX6uv8K9h7JcH6yHw4Kp8Jmzfr56wY/ZYW3pNpME5+4hAVoIIVqsl2qHt9JI7k6hKAqxWIzhPj/uap7keBy7CwPWiL/K+w7muH14bmu9b1728rHTAZLlzmxLtxlUVcUIhnEFQuRMVYJzF5AALYQQLdZrtcMykrt1vF4vwwMxoj4opSeYyjdngl0reTWbt+wt8MbdBfSGjHwy6eLo8SDPpPX2La7F5gfnyckc6XS6K8twthoJ0EII0WJSOyzWSlEU+vr6GIkaeOwC6fE4llVd+Y4dRMHhJdeVeM+BPDFvPSAmSiofPRXg21e9OF1e57xaEpy7nwRoIYRoMakdFmvh8XgYGojR71cpZyaZyi0/mKMT7Qub3Hc4y8G+eug3LXjwvJ/Pn/VTsrqnLd1GSHDuHRKghRCiDbTxkyilNObeO2f7QOtnH0VNX2v30kQHiUQijMRC+CmSnohTrZor36mD9Hks7t6f55d3zB3m8tiYmw+fNBgtbI0YMj84T0xkyWQyEpy72Nb4myuEEB1mfh9ox4hRuflNXdUHWqzOegbm6LrO4GA/gwENM5cgkVm+br7TuBSHX91d4G17C/gb2tJdymrcf8LgibinjatrncbgnK0oTE7kJDj3CAnQQgjRYr3SB1qsbD0Dc8LhMMOxEIZSJjM5jmlWFr1dp/qFwTLvP5hju1HvIJE1FT71TIBvXPRhOb1f5yzBufdJgBZCiBbrlT7QYnlr/aCk6zqDAzEGDR0rnyKRTrZ4xRuzPVDl/Ydy/MJgPfDbDjx0ycsnnzFIV3q/zlmC89YhAVoIIVqsl/pAi6Wt5YNSKBRiOBYmqFXIxK9iVrpn19nvsnnbjXl+dVcRV0NG/peEztETBs9mer8tXWNwzlRU4lLj3PMkQAshRIv1Wh9osbjVfFByuVwM9McYCrpximkSk1MtWt3GqTi8bEeJu/fn6PPU65wniiofPmnwT6Meun389kpUVSUYiqD5Q2QqigTnLUQCtBBCtJj0gd4aVvqgFFRMtm8fIayb5KZGKZdLy96+kxzqq3Df4Rw3hutt6coWfOGcny+eC1Du8fHbEpyFBGghhGgx6QO9NSz1QUlTFfqDXnbGr6LZGabGpnAcZ4lH6Sz9Xov3HMjxkuvKcy7/7jUPHzlpMFnq7fHbEpzFDAnQQgjRBtr4SdT01en2ZiGUYmZV7c1E91jsg1LAozNsuBi8+h0K4+col4ptXuXquFWHN9xQ4C178ngbksO5tIv7Txj8y5S7fYtrAQnOYj4J0EII0SZKKSPdNnrczAclZ9tziA5vY5tTwH3hKdJjl7okfDncPlzmnoM5hv319aYrCp84bfDwJS92D9c5q5pGMBiW4CwWkAAthBBCbCK/YjJUvUAsfp5CKkGqkG/3klZlV7DKvYey3NJfn35o2fD1iz4+dSZAzuzdtnQSnMVKJEALIYQQm0BRFPr6+hiJGnjtAsnxSWzLWvmObWboNu/Yl+fV1xfRGjaXfxbXOXo8yKVc70aHxuCcrigkJDiLJfTuT4EQQgjRJm63m6GBfgYCKuXMJIlctt1LWpGqOLxyZ5F37ssTctdPahwtqDxwIsgPx930alu6meCsTu84S3AWK5EALYQQQjRRJBJhOBbC7xTJTMSpVs2V79RmN8Uq3Hcoy+5QfYe8WFX4/Fk/Xzrvx+zRtnSqqhKK9GG7fLPBOZ1Od01XFNE+EqCFEEKIJtB1fXooik41P8VUOgWA4zGwBm7E8QRRylm0yTMo5Vx7Fztt0Gdxz8EcLxyZ25bu0asePnrSIFHuzbZ0qqYRjkQJDlzHWGmC0YkpCc5iTSRACyGEEBtkGAbbBvoWjOK2+vdQ3X37nF7Q1sgRXOcfQ4ufa9dy8WgOv7Ynz5tuKOBuyMjPpFwcPRHkZLI3x2+rmkYwFEb1hcjj5kqywvlL1yiVumeIjegMEqCFEEJsiOMNTfezDqMU01uqn7WmacRiUYbDXtRyhql4fSiK4zEWhGcAFIXq7ttRs+Nt2Il2ePG2Mu89kGPAV6/vTZYVPnbK4JErXpwerHNuDM7pMiTGs5TLZXbs0GTXWayLBGghhBDrZg0dXDBRsbrzVvTTj6CNn2zjyjaf3+9naCBK1G2RS44tGIpiDdy4+Lh2AEXBGrgR15WftWClNXtCJr95OMfhaL0m27ThK+f9fPasn0K199rSLRacM5kMjuPg8XjavTzRxSRACyGEWBfHG1o4jhxAUTD3vxw1fbUnd6IVRSEWizHc50c3cyTH44t2a3A8wWUfx/EYm7XEOcJum3ftz/GKHSXUhkP14wk3HzphcDXfe1FgueAsRDP03k+NEEKIlrCGjyy/wzp8pOcmLXq9XkYG+4n5HIrpCZL5pUswlPLyres2u3xDUxxes6vI22/MY+j14Hglp/GhEwY/mey9HVgJzqJVJEALIYRYF8cXXuH6UItW0hrhcBhDt1FKaVLjcSyruuzttckzWCNLfMhwHLTJM5u0UnjuQJl7D+XYYdTb0hVMhc+cDfCV8z6qTm/VOTcG51QZpiQ4i00mAVoI0VRb+YSyrUYpple4vjeOu9vtZvu2EbaFXVyaTJBMxFd1P6Wcw3X+sYUnEjoOrvOPbcoO9DZ/lfcfynHbUGX2MtuBR654+dgpg1Slt+qcVU0jFI6geIMSnEVLSYAWQjTNVj6hbCvSxo5R3Xnr0jusY8dav6gmi0QijMRCRNwmucQo+ezaPhRo8XOo2fHpPtAGSjm3KX2g/S6bt+4t8LrdBfSGjHwi6eLo8SBn0r3Vlk7TXATDYQnOom0kQAshmmKrnlC2lSmlDPrpRxYed8dBP/1IVx9vXdcZHOxnMKBh5hLEk0W2bRtZ12Mp5dymddtQcHjp9hLv3p8n6q2fyBgvqXz0pMG3r3nopfHbEpy7Sy9/IykBWgjRFFvxhDIB2vhJ1PTV6X8kQyjFTNf/IxkOhxmOhTCUMpnJcUyzgu52t3tZC+yPmNx3KMuBvnotdsWCL5338/mzfkpW75RrSHDuPr3+jaQEaCFEU2y1E8pEnVLK9MSHo8ZR3FY+RSKdbPeSFtXnsXjPgTwv2z53et4Pxjx8+ITBWLF3xm/PD86JsQzZbFaCc4fbCt9ISoAWQjTFVjmhTPSmYDDISH+EkMskk7hGpVxu95IW0FWH1+0u8Na9BfyueoC8kNW4/3iQJxOdt0u+XhKcu9tW+EZSArQQoim2wgllovdomkZ/f4zhkAdKaRLxZAeGNIfbBiu8/1CObYF6W7qsqfCpZwJ8/aIPu0fa0klw7g1b4RtJCdBCiKbo5RPKxOZo9wlGgUCA4YEoEb266CjuTrDTqLWle+5AvS2d5cBDl3x88nSAjNkbdc6LBedMRn5ndKut8I2kBGghRNP04gllYnO08wQjVVVro7gjXlyVLMnxqUVHcbeT4bJ5+748r7m+iNaQkZ9O6Bw9bnA+2xtt6TTNRSgSAU+QVNmR4NwjtsI3khKghRBN1SsnlInN084TjPx+P0MDUaIem3xynGyxsCnPs14qDq/YUeJd+3OEPfWyhfGCyodPGnx/rDfa0jUG52TJZmosLcG5h2yFbyQlQAshhGipdpxgpChKbde5z49u5kiOxTtu1/lwX4XfPJxjT7jelq5kwRfOBvjis34qtgRn0T16/RtJCdBCCCFaqtUnGHm9XoYHY8S8DsX0BMl880dob8SA1+K9B3O8eNvczh/fvebhIycNJkvd35ZOgvPW1MvfSEqAFkII0VKtOsFIURT6+voYiRp47QLJ8Ulsy1r5ji3iVh3etKfAm/fk8TZk5HNpF3993OB4svvb0rlcOsFweDY4J0ZTZLPZdi9LiA2TAC2EEKKlWnGCkcfjYWggRr9fpZyZJJHrpNDm8MLhMu87mGPIXy8jSZcVPn7a4B8ue7G7vM5ZgrPodRKghRBCtNRmn2AUiUQYiYXwUyA9kaBaNTe44ubZHTS573COm2L1NVVt+NoFH585EyBX7e62dLruxgiFwWNIcBY9TQK0EEKIltuME4x0XWdocICBgIqZS5DILF8q0koh3ead+/PctbOI1vCZ4aeTbu4/YXA5193/HOu6GyMcwdEDTBUtEteS5HKdVWsuRDN190+sEEKIrtXME4wikQhD0SCGUiYzGcc0KyvfqQVUxeFXri/yG/vyBPV6W7preY0PnTD40YSbbm5Lp7vdBMN9WC4/iaLFVFyCs9gaJEALIYToWrquMzgQY9DQsfIpEulku5c065ZYhXsPZ9kVrJ+4WKgqfO6Mny9f8GN2cVs6t8dDMNyHqfmYLFhMTUyRz+fbvSwhWqarA/Sjjz7KN77xDY4dO8bk5CSaprFz507e9ra38frXvx5V7e5aMiGEEEsLhUIMx8IEtQqZ+FXMSmfsOg/7LO45lOX24bnr+ccrXv7mVICpcve2pfN4vATCEaqaj7FClWQyQaHQWcNohGiFrg7Qn/70p/n+978/57Jjx47x+7//+xw7doz/8l/+S5tWJoQQYrO4XC4G+mMMBd04pTSJyal2LwkAr2bzlr0F3rC7gLshI59Kujh6IsjpVPeO3/Z4fRjhMBXVx7gEZyG6O0B7PB7uvvtu3vSmN7Fjxw6+853v8B/+w3+gWq3y2c9+lt/6rd8iFou1e5lCCCGaxDAMhvv7iLirZBOjlMuldi8JcHjJdWXecyBHv7felm6qpPI3pwJ866oXp0vrnL0+P/5gGFPzMZqrkEzFKRaL7V6WEG3X1QH6z/7szzAMY/bPd911F1/+8pf59re/jeM4XLp0SQK0EEL0AE3TiMWiDIe9qOUMidEpHMdZ+Y6b7MawyX2Hsxzqq4/fNm348nk/nz3jp2h1Zymhzx/AHwxTVr2MZsskUxOUSp3wYUWIztDVAboxPM8ol+ujUIeGhlq5HCGEEJvA7/czNBAl6rHJTY1RLrV/B7TPY3H3/jwv215Cbdhc/udxNw+cMLhW6M5/Xn3+AP5QhBIermRLpFLjc/5dFULUdOdP+BJ+8pOf8M///M8A3H777Wzbtq3pz+HxeJr+mKul6/qc/4ruJ8e0N8lxbQ5VVYlG+xgK+3CZWdJTU9i2je5u/YhrXa/9c+lza7zu+jJvvSGD31XfAb+cc/GRZyI8kfDWbt9lU7hnd5wVDxPZMqlUksr0SZnt/HdvM8nPqdiIngnQTz/9NP/6X/9rbNtmaGiID37wg01/Dl3X2blzZ9Mfd61GRkbavQTRZHJMe5Mc1/XzeDz094UIuaqUslOUSzq+4fZ+q3jESPGmveMMe+qlDAVL4+sT2/jO1CC2R2UT9m02ldcfwBcIUcJLslChnM3h9aoMD/vavbSWkZ/T3nLmzJmWPE9PBOgnnniCe+65h1wux+DgIB/72McYHh5u+vOYpsmlS5ea/rirpes6IyMjjI6OYpqdM5pWrJ8c094kx3X9FEWhr68PQ7cpjscZTSawLWvlO26i6/wm7zuQ4bmxeumI7cAjVwN86myItOkA4+1b4DoEjCC+YJhU1iZ+9RrpdGbL/V2Vn9PeVK1WV75RE3R9gP7xj3/MvffeS6FQ4LrrruPjH/84O3bs2LTn64RaMNM0O2IdonnkmPYmOa5r4/F4GOqPEvMrlBJXSebbO9HO77J52415fnVXEVfDuYDHpnSOHjc4l9EBa/p/nU9RFAJGEI8RJm/rXBkvkE5LeJSfU7EeXR2gf/CDH/Cv/tW/olQqsWvXLj7+8Y9vys6zEEKIzdXX18dILIjPKZAaj2NZrdlFWoyCw8t3lLh7f44+T73OecrU+cjJII9e1uim8duKohAIhvAYYXJVjdGpAplMfMsHZyE2oqsD9NGjR2fb6ly4cIEXv/jFc67/4Ac/yBve8IZ2LE2ILcvxhrCGj+D4wijFNNrYMZRSpt3LEh3K7XYzNNBPf0DFzMZJZNv7d+Vgn8l9h7Lsi9QDfNmCv7sY5LHSjVwYnwA6Y+LhSlRVJRAM4Q6EyJoao4kC6XS6ZV9xC9HLmhagT506xYULFwDYtWsXBw4caNZDCyG6hDV0kMqBu8DtB1UD28K8/jbcpx5GGz/Z7uWJDhOJRBiOhfA7RTITcarV9u2IxjwW7zmY487r5n6V//1RDw+cNEhaPrZt644R3KqqYgTDuAIhcqbKlckcmUwGq8215EL0kg0H6GPHjvF7v/d7nD17ds7le/fu5U/+5E84fPjwRp9iSZ/85Cc37bGFEGvjeENUjrwGvGHmfL3t9lM58hq86auyEy2A2slbg4P9DAY0qvkpptKp9q1FdXjjDQXesiePt+FfxPMZjaMngjydqPWj64a2dKqmEQyG0fwhMhWFxGSOdDqNbdsr31kIsSYbCtAXL17kXe96F4VCYcFEqDNnzvCud72LL33pSx3R+k0Isbmq229dGJ6h9mdvmOr2W9HPPtqOpYkOEgqFGI6FCWoVMvFxzEq7yiEcbh+q8L5DWUb89YCZqSh84pkAD13yYTvdUeesahrBUBjVNx2cJ7JkMhkJzkJsog0F6KNHj5LP5wHo7+/n0KFDKIrCiRMnmJycJJ/P89d//deb0pNZCNFZ7P4bWPrEKqV2vQToLcvlcjHQH2Mo6MYppklMTrVtLdcbVe49nOXn+uslI5YN37jk45PPBMiZ3TF+e7HgnE6nO2LEuRC9bkMB+oc//CGKonDXXXfx53/+57hctYerVqv8zu/8Dg899BCPPfZYUxYqhOh03bFbJ1rPMAxG+iOE9CrZqWtU2tQyzNBt3rEvz6t3FtEaMvKTcZ37TwS5kO2O8+o1zUUwHEbxBkmVYWq8tuMswVmI1tnQb4t4PA7A61//+tnwDLWdhte//vU89NBDJBKJja1QCNEV1MQ57NguFg/SDmri2RavSLSbpmnEYlGGw17Ucoapsam2hDxVcXjljiLv2J8n7K4//1hB5YETQR4bd9MNHwBdLp1gOAyeIKmyQ2IsQzableAsRBtsKEAbhkE6neapp57ijjvumHPdU089NXsbIUTvc11+HGvnrTgL6qAdlFIa1+XH27U00QaBQIDhgSgRvUouOUa5VFz5TpvgOdEK9x3OcUOo3rqtVIXPnwvw4LN+TLtbgnMEPAbJks3UWJpMRk7IFaKdNhSgb775Zr773e9y9OhRzp49y8033wzA008/zSOPPIKiKLOXCSF6m1LKoB/7GuZ0GztH1VBsCyoF9FMPSweOLUJVVWKxGMMRL65KjuR4oi0nsw36LN53MMeLRuaWi3z7qoePnjKIlzq/JZ2uuzHCERw9QLJkkxhNkc1m270sIQQbDNB333033/ve97Btm29+85t885vfnL3OcRxUVeXd7373hhcphOgO2vhJ1PTV6UEqIZRiRgapbCF+v5+hgShRj00hNUG2kG/5Gjyqw5v35HnTngKehoz8TMrF/ScMTiQ7vx+d7nZjhGrBeapokYgnyeXaO9ZcCDHXhgL085//fP7zf/7P/Mmf/MmCkaAul4vf//3f5xd/8Rc3tEAhRHdRShlcF+Tk4a1EUZTarnOfH3c1T3I8jt3yoR0OLx4p896DOQZ89R3vZFnhY6cNHrnsxenwOmfd7SYY7sNy+YkXLKYmp2Y7XQkhOsuGTzl++9vfzktf+lIefvjhOZMI77rrLoaHhzf68EIIITqY1+tleCBG1Ael9ART+dbvlO4Jmdx7KMdzYvWNnKoNX7ng4zNnAhSqnd2Wzu3xYIT7qGo+JgsWifEEhUKh3csSQiyjKT17hoeHufvuu5vxUEIIIbpENBplJGrgsQukx+NYVnXlOzVR2G3zzn057tpZQm3YXP7JhJv7TxhczXd2WzqPx4sRiVBRfYwXqiSTEpyF6BYb+u3yz//8zzz++OP4/X7e8573zLnuox/9KIVCgec+97k8//nP39AihRBCdA63283QQD8DAZVyZpKpXGtPbNMUh9dcX+Tt+/IYer2F25WcxodOGvxkwtPS9ayVx+vDCNeC81i+wlQyTrHYni4lQoj12VCA/uu//mt+/OMfL7r7nEwm+fCHP8ytt94qAVoIIXpEJBJhJBbCT5H0RJxq1Vz5Tk308/1l7j2UY2ewXmNdMBU+czbAV877qHbw+G2vz08gFKGsermWLZNKT0pwFqJLbShAP/PMMwDcdtttC6577nOfywMPPMBTTz3F//7f/5u/+7u/o1gs8u///b/nLW95y0aeVgghRIvpus7gYD+DAQ0zlyCRSbf0+Uf8Ve45mOP5w5U5l3/zspePnQ6QLHduWzqfP4A/GKakeLmaLZFKT1Aqldq9LCHEBmwoQM+01VnsF0F5elSraZpcuHCB3/md3+FHP/oRf/EXfyEBWgghukg4HGY4FsJQymQmxzHNysp3ahKfZvOWvQXesLuA3pCRTyZdHD0e5Jm03rK1rJU/YOALhinh4Uq2RCo1PvtvoxCiu20oQA8MDDA6OsqnP/1pXvrSl6LrtV9k1WqVT33qU0DtBMO/+Iu/AOD666/nK1/5ygaXLIQQohV0XWegP8ZQUMfKp0ikky17bgWHO68r8e4DeWLeelu6eEnlo6cMvnPV07Ft6fwBA38oQtFxczlTJJUao1Jp3YcOIcTm21CAft7znseXv/xlHn/8cV71qlfN1jr/8Ic/5MqVKyiKMqe8I5vN8o53vGNjKxZCCLHpgsEgI/0RQi6TTOIalRbunO4Lm/zm4SwH+updPUwLHjzv5/Nn/ZSszmxL5w8Y+KaD88VUkVRqdMGMBCFEb9hQgL7nnnt4+OGHKZfLXLlyhS984Quz1zmOg8fj4Z577pm97PnPf76cUCiEEB1M0zT6+2MMhzxQSpOIJ3EcZ+U7NkGfx+Lu/Xl+ecfcssDHxtx8+KTBaKEz29IFjCDeYISCrXNJgrMQW8KGfhvt2bOHv/zLv+R3f/d3SSQSc66LxWJ88IMfZM+ePRtaoBBCiNYIBAIMD0SJ6FVyU6OUy6050U1XHX51V4Ffv7GA31UP6xezGkePB3ky0XnjtxVFIWAE8QQj5C0XF5MF0um4BGchtogNf5x/0YtexLe+9S2+//3vz5lE+MIXvhCv17vRhxdCCLHJVFWtjeKOeHFVskyNJVq06+zwvMEK7z+U47pAvS1d1lT41DMBvnHRh9VhbekURcEIhfEYYXJVjdFEgXR6gmq1tUNkhBDt1ZTvw7xeLy972cua8VBCCCFayO/3MzQQJeqxySfHyRZbMwlve6DKvYdy3DpYP7nOcuDhSz4+cTpAxuysOmdVVQkYIaIjOlN5i2vxPJlMRoKzEFtUUwL0Qw89xFe/+lWeffZZisUijzzyCB/+8IdxHIe3ve1tRKPRZjyNEKILON4Q1vARHF8YpZhGGzuGUsq0e1linpld56GID93MkRyLY9v2ynfcoIDL5m035nntriKuhoz8LwmdoycMns10Vls6VVUxgmH8fTEq3hgXLpxlcnISy7JWvrMQomdtKEA7jsN//I//kb//+7+f/bOiKHg8Hr73ve/x9NNP09fXx9vf/vamLFYI0dmsoYOY+18OSv1r9+rOW9FPP4I2frKNKxONfD4fQwNRYl6HYnqCZD636c+p4vDyHSXu3p8j4qmXh4wXVD580uD7Yx7ooLZ0qqoSDEXQ/EGyFYWJeImwOUkymZTwLITYWID+5Cc/yTe+8Y1Fr7vjjjt46qmn+Na3viUBWogtwPGGFoRnABQFc//LUdNXZSe6zRRFIRqNMtwXwGPlSY7HsVsQBg/3Vbj3cI4bw/Vyh5IFXzgX4MFzfsp2BwVnTSMYCqP6QmQrCvGJLJlMBl3XCQaD7V6eEKJDbChAP/jggyiKws0338wb3/hG/uAP/mD2ut27dwPMnlgohOht1vCRheF5hqJgDR/BdeGx1i5KzJrZdY56oZSeYKoFu879Xov3HMjxkuvm9pD+7jUPHzlpMFnqnPHbmuYiGA6jeINkKgqJ6eDcirIWIUT32VCAngnHv/mbv4lhGHOu6+vrAyAej2/kKYQQXcLxhVe4PtSilYhGqqpO7zr7cVfzpFqw6+xWHd54Q4Ff25PH2/CvzLm0i6MnDI5NdU5busbgnCrD1HgtOLeq97UQojttKEC7XC4qlQr5fH5BgL548SKAtLITYotQiukVrpfyjVZr7LBRbMmus8MLhsu872COYX995zZdUfj4aYN/uOTF7pA65/nBOTGWIZvNSnAWQqzKhgL0vn37ePLJJ/m///f/zqlz/slPfsLRo0dRFIUDBw5seJFCiM6njR2juvPWxcs4HAdt7FjrF7VF1TpsRBmK+GsdNsYTm77rvCtY5d5DWW7prw8SsWz42kUfn34mQK7aGW3pFgvOmYx8uBNCrM2GAvSb3vQmfvazn3H+/Hn+63/9ryjT/3C+853vnO3I8aY3vakpCxVCdDallEE//cjCEwkdB/30I3ICYYs07joXUuPkCvlNfb6gbvOOfXledX0RreGwPzGpc/+JIJdynTF+W9U0QuGIBGchRFNs6DfbG9/4Rn784x/zla98BWA2QM98Bfa6172O1772tRtcohCiUy3W89nz449NXxZCKWakD3SLzK913uwOG6ri8KqdRd65L0/QXS97GC2oPHAiyA/H3XRCW7rGrhpSqiGEaJYNbw386Z/+KXfeeSdf/epX54zyfs1rXsNdd9210YcXQnSo5Xo+S7eN1prf13mza51vjlW491CW3aF6QC9WFT531s/fnfdjdkBbuvnBWU4OFEI007oDdKVS4amnngLg4MGDvOIVr2jaooQQnU16PneGVvd1HvJZvO9gjheOzG1L960rHv7mlEGi3P62dI3BOV2GhARnIcQmWHeA1nWdd73rXTiOw5/+6Z+yc+fOZq5LCNHBpOdz+3m9Xob6o8T8yqb3dfZoDr+2J8+bbijgbsjIz6Rc/PXxIKdS7R+/3XhyYLoMUxM50um0BGchxKZYd4BWFIWhoSHGxsaIRCJNXJIQotNJz+f26uvrYyQWxOcUSI3HsazqyndaF4df2lbmvQdy9PvqbemmSiofOx3gH694cdpc5yx9nIUQ7bChGug3v/nN/J//83/4xje+wR133NGsNQkhOpz0fG4Pt9vN0EA//QEVMxsnkd2893lvyOS+wzkOR+tt6Uwbvnzez+fO+im0uS2dprkIRSLgCZIqO3JyoBCipTYUoIeGhtixYwdf/epXuXLlCr/0S79Ef3//bDeOGa973es28jRCiA7TzT2fF+sc0g312n19fQxHg/icIpmJONWqufKd1iHstnnX/hyv2FFCbTi8Pxp388BJg6v59ralW9jHOS3t6IQQLbeh34Qf+MAHZsPyE088wRNPPLHgNoqiSIAWosd0a8/n5TqHaOMn27iypXk8HoYGYsT8tV3nqU3adXYpDq/ZVeTtN+YJ6PVd3Ms5jQ+dMHh80rMpz7ta0sdZCNFJNryVIF+XCbE1aeMnUdNXu6bnc7d1DlEUZXrX2cDnFEhPJDZt1/nWgTLvP5Rjh1Hv4JE3FT59JsDXLvioOu2rc5Y+zkKITrShAP1bv/VbzVqHEKILKaVM13Tb6KbOIY0dNsqZSRK57KY8zzZ/lfcfynHbUGX2MtuBf7js5ROnDVKV9tU5Szs6IUQnW3eAPn78OMFgENM02bdvHy960YsW1D4LIUSn6IbOITO7ziNRA49dIL1JHTb8Lpu37i3wut0F9IaMfHxK5+hxg7OZ9rWlUzWNYDCM6pfgLIToXOsK0B/4wAf40pe+NOey5zznOTzwwAOEw8v/IyWEEO3Q6Z1DZmqd+/0q5cwkU5uw66zg8NLtJd69P0/UW29LFy+qfOSUwXeueWjX+O3GHedMRSExkZU+zkKIjrXmAP3FL36RBx98cMHl//Iv/8IHP/hB/uRP/qQpCxNCiGbq5M4hkUiEkVgIP0XSm9Rh40DE5L7DWfZH6jvaFQsefNbP357zU7LaU64xfwCK7DgLIbrBmgN0Y3jevn07hmFw+vRpHMfh7//+7/njP/5j3G53UxcphBAb1YmdQ3RdZ3Cwn8GAhplLkMgsv0u+HlGPxbsP5HnZ9tKcy78/6uHDJw3Gi+0Zv+1y6QTD4dk+zjIARQjRTdYcoM+cOYOiKLz5zW/mj//4jwH48pe/zO/+7u9imiYXLlxg3759TV+oEEJsVCd1DgmHwwzHQhhKmczkOKZZWflOa6CrDq/bXeDX9xbwueqh9HxG40MngjyZaM9Gh667MUJh8BgkSzZT0sdZCNGF1hygc7kciqLwyle+cvayV73qVfzu7/4uAPl8vnmrE0KIJmt35xCXy8VAf4zhkBsrnyKRTjb5GRx+cajCPQdzbAvU29JlKwqffCbANy75sNvQlk53uwmG+7BdfqaKFolrSXK5XMvXIYQQzbDuLhweT72pfmPJhnz9JkT36NapfN3KMAyG+/sI6ybZxDUq5XJTH3+nUeXeQ1l+fqBeQ2058NAlH584HSBrtr7O2e3xEAz3YWo+JgsWUxNTstEihOh66w7Q999/P9FodMXLFUXhv//3/77epxFCbJJunMrXrTRNIxaLMhz2opYzTI1NNXWzwXDZvH1fntdcX0RryMhPJXSOHg9yIdv68dserw8jHKai+hgrVEkmExQKhZavQwghNsO6f6t+73vfm/PnmR7Q8y8HJEAL0UTN2DXutql83SwQCDA8ECWiV8klxyiXik17bBWHV+ws8a79OcLueiAfL6h8+KTB98da35bO6/MTCEWoqF5GcxWSqTjFYvNesxBCdIJ1Bei17JzIcBUhmqdZu8bdNJWvW6mqyuDgABGvgquSJTk+hW3bK99xlY5EK9x3KMeecL0tXcmCvz0b4MFn/VTs1v7u9QcMfMEwJTxczZZIpScolUor31EIIbrQmgO0jO8Woj2auWvcDVP5ulkgEGDbUD+lqRLJ8VGyxeaVLgx4Ld57MMeLt82tn/7OVQ8fOWUQL7W2LZ0/YOALRSg5bi5niqTT45SbXNsthBCdRgK0EF2imbvGnT6Vr102Wh6jqiqxWIydgyECdoZrY1cpN2kX1qM6vHFPgTfvyeNtyMhn0y6OHjc4nmxtW7qAEcQbjFCwdS6lCqRSCUyz+QNghBCiE7X+zBIhxLo0c9e4k6fytctGy2P8fj9DA1GiHptKapysEm5SyYbDi0bKvPdAjiF//fHSZYWPnTb45mUvdovqnBVFIRAM4THC5C0XF5MF0um4BGchxJYjAVqILtHMXeNOnMrXThspj1EUhVgsxnCfH93MkRyLo7lc0Lf8B57V2B00ue9wjpti9YBateFrF3x8+kyAfLU1belUVSUQDOEOhMhVNUYTBdLpCarV6sp3FkKIHiQBWogu0exd406aytdu6y2P8Xq9DA/GiHkdiukJkvnaYJCNViGHdJt37s9z184iWsOyHp9w86GTBpdzrfnVraoqRjCMKxAiZ6pcmcyRyWSwLGvlOwshRA+TAC1El9iMXeN2T+XrFGstj1EUhWg0ynBfAK9dIDk+id2EUKkpDq++vshv7MsT1Ovdjq7lNT50wuBHE25a0ZauMThnKirxiSyZTKapXUSEEKKbdX2AvnjxIg888AA/+9nPOHfu3GyLvaeffnrOtEQheoHsGtc1c4riWspjPB4PwwMxYn6VUnqCRL4546hviVW473CW64P1IF6oKnz2jJ+vXPBjtqAtnQRnIYRYna4P0GfOnOELX/hCu5chRMvIrnHzpyiutjymr6+PkVgQn1MgNR7HsjZeAzzss7jnUJbbhytzLn/kspe/OR0gWd78tnQSnIUQYm26PkAPDg5y3333ccstt/BXf/VXPP300+1ekhBiE23GFMWVymM8domh67bRH1Axs3ES2Y3v+Hs1m7fsLfDG3QX0hox8Kuni6Ikgp1P6hp9jJY3BOVtRmJzISXAWQohV6PoAfdNNN3HTTTcB8NGPfrTNqxFia2lmGcVqbdYUxaXKY/q8KsM7hglQIj0xSbW6sZZtCg4vua7Eew7kiXnrQXWqpPLRUwEeverF2eQ6Z0VRMEJh9ECYnKkyITvOQgixJl0foIUQ7WENHcQ8eBfofhxVQ7Etqtffhn7q4XWVUazWZk5RbCyP0XWdwcF+BgMuqvkpEunUuh93xr6wyX2Hsxzsq5d+mBb83Xk/nzvrp2htblu6xj7OWVPjymSOdDotwVkIIdZIAvQatfPERF3X5/xXdL9uPaa2J0j5Ob+K46mHVQfAHaD6nNfiLU6ilrOb8+RmHkdZOmjqZmHDP6fBYJDhWBhDLZNJTmJWKuju1U/603XXnP9G3Bbv3JvmZdfNHen9owkvH30mwmjRBRpzSjmaSVEUAkYtOOeqGlfTeTKZFJZldd3fvXbp1p9VsTQ5pmIjJECvga7r7Ny5s93LYGRkpN1LEE3Wbcd0PHYLWV940UIDxxfBd+glDCWe3JTnNl1pLrhdwGIh2maXnkZf58+ppmlE+yLEfCp2KUUuU2KgP7butQ4PxrgzOs6rBsbxafVd3tGSly+M7eREPozSB9v61v0Uy1IUBZ8/gDsQpmC7SeZKmNkswWCQYDC4OU/a47rtZ1WsTI5pbzlz5kxLnkcC9BqYpsmlS5fa9vy6rjMyMsLo6KiMzu0R3XpMcwMvwbGdJa9PqH2UN/FnRc06lPa8BBp3oh0b77lvMzp5el2PaRgGw7Ewdvoqz56PU6mU170+XXfxkt06bxg4zzZ/vVwjZyp87tkQ37hsYDkFoLD0g2yAoij4jSA+I0w8VSVx6TLpdFoGoGxAt/6siqXJMe1NrZqQKgF6jcrl9f+j2iymaXbEOkTzdNsxXalm1rbtzX09V55Gj19YcMKfXcqw1mfVNI1YLMpw2ItaSjCenJrtJ78e1wWq3HekwK39pdnLbAcevuTlE88YpCsqsDn/WDfWOOeqGpfHajXOMnK7ebrtZ1WsTI6pWI+uD9CmaZLNZmf//4xUKoWu6/h8Pnw+X7uWJ0RPUuPPYsd2s/hUPAc1/uymr6EZ/bD9fj/DA1H63Ba55BjlUnH9j+WyeduNeX51VxFXw8b4sSmdo8cNzmU2r85ypquGOxAia2pci+dlx1kIITZR1wfoJ554gne+850LLr/jjjsA+K3f+i1++7d/u9XLEqKnua48TvX6W8EbZm6IdqCYxnXl8XYtbVVUVa3tOkf8uCpZkuOJdXeiUHB4+Y4Sd+/P0eep71xPmTofORnk0csamzV+e6aPsx4IkjU1rkpwFkKIluj6AC2EaD2llMF97GtUDtwFbj+oGtgWVAq4Tz3c0aPFvV4vw4MxYl6HYnqC7AZGcR/qq3DvoRz7IvUSibIFX7oQ5IflG7kwPgFUln6AdWocgJIzVS5P1gagSHAWQojW6PoAfdttt3H69PpOGhJCrJ82fhLvIoNHOjU8K4pSG8UdNfDaBZLjk9jrDJz9Xov3HMjxkuvm1k3+06iHD580SFo+tm1rfk86VdMIBsNo/hCZiiIjt4UQok26PkALIdqnGXXIreB2uxka6GcgoFLOTJLIra9Hta46vPGGAm/Zk8fb8NvzfEbj6IkgTydqvaL11beMXhVV0wiGwqi+WnBOSHAWQoi2kgAthOhpkUiEkVgIP0XSE/F1juJ2uH24zD0Hcwz766E1U1H4xOkAD132YTvNr3PWNBfBcBjFGyRdhsR4LThvpEuIEEKIjZMALYToSfVR3BpmLkEik17X41xvVLnvcJZb+uvB27LhG5d8fPKZADmz+eO3Nc1FKBIBT5BU2WFKgnNbOd4Q5e23MDq4g7IzgnPlyY4tVRJCtIYEaCFEzwmHwwzHQhhKmczkOKa59hP5DN3mHfvyvHpnEa0hIz8Z1zl6PMjFXPN/fbpcOsFwGDxBkiWbqbE0mYwEtXayhg5i7n85qqqRc7upbB/E3nYL+ulH0MZPtnt5Qog2kQAthOgZuq4z0B9jKKhj5VMk0sk1P4aqOLxyR5F37M8Tdtd3fEcLKh8+EeSxcTfNbks3PzgnRlOz/e1F+zjeEOb+l4My73grSi1Up6/KTrQQW5QEaCFETwiFQgzHwgS1Cpn4VczK2nednxOtcN/hHDeE6m3pSlX4/LkADz7rx7SbG5wbSzUkOHcea/jIwvA8Q1Gwho90xUm0QojmkwAthOhqjbvOdiFNYnJqzY8x6LO452COF47MbUv36FUPHz1pkCg3tyVd/eTAkATnDub4witcH2rRSoQQnUYCtBCia4XDYYaiIYJahWziGpVyeeU7NfBoDr+2J88bbyjgacjIz6RcHD0R5GSyueO3VU0jFI6geIOkypBYY42z4w1N990OoxTTHd13uxcoxeVPPFWK8t4LsVVJgBaiR/Vy2NJ1ncGBGIPGenedHV48Uua9B3MM+Opt6ZJlhY+dNnjksheniXXOjX2ca8E5QzabXVNXjZmT2RpLCqo7b8V1/gegeXryOLebNnaM6s5bFy/jcBy0sWOtX5QQoiNIgBaiBy0Vtnqhc8Bshw11fbXOe0Im9x3OcSRab0tXteErF3x85kyAQrV5belUTSMciaL6Qxvq47zkyWy6D/OmN0A+juLUPgj0ynHuBEopg376ken3vuEKx0E//Yh8UBFiC5MALUSP6dXOARvtsBF227xrf45X7CihNrw1P5lwc/8Jg6v55v06VFUVIxQmgpt4rkpiIks6nV53H+dFT2ZTNBxvCFBA90MlN315dx/nTqONn6y9l9tvITi4nezEFekDLYSQAC1Er+nFzgEb6bChKQ6v2VXk7TfmMfR6gL2S0/jQSYOfTHiatk5VVTGCYXyRGCVXH+cn4sTj8Q2P3J49mU3RQPfhqBqoLma3RdV5Jzl26XHuVEopg+fyjxhWRqlcvkR5jbX2QojeIwFaiB7TS50DNtph4+f7y9x7OMdOw5q9rGAqfOZsgK+c91Ft0vhtVdMIBsNo/hCZisJ4vEikGiedTm84PMP0yWwub33HufaktQ9Ktg2qC8cbRrEtMIvgWF11nIUQottIgBYt18snt3WCXukcMLPrHHKZZNbYYWPEX+X9h3L84lB9p9p24JErXj5+OkCySW3p5gfn+EStxlnXdUKh5gVYNXkBx/tqQAFFbfiGQakFacdVC9EAbj9KKdM1x1kIIbqRBGjRUr18clunaEXngM38EKTrOv2xKENBN04xTXwNu84+zebXbyzwut0F9IZzAU8kXRw9HuRMujlt6WaCszodnBPTwbkZu82Lsft2gVkCb3CRa51aqHZmnlvB8YZQkxc2ZS1CCCEkQIsW6tWT2zrN3M4BDe91kzoHbOaHoDm7zlOr33VWcLjzuhLvOZAn6q2H2HhJ5aOnDL591cNy47cdj4E1cCOOJ4hSzqJNnkEp5xbcTlVVgqEImj9Eejo4b+TkwNWyg4Oge8Gq1ks3HEBxqO1KT/+59mqglMHu24Wavrap6xJCiK1KArRomV48ua1RJ5WmzHQOqK0nhFLMNGU9m/UhaM6ucylNIp5cdSjdFzb5zcNZDvTVx2+bFjx43s/nz/opWcu3pbP691Ddffuc12SNHMF1/jG0+DlgbnCeKdVoRXCe4biN2i5z48mCs+t1wLagWqr91yygOLbUQAshxCaSAC1appdObpuvE0tTlFKm6R9INuND0Mw0wbXuOvd5LN69P8/Ld5TmXP7YmJsHTgQZK65c5+x4jAXhGQBFobr7dlz5SUIe14Ia580q1VhStdwQnue//wpYFZTS3Np3qYEWQojNIwFatEyvnNw2X6eWpmzGjngzPwTNnya42lpnXXV43a4Cb72xgN9V3wG+kNW4/3iQJxPuVa/BGrhx0Q8EqqIQNPx4D91B7uLx9gXnGS7PdLeNJT4UKPN22WVKnhBCbCoJ0KJlenUsbieWpmxkR3y54N2sD0GRSIShaJCgWiG96r7ODrcNVrjnUI7rAvW2dFlT4VPPBPj6RR/2GtvSOZ65J+WpioLh96L7gqRtH1eKSQqXrrYvOE9TrDKoKvVCZ2Xu/3ca1idT8oQQYtNJgBYts9knt7VLp5WmbGRHfKXgvdEPQW63m6GBfvoDKtV8kng6tarXtD1Q5d5DOW4drAdty4GHL/n4xOkAGXN947eVchYAVVUI+X1oXoO07SOes0kXiqgTo7jaHJ4BHM1Tq2+e3YFuqL22qyiFJNrk6abVugshhFieBGjRUpt1cls7dVppynp2xB1viOr2W6nueQGKbc8O45i5T2PwXu+HoL6+PoajQXxOkexkAtNcedc54LJ52415XruriKshIz+d0Dl63OB8dmNt6TzJ83j23YbiDZGy3CRyNplCEdtxaq+pQ74VUcrZ2i6z5dT7QDvO9M6zgzp1Hv3kw+1ephBCbBkSoEXLbcbJbe3UaaUpa90Rn9l1djxBcPnmDOOgOn2CXkPwXuuHII/Hw2B/jP6AiplLMJVZ/gMHgIrDL+8o8a79OSKe+m7reEHlwycNvj+2fFu6lei6GyMUBo9B4vIzjA09j2ypyGxTjQ77VkTNTWKVMuAN1ULzvJZ1ai7ezuUJIcSWIwFaiA3qtNKUteyIzyn3mHOCWm0Yh5I3Z3eiG4P3aj4EKYpCJBJhJFbbdU5PxKlWzRXXf7ivwn2Hc+wN19vSlSz4wtkADz7rp2xvMDiHIzh6gKmixdRoimz2ezjeJ9E6+FuRmQ9pjl0Fb7h2rGwLSmkUy+za8weEEKJbSYAWogm08ZMopTTm3jtnT77Tzz7alkEWa9kRn1PuYVvzbqyA7oNKbaDIWkpRPB4PQwMx+v0qlWycRHbl+/Z7Ld57IMcvXTe3jd13r3n4yEmDydL6x2/rbjfBcB+Wy0+iaDEVT5LL1QeldPq3IkopgzZ+iureFzO78666wB9FO/vdjgr7QgixFUiAFqIJrKGDmAfvAt2Po2rgi1C55S3opx5eVR9o2xOkOvLcprScW8uO+JxyD7MAbj+NpRGOqtX+tIZSlJlaZz8F0hOJFXed3arDG28o8Gt783gbMvK5tIu/Pm5wPLn6tnQLHtvjwQhFsFx+JgsWUxNT5PP5dT9euzjeENbQAZR8AnRf7bjYFphFrKEDuC7+s4RoIYRoIQnQQmyQ4w1hHnktjjfETPicqSM2j7xmxT7QGWMn+W2vxm7orLDRISyrrlM2y7UpdzMlAeUceIzZ16HY1qpLURo7bJir2nV2eOFwmfcdzDHkr3e6SJcVPn7a4B8ue7HXWefs9ngIhvswNR+TBYvEeIJCobCux+oEs98UOBZUcnPflR6Y4imEEN1GArQQG1TdcWs9PM/rkOB4w1RuegNqdnzRnWXbE2R84LlQMaFxLHQThrCsVJZgDR3E2n7zvF1npxaiFRUUFe3CD3FdfnzFNUQiEYZjIQKUyEzGV+ywsStY5b7DWW6O1XenLRu+dtHHp58JkKuury2dx+PFiESoqD7GClWSye4OzjM6rVWiEEJsdRKghdggO7anFjgbT8JTFKAWAu3+PbMBaP7Osjl4iCW7SWzizuLsyYMoYJmge2vb5o5d24HOxXGvovxE13UGB/sZDLio5qdIrNDXOajbvGNfnlddX0RreNlPTOrcfyLIpdz6fiV5vD6M8HRwzldIpnojOM+Yf2Koo6ig++vfHKyiJaAQQojmkQAtxAY5qrbEiGVl3n9ZsLNse0Oo1ANRY20rjtWUncXFJgtaw0dqtbQzO+e2Pf0hQAWzhOvaUyuG51AoxHAsTFCrkFlhmqCqOLx6Z5F37MsTdNd32kfzKh86GeSfx92spy2d1+cnEIpQVr1cy5ZJpScpFotrfpxWW+uY9cYTQx2Xt9bOruFbA+u6m1Gzo+su+VmPzRgVL4QQ3UICtBAbZZaWv76xNAPm7CyrpQyW6sb217+ib+zDvNEhLEtNFlRK2Tk127Unnu4vrOkLRlw3crlcDPTHGA65sQtpEpNTy67h5liFew9l2R2qd/koVhU+e9bPl8/7MdfRls7nD+APRSjh4Wq2RCo9Qam0wnHoEOsZsz5zYmjlwF0LwnMttNobLvnZ7NcghBC9RAK0EBukllJYc8Ysw5yaYru64D4zO8ta6hIl3Q/2vJA93YdZTV5Y97qWG+ltx3ax9I6vsmSADgaDjPRHCLpMsolrVMrlRW8HMOSzuOdQjhcMz73NP17x8jenAkyV196Wzh8w8AXDlPBwOVMknR6nvMwaOs1Gxqxr4ydxhUawdj1/wTcVM4/RipMJN/IahBCiV0iAFmKD1OwEdjFV29FVtOlg4dT+a1soiwTomZ1lK7IT3SxQ0XzzblGbMGf37Vp3L+llR3rbNmjq9Cjo+RyU8twA5HK56I9FGQp5UMoZpuJTOPN31qd5NIe37MnzxhsKuBsy8umUi6PHg5xKrX38tj9g4A9FKDpuLmeKpFJjVJYpGelU6xmzPofLs7ALR4NWnEy44dcghBA9QAK0EBs0U5+qWOZsj14cu3aSF05tl7BRQ09l2xtCsyuo5QK2y9twUlgBxbE3FIiW7dxgV8Gq1IZxzOscMn80dOOuc25qlHJ5qVIJh5dsK/OeAzn6ffVgPlVS+djpAP94xYuzxjpnf8DANx2cL6aKpFKjmObK0ww71Ua7acycTLjwJMLa35eNlvyshnQEEUIICdBCbNicwSUNu4OOVZkeQtIw4W9eT2V1+r+KY6NUcsy3kUC07Ehvs1ALzwtOfnRQqAV8Xddru85BN04pzVQ8ueSu896QyX2HcxyO1sOtacOXz/v53Fk/hTW2pfMHDPzhPgq2zqUeCM4z1jJmfTHa2DHMPS+sjfNu/DDi9uOU0i0Z6b3R1yCEEL1AArQQTbDU4BJg2WEm+sQJzBtuW/xB1zD9b9E1LTvSm9oOtMtdP3mw/rSEgkFGjDAhl0lmaula57Db5u79OX55Rwm14Wl+NO7mgZMGV/Nr+xUTMIJ4gxEKts7FZJFUKt4TwXnGcsfEUVxYg/uobv95lGJqyVHwCgqLfYxZ38iZtVvLqHghhOhVEqCFaLp6sFhpmIlazjI0+QSXQ0fmXrHK6X/LrmKZkd5a6iJ23w6camlOKYBulxgKuRk5eDPas98nscSus0txeO2uIm+7MU9Ar19/OafxoRMGj0961rRWIxjCY4TJ2zoXkwXS6dUH525qp7bUMXE8QXAHsAOx2p/D2ygPH8R19rvop785eztr+AhUSyh5c8FIbxyrJfXHaxkVL4QQvUoCtBBNsJG2XqHcJQLnnqIY3bf82O01crwhHF8favISjttAKddqm7WxY1R3vwColY4wXToS9nsYMXSCToZcIYeTWrw93a0DZd5/KMcOo16akjcVPn0mwNcu+Kg6q9sLVRSFwHRwzlU1xpJFUqlJqtWFJ10upRvbqc3/tsJxFKw9L1rY7hCF6t4Xo40dm92Jnq0/XmykN62rP171qHghhOhREqCF2KBmtPVSy9mm7hwuFiydQBRt8sx0f+l6HavbpTIY8jLkLlMtpInni2i55IJfDtcFqtxzMMdtQ/XuF7YD/3DZyydOG6Qqq6tznh+cRxMF0un0moIzdHc7tcZvJsrP/Y3lbom59048P/1U7U8dVH+80rcrQgjRyyRAC7FBndbWazZYqq4FX/PPBMuZOtaI4WUkoGI4adLJLGbVqpV4TJ6ZfTy/y+bX9xb41d0F9IaMfHxK5+hxg7OZ1bWlU1WVQDCEOxAia2pci+dJp9NYlrXynRfRae/7ejm+yArX17teSP2xEEJ0BgnQQmxQp7X1WjCmm7nTDa3hI/iu/oRtqZ/RN3QrVilDPD/das9xcJ1/DKWcQ8HhZdtL3L0/T9Rbb0sXL6p85JTBd655WM2pa6qqYgTDuAIhcqbKlckcmUxm3cF5Rqe97+ulFFM44W3LXF/fdZb6YyGE6AwSoIXYoE76Wh3ADg7Ww/O8Hs+ON0RweCc71csY+dOk/+lJSpFdqB4DpZyrlXiUcxyImPzm4Sz7IvWyiooFDz7r5/PnApSttQfnyckc6XQa215seMvaddr7vl762UcpDx9k8Q8jDvrZR+dcIvXHQgjRfhKghdigTvta3XEbteDc2ONZUdA1jaGAxrBqoF1OkUgnAXDlkrM3i3os3nNzjpdun9u27vujHj580mC8uPL47XpwDpOpKMQnsmQymaYF5xmtet83u8uHmr6G6+x3qe59MXNDtIPr7HcXb2Un9cdCCNFWEqCF2KCO+1q9Wl4wICXs0xnxOwSdDNn4ZZR0cs71uurw+t0F3rq3gM9V7wZxPqNx/4kgTyXcKz6toigYofBsjfPEJgXn2edrwfveqi4f+ulv1oak7L1zNqgv1QdaCCFE+0mAFqIJOuprdZenNt5Z1fC4VAYMnSFXnmoxQzxXRHU06nHY4flDFe45mGUkUA+62YrCJ54J8PeXfNiraEtnBEN4ghGypsaVJpdqLGcz3/dWd/lQ09dmu20IIYTobBKghWiSTvlaXankULDp8+qM+MFvJ0hPTXfYwIFKHoCdRpV7D2X5+YH6wBLLgW9c9PGpZwJkzZXb0s1MDsxZLkYTBVKp1IZPDlyrzXrfZ7p8OIo6Z9gMZgEFu2u6fAghhGg+CdBC9Bh/JUW/3yTmt6jks8SLM/XMDko5R6g8zt2HsvzK9UW0hoz8ZFzn/hNBLmRX/rXgDxj4QhEKtpuLycKaB6B0A8cXxnF5oaGbCQBuP04ps+ouH900KVEIIcTqSIAWokeoqkpfXx/DrnEUc5TU1RKWqsN0H2i1WuTV7p/xnn3fIeyul1eMF1QeOGnwg7GV29J5fX4CoQglxculVJFUanTVI7e7jlleGJ6h9mdvCMzKYvea+xAD+ynf8EtdNSlRCCHEyiRAC9ED/H4/g/19xLwOxfh5MvkC1u7bQTFRgJv0y/xW+FH26pOz9ylZ8LdnAzz4rJ+KvXxw9ni8BMJ9VDQfVzMlppJjVCorB8iutlLp9wrXmy4/pT0vYboLd8P9On9SohBCiOVJgBaii2maRjQaZTjiw2XmSI4nsC0LLZ9DzY4T27ad928/x4tDV+fc79tXPXz0lEG8tHxbOrfHQzDSR0X1M5arMJWcoFQqbeZL6hwuD0opM2cgTY1TC76u5TuTpIO76j245+uiSYlCCCEWkgAtRAdZS71sIBBgeCBKxG1RSI2TLeRnr/OoDm/aOcab9zyLpyEjn027OHrc4Hhy+fCnu92EIlEqmo+xQpWpqUmKxWJTXmO3UIppqJZQ8uaCkeg41oqDWkzdWPb6bpmUKIQQYiEJ0KKptvIJUxt97avtOayqKrFYjOGIF1clS3JsqqFlnMMdI2XeezDHoK9e55wqK3z8tME3L3uxl6k9cHs8BMN9mJqP8YLFVDJBoVBY/ZvQQ2YHtWBBJTf3XVvFoBbdzIE+uOT13TIpUQghxEISoEXTtGroRCfa6GuvBkeo3PR6UF31VmmOvaBe1u/3MzQQJeqxySfHyRbr4faGkMl9h3I8J1Y/qa9qw1cv+PjMmQD56tJt6RqD81ihSnILB+cZGx3UEs5eYNy3a/Er2zChUgghRPNIgBZN0eqhE51ko689Y+wkv+sXwR2oXzjdKk2plmq9iLc9h4HMaYb7/OhmjuRYfHbXOey2ece+HHftLKE1LOHxCTcfOmlwObf0j7nb48GQ4LykjQxq0asFvOe+TWFeF462TagUQgjRND0RoMfHx/mLv/gLvve975HNZtmxYwe/9mu/xjvf+U5UdeVhEGLjZoZOLKrHT5jayGu3PUHGB54LyvwfxVqrNCdfwa+rXHf9DoaunqeYniCZzwGgKQ6/cn2Rt+/LE9TrJ6pdzWs8cMLgRxNuQMHxGFgDN+J4gijlLNrkGbxUCYQjVHz9XAvsJWFqFM1JNPvYis0ntpqNDGrRJ0+jl4uYB181+/7rJx9CmzzT5FUKIYRopa4P0IlEgre+9a1cu3Zt9rJz587xwQ9+kPPnz/NHf/RHbVzd1uH4witc37snTG3ktZuDhwAFnIXT+1RFIRaLMqJl8F2+RnL8Kvb0lL+f669w76Es1wfr9ytUFT57xs9XLvgx7VpwNnc/H7v/RnBsqJbwulSMQy/AuXqc0SKMD/0ChYoFPiC0a8uU3LRKce+dVHa9kJkuHo7bT+V5d+M6+130099s7+KEEEKsW9dvz/7lX/7lbHj+b//tv/HDH/6Ql7zkJQB87nOf4+mnn27n8rYMpZhe4fre/bp69rUrGrgNHG8Y3Ebtzyz/2m1vLVwrZpHGfsGGx8UNUZ3d3gJ2doL0Mz/GtixG/FX+4Lkp/vttqTnh+ZuXvbzvO1G++GwA01aw+vdQueVN2MNHwOXF7QsSG96BO7qDMaePE+Ff5Hz01lp4nvNiamUnjrd3P/C0SsETo7zrRSw2iKW698XY4W3tWJYQQogm6OoAbds2X//61wHYvXs3b3rTm4hGo9x7772zt/nqV7/aruVtKdrYscX73ULPnzCljR0DlwcnEMNxB8DlxXEHcAIxcHmWfe3qdB2s4ti1UgEVhsNe9garhMw4icQklZPfwV/NcPf+HPffMcXtw/UBJqeSLv7t9/v4i6dDJMu1wO54DKq7bweXF93lIhYK4AsPMKYMcKYU5HKyRMFxg+5ffFHTZSdiYyb7b1nmWgVz752tWooQQogm6+oSjsuXL5PNZgG44YYbZi9v/P8nTpxo6nN6PJ6mPt5a6Lo+578dxSmjPfud2uQ1peFzmWPjffY76E4Z2vjebSbb7aaCirPITqOCgtvtRnUWf+3q1BmKN9wGqkbICDAShBAJcukMpWIR/4mvcmfoIu+6JU3MU29LlyirfPxMmO+O+nFQ0BvaOpvDB3C73RiRMJYvxkTVQyJrkS/XBqAo7kB9d1x14ei+2p8dC8Usojg2qhFt69/1TmJ7gpiDh7C9IdRSBn3iBGo5u+x9dF0npxu1zeclPlcqgT55j7tIR//+Fesix1RsRFcH6Kmpqdn/bxjGov8/kUg07fl0XWfnzp1Ne7z1GhkZafcSllDEnPgO6eAuqq4ArmqecPYCuq8IHfC+bZZ43yFMzcExs1iaG0dRURwbzaqgaGAceAH9yaU/yCWK11B2PZchVxG7lCZRKOE4Ks8zn+D9t5zhBn99QIppK3wrMcRD8W2UFY2ReVUAmstFacc+CuFtTDohxnMucuXajrWiKtO30VEcG1t1Ybv759zf8QRwmQVifo3+Hj5mq5UxdtZO8kSZ/brOvOE2hiafIJS7tOx9L5o5St7YkiO/DdXsiN8nYm069/evWC85pr3lzJnWnKTd1QF6KU5DKYGyVHeEdTBNk0uXlv9HczPpus7IyAijo6OYprnyHdrmFAAVYCs0RCt69lP1z5RV1HZ5HWBmvzhRsCgs8fcmet1uBnfegKd0gWSxgmlBH2nucX+Tuwb+Zc5tfzTh5aPPRBgtuoCJOde5XDpGKIzi8TAxVeGaaZEtx7H9sQXPaVVNMIu1EhN74fZoRfOSefYpCtnRtbwNPcf2BMlvezWOaS3Ypb8cOkLg3FNL7kTrus6A50nSgaUCsoN17GEubfH3uJt0z+9fsVpyTHtTtVptyfN0dYCORqOz/3+mlAMgn88veptmKJfLTX289TBNsyPWIWrs3BR2zF72+vnHy+v1MtQfJbprD2YxzmQ6i8up8BbfE/yG/58JqPU650tZjftPGDwR91CL5fXrNM1FKBIBT5DJkk1ifJLMuUcpP28nKApKKT19QuDMB0kHp5xHS17EUl0w57ra9ZQylI1tWPELG3tjulx15LlYLg94w7WyJEUBx8HR/VBKU4zuW7a9nb+cwHPhnyg3dOGocXCd/S5W/AILe6+ITie/f3uPHFOxHl0doHfs2EEoFCKTyXD+/PnZy5999tnZ/3/o0KF2LE1sIbMjnxf5tsNBxdG9mAdfiVJM4548SczvYjDsw2MXSGVz2O4iv6if5b7At9nhSs7eN2d7+Pjkfh766SiWM/exNc1FMBxG8YZIlmwSo6nZD5EK1CfoVUsoeRN0H46ioo0eRz/zj1R3vwClWsLJV2onE6ranAmIvdx2cLVsYwB8kdp7M0NRABV8EWyjf6m7zvKdfRSuPIW5987ZEe/62UdR09dWvK8QQojO1dUBWlVVXv3qV/PZz36W8+fP8+CDD/KSl7yE+++/f/Y2r33ta9u4QrEVLDXy2XF5UFCwtj0HgKDXzdBz72DgyncpX/gZU/kc28MR3u//Eb+g1z/02Q58vXQzf5N/Abmxc7icsdnrVE0jGAqj+kKkypAYS5PJLGyTt9IEvZnWe4pjQyW38DX1cNvB1XJ8fXPDcyNVw/Gt7tstNX0Nz08/1cSVCSGEaLeuDtAAv/3bv813v/tdrl27xu///u/Pue6tb30rN910U5tWJrqN4w1NB87aTuFqRzbDwsCKWcHafjM4Nm6XykDQy6DHRCklmAjsJszj3HMwy2t3fQ9XQ9OSpyvb+cvcnZyzBsFxcE9PrFNVlWAoguoPkS5DYjxLJpOZU+8/33IT9JbbNe/1toOr5bi9y1+vL3+9EEKI3tX1AToWi/G5z32O//k//+fsKO+dO3fOjvIWYjWsoYMLdpDXOpWvMbBWd92Ogk2f4WU4oBCwM2RSWapmhbu8x3jvC0bp0+u1zONWkPvzL+Y75f3UJhM6uM4/hlrJY4Qj6IEwmYpKfCJLOp1eNjjPWO4DwVK75jgO+ulHVv3BoZcpVhXHthbfhbYtFLs1J6oIIYToPF0foAGGhob40z/903YvQ3QpxxtaGCRhdiqfmr665kDpjQwwEAsQ04pUCmnihRKHXVf57cij7NPHZ29XtuCRxDY+djpAOmSiei6glHNok2cIuMA3soOsqXFlMkc6nca2lz5ZsdFqPhCsVOax1anxZ7Fju8Fy5pxEiGMDDmr82RUfQwghRG/qiQAtxEZYw0cWL2WA2al8y3VbaKRpGtFolP6ghWZPkcoU6dOK/JvIt3ipPrct3feuefj4uT706HWU7ZnQquD1eggNjVCqWFycKpBMJrGs1fdrmP1AoLpqJw+qGoptgVlc8IFguTKPrc515XGq199a68Lh2A0DURwopnFdebydyxNCCNFGEqDFluf4witcv7qOFKFQiKFoiJBukT//GFnvK3mr8SRv07+DT6n3GD1bHeT+JzWOjVvobhch/zYKh34JXdcIBgKUXQbPljQyT3wDO772vuPW8JFacG5oUecAuP0opcyaPhBsZUopg/vY16gcuAvcDZ1KKgXcpx6WnXohhNjCJECLLW+mI8XS1y8flNxuNwMDMQYDLuximqnEFM+/TuF94Q8xotUfO+34+Ujll/lG/gB66m9RyOG4A6QHnkPM48Z0GYyZOolkhWKlAje8BE/84pqDmh0cnNf7mdkSBMcXwQpfJz/4q6SNn8QrZS5CCCHmkX9HxZa3bEcKVBxXvY9zY3hSVZW+vj6G+gJ47SLZ+DjXeQr87vOy3NJf33G2HIWvVJ7Hx0p3kDUVFKeKNXAj7tGnCe75eegbYryoMp4qky83zG5cY/nIDMdtMBueFWVeH2MNe9sRrLFjS54cuZFuJL1IylyEEELMJwFabHlLdqRweXAcsK57zuxFMyfihXKXGOzvo8/jUM5MUimned++PK++vojW8BA/rezk/+Xu5ILVDzgoOKiKQqh/BI+dJOnbQSLrIpHOYTsLTxBcz0ATpZylVrShLNFBwl7y5MhmdCMRQgghep0EaLElrLSruqCPc7VCddvNKMwNtbruJvaitzGSO4UrdYn0+Z9yV/8479yXJ+Sut5YbLfv4q9Iv84PKXhpLKQyfF6/fIHctzbWJHGnnMs7u65Zc93oGmqi5SaxSpjZFr/5I02+EXfvfIrvbm9GNRAghhOhFEqBFz1vtrupifZxnr1OgLxxmMBIgaOfIloMcCDv81gtT7HHVJ/kVqwqfP+vnwWsD5I/snX1On8dNwDDIKwbn8w6FJ3+AnU+h28cxdz9v8YWvc6DJTEmK4/LWTn5TZia1OLX16D6wKgt2t2e7kSjagu4dYMnJh0IIIcQ0CdCip613V3W2M4eiYQRDDAQ9xDwW5VwctTTK/6d/nV/yn5hzn29d8fA3pwwSZQ0o4Dr/GNq+OzCCQcpagEtlF1OZIpz4B7R8EgC1nGVo8gkuh47MW8D6B5rMlKRUbnrj9OtuGLpiW7PvC9XKnPs5vjC4vEt271hPOYkQQgjRiyRAi5623h7PSjGN2+snFosxqFdQzSyFVJq3qN/mLb7v4FHqU+hOmUP81YVdnHnm/OxluttNTM1TvvoTJv17iFdUyunEoifkhXKX8F0+R+H6F86WmOhnH0VNX1v369bGT6Jd+SnWnhfPGwDSYP4ww2p5YfeO2ruxaOAWQgghtioJ0GLVurE7w3p6PCuKQj8posMR/HaKbCbPC3iCe/W/Z1Cpt6Wbcgw+XHkF/5Dfj2KdRuc8LpdOMNKHrQeYKFgkxi5RKJwClv5hyxg7KW57Ds50onWMGJWb37ThE/cUgGISFoRiB0oZ0N3z3owVHnDl6eFCCCHEliABWqzKZnVn2OxQvtYez4FAgMH+PkI7d5BPnqePCf5/7q/xHO3i7G1MR+PB6gv4VOVO8njB4+DGpi/WD54gyaLN5OQU+Xx+xfXZniDjA8+FilnbJZ5d2MZP3FOKaZRqCSdfAb1hEIhZQHHshSco6p5asF5t4BZCCCG2KAnQYkWb1Z2hFS3TZns8LzLWGrs6e5KerusM9McYMHTUSo5qIcF9kcd5pfdfUBte9j9bB/h/1ddyxe4H7FpLOr8Xd2yYVOoqibE0mczq3wtz8BALSyamrbMP9PzXrmBDJTf3ykVOUFxz4BZCCCG2KAnQYkXrrSNeTqtapimlDNr4Kap7X8z8E+NcZ7+LVsnRF4sxGAkQUMoUk9d4+UiSt+86jaHV65wvWTH+ynwtP+IQACoOht+L2x8gVXSYymoUL13FcdZW52B7Q6jLXL+RE/eW7G+9xAmKaw3cQgghxFYlAVqsaD11xCvZjFC+GMcbwho6APn43F3VapnAvtsZ3jFMtDJO5eJPuV69yL235dhhWLP3z9tuPlF4Pl+qvpCqHgQsgn4vHrdOxvZyaTJPJl9CnRzDtcbw7HhD2L4oliuAjQ6VAjjWnNtsdNd3fn/r5UZRrzVwCyGEEFuVBGixorXWEa/GZoTyxSwW1H1eDwPhPgZcJexsGHcmzr/5uRzP99Rfp+3ANycHeEB5I0kMHMXEZ7gI+APkqwrXUmVS+QSW7QAqyhLjvpdc13T5iqLpOJqOo9RKTJRSBqql6TehObu+axlFvZbALYQQQmxVEqDFimbriBfbMV5nyNuMUL4YxxeuDRTxhtA1lWjAzZDPxm2mqabivJWHeWPfD9GVeou3E0kXR48HOZNWcDzfxLvtEOGBbRTyKpdML8lcEdOavv0y476XquNuLF9RHBuXWaCi+ZhpF6fkTbCrbdv1XUvgFkIIIbYiCdBiRZvx1f5mhPJFmWUUX4g+v4chn43hZMhnc9xu/Yh73A8RU7KzN41bBg9c2c33jo0BCqqmEY71U+kbZrRqkLh6ifLUD6nuvA18YZRKHtsfQ8EBtzHnBMXl6rhndsUdRcV2B1BUHcUs1trYKSpKdgL38a+2bdfXDm/D3Hsnji+CUkxtuCe1EEII0WskQItVafZX+62qtzUCXmJRN31Knkohx0DlHH/s/goH9cuzt6k4Gn9buJXPFG6jUryKW5kgGIqgbr+J8R2/xGRZJ1OxYfcB2OuG0nS3ivB14DFqJyVO1z83Tu5bqo67cVfcQcFSlVoJx3S7OLUQb1t4Nvf/8twTLsPbKA8fxHX2u+inv9mWNQkhhBCdRgK0WLVmf7W/mfW2Ho+H/v4okSEDJ3cRpZrjX7ke5i7vE3Nu9z3zIPdnXsCoHQEgqFoEh7eT1SJcHnopyZIHx1FqPyna9I+LL4KTm6j9WdVql1nVhketlWLYxsDiizPLi/Rart0PbwjM9kz8s8Pb5oTnOoXq3hejjR2TnWghhBACCdCizZoeyjWNaLSPwbAfr1MkM3aaX9nxU37D/yP8aj2YnreH+L/l1/BEeRuKXcCtuwgGAthjWS6lTCZi11GyPMyGSaWh2ZyqgTsAitbwQtR5o7IVHE9wiRe9wotY6fpNYu69kzmvd84IcAVz7514fvqp9ixOCCGE6CASoEVTtXPcdyQSYbAvSNBVpZgZZ29givdff5xt/vrucMb28rHiHXyFO7DRUO0UfSED2x0kfvJHJM89S7lcprJnN3OS7PxabZdn7q6zoswbde2glDOLvh+4PCil2nXzJ/4ppQy4Nmfi30rHxvFFaq9DbfxgoAAq2NaKnVOEEEKIrUICtGiaVkwWXIzf72cgGiHqV7HyKQLFOP/uUJZbB+s7zpaj8PXSTfxN/gVkHD+QIVxN4vW4SY0niZ/5Kfn4aMOjzuvp7DgLQrRiV3FsqxY45/SAnh59rbooP+/uBe+HmroC1VKt24bbj+bSsarmbB/ozZj4t5pjo5RzOI3huZGqoZRXHk0uhBBCbAUSoEVTLDtZ8MBd2MER0D1N3ZX2eDxE+yL0B93o1QLWVJxfvyHFa64vojVUXDw15eXoMzHO+oZxPOMEMAmZCQpli0tTWVKp1IIJgmr8WezY9C70TDkDCrPBulqujQN3+8FywCzUbjc7+hqsvutrU/3mvR+1ywHHQq3k0XHjVCrYjr0pE/9mj80i48wbu4UouXGYnrQ4f2ccmL5eCCGEEBKgRVMsOVnQ5cXxhqjufj7K9Hjoje5K67peK9cI+/A6JQpT13jhYIp33Zwj7KkH4TErxNHci/le9UZcyg9xjz5NuC9GVQ9wtexm0rOD8nAAJbww1LuuPE71htunT/ab4QAK2BbKzG5xKV3bnLbKDTdzUJNXsKM7Fl2/go06dRm7b/u8nLo5E/+s4SO14NxQMrJYtxAFoFqplafMWzHVcq1dnxBCCCEkQIvmWLQ+VtHqoW1eXe1yfZKXomlaLThHAvjVCoX0OMOeJPf9Qo694Xo9cslx8ZnCbfxt4VYq6KCA7/CdBC7+gGQmz5jZT2rXi1YuNbEqYJm1kwVnT6izoFpBjZ9DzU3O7hbP7yRS3f2CZV+LUi3g+fHHULbfQnBwO9mJKzhXntyUenE7OLhIvTUs2i3E5Wb2g8IsZ/ryNp3dKIQQQnQYCdCiKRadLKjXpuuhKLWdaMNT270tZ8Ayl+yTPJ+qqoTDYQYiBkHdopSZRKmm+DcHcvzSdeU5t320dID783cwadd2jt26i3AwSE4Nck7bRWbyR5R+4Y2LlppUDr4KdedtOB6jVvPr2Dj5OOj+2gcA26qN2XZ5cYxByE3W7rpIJ5HZ90PRFpRNzNQ5K6UMnss/YlgZpXL5EuXy3NfSLI7bYOnwW+8WYhlDjfdacEvLGEJv+uqEEEKI7iMBWixqrd00Fpss6KgaqOp0u7fpQKbpOLoXSlkcX2jxB2sQDofp7wsRdtuY+SS5qRRv3J3nzXvzeBs2tc+lXfxl6gU87f8FAFRFIRz043jCXC3rTOZMTN9OuPn6WmCcDsKzwVjVwGPUdmMdu/ZnRYFSthb4oTb8xB8FFOzwCI7Hv2Q5ijZ2jOqeF+J4wywsm0g3vc55OUo5y8Jd5RnO7OvDY9Tfi/lsCzyBTVylEEII0T0kQIsF1tNNY8nJgo3huZE3yHIlAYZh0B8N0+dVsItpUmNJbh8s8r6fzzHkr5+Yly4rfPy0wT9c9lLZ7gU/+L1u/IEQU7afiZRFtmrjePtA8dTCoe4DX7gWCp16sAen3o5upuuGNwjVYq3bRkMZhGJb069v6XKUpSqGnRaXEqu5SaxSZpHhLdOTD3NxAJRiCie8rdaeb0Ef6CW+ZRBCCCG2IAnQYo5lu2msULc8f7JgdfBQbVdzCYuVBPj9fmJ9EWJ+DaWSIzOeZIe/xO89L8vNMXP2dlUbvnbBx6fPBMhXay03PMnz+A7eTlkPcb6oMZUtYaPiBCLMlpJo7ukd1um6bKvasOM63WVjXls6xxOqdd2YDZ9OrRSj4b2ZX45iDR9BqZYXloCYBRTHXnrM9yb00Z75dsCxKgvXYluzu+H62UcpDx+svU7HXtDXWj/76IbWIYQQQvQKCdBijiW7acCiQXHBTRrqga3Y3nqf5PnmlQR4vV5i0QixgI6rWiAbT+KlxH0H87xyZxGtYUmPT7j50EmDy7n6X99gOIIrEGHi/Amuxn6BUnW6B7TbV58i6A7UWrnVV1vfeZ69SGV2J3qGqjW8hulhJ4415+XML0eZOalScWyY7j6y3O1h8/poz/l2oHEt87p+qOlruM5+tzbOe96Jk66z35Ux3kIIIcQ0CdBijpWmza2mbnnGnJIAtSGQTZc/KMU0uq7T11drSeexi+Sm4uQqRX7l+iK/cWOeoLsebq/lNT50wuBHE/WOEB6PF6MvRs52c3kiSyr1Tzjep3BN74JbfbtqvZpRQFvqr/vy3SWUXKI2VTA4NHsS4MLXmpn35+XLHebf3vYE173zvxrzvx2Y6RYy/zHV1GWUQrLWtWN6p1oppVFTl9f93EIIIUSvkQAt5lhr8FuK4w2hFhLYqlbf1UWpZVXNhQuboamfMXz9CD7K5FNj5IoFbolVuPdwll3BekgtVBU+d8bPly/4Me1awFRVlXBfFNsd5Fq2QjwxhmnWSjwad8Ht5/4GzswwlMVXOr20xuDaUKoBqBMn0c//YMFUwfpDLBx+sthJlcvd3hw8tKGd/9VYrFvInGXNlO/YJkohMXd9TQjxQgghRK+QAC3mWGvwW8xMKcLcsdDTwVdRiPjdDGoF+gNlipMTJPI5hnwW//G5OV4wPLeV2z9e8fLRUwGS5fpj+QMGvnCUZFllYjRJLje3RKKxjtjxGiwoyZhDAexFQvT0dZUCynTZxqInSS4x/GStt7e9y+/sr2Xnf702Wr4jhBBCbBUSoLvUak82W+tJabPB78Bd4PbX+xdXCuinH15xB3LO2Ghf35yd35BPZ9AHESWLWcgwTh9G6QTv2l/gDbsLuBvy9umUi6PHg5xK1U8z1HU3oWiMkuLj4lSBqakpbHvuqOwFdcRuo2ENS4XD6R1y26rdr7HURHOBWaunXm0ZxIy13F6dvsxR9Vq3jJkT/UoZFNtc9c7/RjSzfEcIIYToZRKgu5A5sJ/y3peCXg+41etvQz/18JyTzayhg1Smg/BMIDOvvw33vNstxlni/6+kPjY6PHvCnuFx0e9XiWkFnHKOZKGIbdu8bFuRe/dM0e+th+CpksrfnA7wrSveWunFtFC4Dy0QYSJvMREfX3ToyJzwPj28pHYSnFN7FSsO0nNgXiAH5txvpTKIBXdd5e31iRMUb7hj7uhwTQfdi1PKtKRvdLPKd4QQQoheJwG6y5guP8UDr5yeHjd3QId55DWzdaqON0TlyGugYZAH07erHHkN3iXqWWdCqMLC7hGrqYO1g4M4vgioGn63i/6ARkwroVZyZLJFqpbNPuUSv+3+Cke8F+uPbcOXz/v53Fk/hWp919rj8RKM9pOp6oyPZ0inlw559fA+r9/xzK7ychyn3taufmHttbrcy9+3CWy3UesSAizo1ewO4HiMTa8/bkb5jhBCCLEVLHVmlehQU+F92J75AzEAFBxvmOqOWwGobr91YXievh3ecO36RaymDnY5dnAYr8fN9oiXG0MWQ3acfDrOVDZP0ErzO66/5a/df8kRtR6ef5ge4H3nX8GHci8ir9V2YFVVpS/Wjyc6wtWsw/krY8uG59pzDy4Mz1Dv6Ty7Gz17BXP2160q2FWollAqeZR8ovb/W7DzWt59x7LXm3vv3PQ1zJTvLJj0skTdthBCCLFVyQ50l8kHRpa5VsGO3QBnHsXuv4GlaxaU2vWLDMbYSB2scuRVDN9wgAF3FU81TjZTJGNauKjyZu37vNP1jxhKafb2F80wf5V/KT8xb4AQEAJr5Aih8ScxqmlSFY3xRU4SXHJtboPZ17zIJL1lZgPWr7OrKKWGoN6inVfLH53Ta3oOVcMJRDd9DbD2Om8hhBBiK5IA3XVWLORd4+3m3WsddbC6rhPeuZ/Ic1+E10mSyxbJVmqlELepJ/lXrq+yU52cvX3O8fJx82X8XfFmbLNey6ypKpGQQbn/V7j4wy+QKFQp7X8dji+CUkyhn3102WEeSjlbC82N3T8UBVBrQbpark0inL/DbltglsDlro/ohhbvvK50vNZ3PNdjrXXeQgghxFYjAbrLBPLXyHmHlrjWQY0/C4CaOIcd28XiwctBTTy76COspQ7W5XIRiUQYCPvRD/wcuXyCuGkBCtuVSf616yv8onZq9va2o/D31vP4SPnlpCw3KA4KtQAd9Htx+0MkLD9jqTKl6146ZxfdCW+jPHwQ19nvop/+5qJrV0rZJd4XarvQ1QqUMhCI1XaoZ3enHShnoATatadqQbrFO69a7hp2eOlvFxSZAiiEEEJ0DAnQXSaafobJwA3TfYPnnWxWTOO68nitDtihVs+reRpKGGq3U0ppXJcfX/TxV9O/WNM0wuEwA5EAhlalmJlgslDFcVkEKPEbrn/kjdr30ZX6bu7T9m7+r/mrnLGvA6qgWlCtoLs0wqEgBTXIs3lI5vM4qo4zeGOtI8a8Uozq3hejjR1bfCdaofZaLWeRyYcOavwsTnAQiqmGWmlnulWcteGR2RvhSo9ixrLgDS68spRFS19t/aKEEEIIsSgJ0F1GrxbwnX6I/J6XzmlPR6WA+9TD2OHr6uG3UgCvXguhVgWlWq71cz61fD/npepgtUqOUCTCYF+QoG5Rzk2RyNRKPhTgLu3HvM/1EFGlXrM84YQ5av4K37ZvYXYSIQqKphPSLTTvMKN2mIl8lUrFBDScmZMkG0dvz5Ri2Bbm3jvx/PRTCxfu8tRKMRpDqDL9OKUsaimF6+TfYw0fwTYGcDxBlHIGNRef3Vmv7rp91T2zm0mfOEFp2y04ZnFhH2irIh0whBBCiA4iAboL6ZOn8cYvLAi4wJxx00q1hJOvgO4HRUW78ENclx9fYyhU/v/t3XlwlFWi/vFv70lnDwlLiIijGJEYwGFRUVDBARWH60K5zA8LtURcgLHGqitXBx1cYO69OhdhMCDewbjhqOiMCw46lsCAgIjIIiKyb5FsnT3p7f390fRLN+lA2gtk4flUUXS/7+m3z9sn3Xn65LznYLFYSEtNJdudRpoziL/WQ3mpB+PIbA29M3zcl7aQPMfR5Z8bDTtvBa7kTf9VNOCKOqIrwUmKy04VXTjUaKeq3g84wekMfSkI9zofef6jjONfUOdvBEdCaDaNYy8idCSA39vs+N4mC7AA/h4DTluvtLWx+mjPf+Qy2poBQ0REpM1RgG6nYgVBf8/LmoxdthhH53O2+BpaFMQiw2Sq20m2206GtR5++BzPrg3m6n+dXAHu7l3D1d2jFzVZFriIF/038JMRHXQtFkh3J2A4kzngdXG4sg5/aA3t5s4yxn0DIyEdf8/LmvYQR81QF2w66UYzk3CYC7AcO+7bYmnR3Ncni2bAEBERaR8UoDuQk7EUczhMpia5yHbbSbc1YjR6qKitI5jZB+f+H3D6qrnpnDpuPa+ORPvRVLoj0Jk5gRvZEOzV5LgJTjspyclUGMkUV/upbvRB0ID60lAPeXjIgq8utAS4tbkfTQvYbPjPHti0h9jhCl0kGGt8eEMVOGIviNKSua9P16wUmgFDRESk7VOA7kBaOgWdkZB6pJez6VjfpPMGkZudRLrDj+GrxVPrI+g3wLCCJcgl56ZyX5fddHMfvTCxymvhlT1deb/7ZIJWR9RzWi0W0pITCTjT2ON1UVrjJRAkFFiDgage8jADQkMvml09MDzXc3QPsaW+MnrYSkQotxjBZhdEORlfPEREROTMoQDdgbRkCrrmxvp2OrCKbN8hUnIyCBjVeOrsBAxLaBYPq5OezmoecrzHL7N3m48LBOGjvYm8+kMSpReNhWPCc6LTQXJyMuVGCsXVBjWNEUM9DMAIENORWTOOOqY3OXKu5oge4vD5x1qG/HgLovycua9FRETkzKUA3YGcaAo6oMm+lEQn2Ul2MrpegeObv1L+0168rhywWsBiIZk67nIsZYztS2yWo73OG0odFG5JYU/NkR+hlK7mPqvVQnqSG58zld2NTspqfQSC4UBshC7yO1KvmIKB0Bhmiy32/qhp+Y72ELdkCr5Y4pn7WkREREQBuoM53oVokRcZmsHZHhrjXFlZD7ZMgtjAasVKkNG21dxt/wdpljrz+Ie8bhZssrOq2EVUz7DVCoDb5cCdlBLqda4KUOv1HlPDiGnlKvZguDObhF1r6XaCOQWh+Zwt1ohZOWIH7sge4p9zId7PDd4iIiJyZlKA7oCauxDNSExrEpyrqurxB0I9ulZXMqR0pS8/Msn5d861HjIfW284eMN3Fe+UnAPF7zU5ti0YICM9jQZHGrsbnJTVNhKMlXeNYKiH2ZGAtfIg9s1/bxJ2/ecMwWioOrIgTDD0LzwndDDQJOQe20P8cy7E0wwYIiIi0lIK0GeIlJQU0tPtpKYFmgTnsC7BEu7N2MCVCTuitv/T349C77WUGmlY2cexc1kkp6RCeiol1ix+qgxQ72sktmPGLxuxw66lvhL8DVhqfeBIxLDawG8Fe+iZLeFjnOQeYs2AISIiIi2hAN3BpaSk0CkjlYwEK0bpesqSUvEf0zXswsdt7rXclrcal+3ovh8C3ZntvYHNwZ5HthhYKvaZ+x0OJ6mZWdSRwO6GNCr83maHNQMR4fn408qZY5IJgLfGHChiWKzgSMR6aAvWmhL1EIuIiEirUIDuoCKDM43VVB+uxO/3ge9fcM5l5pjiK13buC9pGV1s1eZjK4JuFvhG8Yl/AEGsR7Ya0FiN/chwidS0DGxJ6RTX+ikpKabyvHpwJTdfIcMAX32LppVrbkyyJRjAsfG907IyoIiIiEhzFKA7mOTkZLIy05oG5yNspTuwVv9Ezx5deCB3OwXuUnOfPwh/253Iq+V9qDz7PHA0RMyl3IBj5wqcQS+pXXKoMVz8dLgaj8cTenBd6fEDdMCLpSFiurgTzG6hMckiIiLSVrXrAL1y5UrefvttvvnmG4qLiwHo27cvf/3rX1u5ZqdfcnIyWRlpZCRaobGG6sOeqOAcluYMcmevg4zqsRNrxLV4a6uyeLH0YvZXW7B5tuPyvEMguxeGKxlLYw22ku2kuGw4snMorg1w+HAxPt/R49sPbcWf3iM0Y0YsNjuGPQGLv6HFY5c1JllERETaonYdoL/44guWLFnS2tVoVS0NzjaLwQ1n1/Ob82tJdhwdqHygIYE/N4xite9cSLFACgS65WPbt/5ICQt2u43MLl2pbwyy/3DV0V7nCNbKfc2HZwBvPdic2PZ8hX3/OvUki4iISLvVrgN0fn4+v/vd77j44ov5zW9+09rVOWmOt9R2WEuDM8DFWY3cd2ENPVKOzoBR57Pwxu4M3k4bh88SvYKgYU/Af8GvsNR7SE5w4EpKY7/PRdm6jwl49sZ8Dn/PIYTmaW5m+W2rHUtdKRZ/g8KziIiItGvtOkCPGTOmtatw0jW31LZj26fg2Ynb7ebs3BySbP4TBudubj/39q7h0q7Ri5ks3ZfAwm1JlGQPIJB+THi2WMGVjM1qIyMriwbDwc5aC+U19XDOlbhKdscMwMG0rk22RTkyj3N41UARERGR9qpdB+jW4HK5Ttmxg64UvBeMxHrsktIWSOw3mp4HPqVHJzdl3hIqysrw+31YrBYczujp4BJtQcaeU82Ys6txRIyq+N7jZP62dH6sCpW3utMIHjvswpGIO8FFYlIKpV4nh0vLaPAFsFqsYAFLbj9c+9Y0qXuDAUZzvc8AVhsWhxuHr+6UvobtjcPhiPpfOga1a8ejNu141Kbyf6EAHQeHw0GPHj1O2fFLMy7Ed0y4THI56JRkJ9PeSIr1LGoPbcRhs9C5c1aTx1swGJRWxo1d9pPuONor7fE5eO+nXNZWdsJItpBzZLKMigQbVREfHFarheS0DOoTOrG7wUFlZSU2iw2n02aWSemcS1fL0RUKw3YYddTS6bjnZySm0j3Bi/sUvobtVbdu3Vq7CnIKqF07HrVpx6M27Vi2b99+Wp6nTQXoTz75hP/5n/85YbmXXnqJs84669RX6Bg+n4+9e2OPAT4Z6l15+N1Hh1vkZrrJdjRi85ZRUVGHp6ySs/1+SkpK8Pn8UY/tleplQp6HvPSjj/cF4W97Unh7Vwr1AR9QHPUYo7QK74XdwWLFneDEnZxKKakc9EC9twFrfQ0BI3q1wurD+/Hua/oa+Nz7wZ0TmvYuFsPAUl/JgQYnrhiPP1M5HA66devGoUOHomY1kfZN7drxqE07HrVpx+T3+09c6CRoUwG6urqaXbt2nbCc1+s9YZlTpbGxuWWq/++CNeUEO4UCq8NmJc3upb6qhPrG0Pm66qvAAj6fH9+R1yDDFeCuvFquOash6lirip0s2JrMoTo70MwPk9eLY/eXJBdcg9eZxs46G2U15QTdnbA0VGEE/UQtLGgYGPs3xHwNjMpiyKoAd2bT2TiMIDTWYPjq8TnccApfw/bK5/Od0p8taR1q145HbdrxqE3l52hTAXrs2LGMHTu2tavRaswlrCPGQAeCR3qADQN72Q7ICl2E57AajOlZx+296nDbj8bcvdU2Cr9L4ZvS2MtkR0p0J+G2N1L23TIOWnOotbiw1VdhCzTiP2dIVD1ONHdzuO5GYzU4k0OPNYxQeMbA4q0FaHb1QREREZH2ok0F6HjV19dTX18ftc3v91NeXg6ElrNuTxcHRC1hHckwsO9adSSEpjAwq567z/fQPenotHQ1Pguv/ZDEh3sSCRjHuZgPsFqtpGV0wu9MYW9FPWVl2wkGtxH5StlKtse1CmC47t4LRoEjEcw6GKHHGYETrj4oIiIi0h606wC9YMEC5syZE7Vty5YtXHrppQAUFRUxePDg1qjazxZewtqa2xerLQfbT9txHvgOS2MN3dMtPNhjO/kpR5fEDhqwZG8Cr/6QTKX3OAuZHOFKSCQ5I4sKr43iA6XU1dXFLPdzVgG0/bSVhMoD+HqNINCtDxYjCL56Mzy3ZPVBERERkbauXQfojsrSUIV9z2rsdMOoOEBioJ47etfy65712CMy8qYyB4XfJbOz6sS97BaLhbSMTIKuVPZVNlBWdphAIHDCx/2cujs3LcbY/llcPdgiIiIi7UW7DtCTJk1i0qRJrV2NU8aKwTU5Nfy/cz2ku46Ocy6pt/HS1iRWHHLR7Mp/EVyuBFI6ZePx2ik+VEFNTc0prHXIz+nBFhEREWkP2nWA7sgKMhuZ3nsTv3AfHWLRGIBPy3JYuAlqG048TYvFYiE1PQMS0thf2UhpWclpm95FREREpKNSgG6Dzk72s+CKEhJsR3udlx108cqODJyZ3fEGmy5kciyny0VqZjZVfgeHij1UV1efyiqLiIiInDEUoNsgp83AaQ2F553VDgo3J7Gp3InDaSenBY9PS8/E6k7jQJWX0rJiTRAvIiIichIpQLdB2ysdjF/Whbzu6azbU0tDY8sWjnE4naRmZlMdcFJcXElVlS7aExERETnZFKDbqA3lCdSlZBAk9jRzx0pJS8eelEFxjY/DJep1FhERETlVFKDbOYfDSWpmFjWGi32Hq/F4PK1dJREREZEOTQG6HUtOTcORnEFxbYDDh9XrLCIiInI6KEC3Q3a7g9SMTtSRyP7DVep1FhERETmNFKDbGXdSMqmdnRysaOSnkmK83pZdYCgiIiIiJ4f1xEWkLbDbHWR16UbQnc2e0nr2HTio8CwiIiLSCtQD3cZZsJhjnQ832Kk6XEFFRUVrV0tERETkjKUe6DYurVNn/Imd2HW4hv0HD6nXWURERKSVqQe6jQoGg3gDFjz1BiVHxjq7XK7WrpaIiIjIGU8Buo0KBALsOaAeZxEREZG2RkM42jCFZxEREZG2RwFaRERERCQOCtAiIiIiInFQgBYRERERiYMCtIiIiIhIHBSgRURERETioAAtIiIiIhIHBWgRERERkTgoQIuIiIiIxEEBWkREREQkDgrQIiIiIiJxUIAWEREREYmDArSIiIiISBwUoEVERERE4qAALSIiIiISBwVoEREREZE4WAzDMFq7Eu1BYmIiDQ0NOByOVquDxWLBbrfj9/tRs3UMatOOSe3a8ahNOx61acfk8/lISEigvr7+lD6P/ZQevQNxuVwAdOvWrZVrIiIiIiKxHDp0yMxsp5J6oEVERERE4qAx0CIiIiIicVCAFhERERGJgwK0iIiIiEgcFKBFREREROKgAC0iIiIiEgcFaBERERGROChAi4iIiIjEQQFaRERERCQOCtAiIiIiInFQgBYRERERiYMCtIiIiIhIHBSgRURERETioAAtIiIiIhIHBWgRERERkTgoQIuIiIiIxEEBWkREREQkDgrQIiIiIiJxsLd2BSRk8eLFTJ06Nea+4cOHM3fuXPP+smXLePHFF/n++++xWq3069ePyZMn069fv9NUW4llz549vPTSS3zzzTfs2LEDwzAA2LhxIy6XK6rs+++/zyuvvMKOHTtISEhg0KBBPPzww5x77rlR5aqqqnjhhRdYunQp5eXldO3alRtuuIGJEyc2OaacfC1t06uvvpoDBw7EPMb7779P7969zftq09b1+eef89FHH7F582ZKSkqw2Wz06NGDO+64gxtvvBGr9Wi/0oYNG3jhhRfYsGEDwWCQCy64gPvvv59hw4ZFHTMYDFJUVMRf//pX9u3bR0pKCkOHDuXhhx+mS5cup/sUz0gtbddHH32U9957L+Yxpk6dyvjx4837atfW9cUXX/DKK6+wfft2PB4PNpuN3NxcRowYwYQJE0hKSooq39Js1NjYSGFhIR988AHFxcVkZmbyq1/9ismTJ5Oamtri+lmM8G8EaVUtDdAffvghjzzyCMc2m9Pp5H//938ZOHDgKa+rxPbZZ5/x4IMPNtl+bNiaP38+zz33XJNyqampLFq0yAzRjY2N3HrrrWzdurVJ2aFDhzJ//nwsFstJPAM5VkvbtKUBWm3a+u655x7+9a9/xdx3xx138MQTTwDw1Vdfcdddd+Hz+aLKWCwW/vu//5vRo0eb26ZNm8Zbb73V5Hg5OTm88847dOrU6SSegcTS0naNJ0CrXVvXn/70JwoLC2Puu+KKK1iwYIF5v6XZyDAMJkyYwPLly5scs3fv3rz11lst7sjQEI42pnv37mzbti3qXzg8NzQ08NRTT2EYBjk5OSxdupR33nmHlJQUvF4vTz75ZOtW/gzXuXNnJk6cSGFhIQUFBTHLHDp0iBdeeAGAPn36sHz5cl566SXsdjtVVVXMnDnTLFtUVGQGrUmTJrF69WpuvfVWAJYvX86SJUtO8RlJS9o00owZM5q8fyN7n9Wmrc/lcjF+/Hg+/PBDvv32W2bNmoXdHvpj7JtvvklZWRkATzzxBD6fj5SUFN555x0+/fRTcnJyMAyDp556ioaGBgC+/fZbM2RdddVVfPnllzz77LMAHDx4kNmzZ7fCWZ55WtquYTfeeGOT92pkeFa7tr6CggL+/Oc/s2LFCr799lvmzp2L0+kEYMWKFXg8HiC+bLRkyRIzPN96662sXr2ayZMnA7B161aKiopaXD8F6HZk+fLl5g/M7bffztlnn81FF13EddddB8CPP/7Id99914o1PLMVFBTw8MMPc9VVV5GQkBCzzCeffGL2aN1zzz106dKFoUOHcskllwDwr3/9i/LycgD+/ve/A+B2u5k4cSIZGRk89NBD5rE++OCDU3k6QsvaNB5q09b3n//5n0ydOpVevXqRkJDAqFGjuOKKK4BQ79TevXvZsmULO3bsAOC6667joosuokePHtx2220AeDweVqxYAUS32cSJE8nMzOTmm2+mZ8+eAHz00UcEg8HTeIZnppa0azzUrq1v+PDhjBgxgs6dO5OQkMDw4cPp1auXuT/8BSmebBT+DIZQJ0ZGRgYTJkzA7XYD8X0GK0C3MYcPH2bw4MHk5+czcuRIZs2ahdfrBWDLli1muV/84hcxb0eWkbanuTY855xzgNCYu23btuH1es1f4D169DA/KDp37kxycjKAviy1QX/84x/Jz89n4MCB3HvvvXzzzTfmPrVp2xB+rSM1Njaat7t06RLVDif6rD3R53JVVRX79+8/CTWX42lJu0b69NNP6du3L3379uWWW27h/fffj9qvdm1bGhoa+Oyzz9i+fTsAv/71r802jycbhd/bKSkpZGdnA+BwODjrrLOAUNgOZ64TUYBuY3w+Hx6PB5/Px+7du5k7dy73338/ABUVFWa5yMHzkbfDvZfSNkW2YeQHfuTtsrIyPB4PgUCgyb7I+8f+SVJaX/i9W1VVxfLlyxk3bhxfffWVuU9t2vZ89dVXrF69GoDLLruMnJycqM/R5t6n4TItKat2Pf1itWukmpoaGhoaaGhoYNOmTfz7v/878+bNM/erXduGkpIS8vLy6Nu3Lw8++CBer5drr73WHE4D8WWj8P/NfQYHAgGzN/tEFKDbiLPPPptnnnmGzz//nG+//ZaioiKysrKA0J/116xZ06Lj6AKk9inywocTtWG4rNq67bjtttt48803+frrr1m5cqU5rtnn85lj3o9Hbdo6Nm7cyIMPPkgwGKRLly7MmDHjuOXjueY+nve0nFzHa9dLL72UwsJCVq5cyddff83TTz9tztAxd+5c6uvrj3tstWvrW7JkSbOTLjSnpb9XW1I2TAG6jfjlL3/JLbfcQvfu3UlISGDw4MHceeed5v5NmzaRkZFh3q+pqTFv19bWmrcjy0jbE9k+1dXV5u3INszMzCQ9PR2bzdakXGTZzMzMU1lVicOECRO4+OKLSU5OJisri2nTppGYmAiE3ruA2rSNWb9+PXfddReVlZV07tyZhQsX0rVrVyC6HZr7rA2XiSx7vPe0nB7Ha1eAMWPGcNVVV5GVlUVycjJjx47lsssuA0LDBMJDBNSubUN2djbbtm1jw4YNFBUVmW35wQcfsHnzZoC4slG4zZr7DLbZbKSlpbWobgrQbUSsixEivwVZLBb69Olj3t+1a5d5e+fOnebtyDLS9jTXhuHbVquVvLw8nE6nOZ3dvn378Pv9QGiMfPgD4sILLzxd1ZbjaO69G37/hv9Xm7Yda9eu5Z577qGmpobu3bvzxhtvRI2XjGyHyM/XWJ+1J/pcTk1NJTc39+SfhDRxonY1DOOEf0UIv1/Vrm1LYmIigwcPZuTIkea2PXv2ACduq8gy4fd2TU0NJSUlQOgvhfv27QPgvPPOM2f6OBEF6Dbi/vvvp6ioiIMHD9LY2MiaNWt45ZVXzP0XX3wxQ4cOJT09HQhNy7Nnzx42bdrExx9/DIQaXr+AW4/P56O8vJzy8vKouWM9Hg/l5eXU19czatQoHA4HAC+//DKHDx9m+fLl5li9yy+/3PyG/Otf/xqAuro6CgsL8Xg8zJkzxzzuDTfccLpO7YzVkjb9/PPPmTJlCmvWrKG+vp7S0lL+8Ic/UFdXB4Teu2Fq09a3cuVK7r33Xurq6ujZsydvvPGGeQFRWJ8+fcwvOx9//DGbNm1iz549LFq0CAj9NSE8w0NkmxUWFlJeXs67777L7t27Abj++uujFmeRU6Ml7VpdXc3YsWNZsmQJHo+Hmpoa3n77bVatWgWExsGef/75gNq1LXjsscdYvXo15eXlNDY28vXXX7N06VJzf7h948lG4c9ggNmzZ+PxeJg3b575eR3PZ7AWUmkjxowZw/fffx9z3+jRo82FN7SQStu1Zs2aqGE3x3rooYeYNGmSFlJpR1rSpr1794652AqEpqt7/fXXzQ9vtWnrGzduHGvXrm12/4wZM7jppptYu3Ytd999txZSaSda0q4jRow47u/I6dOnm9cvgNq1teXl5TW77+qrr+bFF18072shlTPY5MmTuf766+nRowcJCQkkJibSp08fpk2bxn/913+Z5UaPHs28efPo378/iYmJJCUlMWTIEF599VWF53ZiwoQJzJw5kwsvvBCXy0VaWhrXXHNNVHiG0MIARUVFjBs3ji5duuBwOMjNzeWBBx5gzpw5ClptRL9+/XjooYfo27cvmZmZ2O12srOzGT16NO+++27UX4XUpu3HoEGDeO211xgyZAhJSUkkJibSv39/5s2bFxWeAZ588kmmTp3Kueeei8PhIDMzk3/7t39j0aJFClltiNvt5ve//z2XX345Xbt2xeFwkJqaypAhQ1iwYEFUeAa1a2u788476dOnj3n9SGpqKv379+fxxx9vcnF2S7ORxWJhzpw5PPDAA+Tm5uJwOOjSpQvjxo2jqKioxeEZ1AMtIiIiIhIX9UCLiIiIiMRBAVpEREREJA4K0CIiIiIicVCAFhERERGJgwK0iIiIiEgcFKBFREREROKgAC0iIiIiEgcFaBERERGROChAi4icwOzZs8nLyyMvL49HH320yf5x48aZ+xcvXtwKNRQRkdNJAVpEREREJA4K0CIibVhdXV1rV6Hd8nq9+P3+1q6GiHRACtAiIqfInj17mDp1KsOGDSM/P5/Bgwdz77338uWXX0aVW7NmTdQQkaVLlzJmzBjy8/N5+eWXzTLjx49n0KBB9OnTh0suuYRbbrmFp59+murq6qjjffbZZ4wfP56BAweSn5/PyJEjmTNnDg0NDVHlIoeebNu2jT/84Q9ccskl9OvXj/vuu4+9e/c2OactW7YwefJkhgwZQn5+PkOGDGHy5Mls3rzZLFNdXU3v3r3Jy8vj9ttvN7e//fbb5vOtWbPG3D5kyBDy8vIYMmRI1HOtW7eOiRMncskll5Cfn8/VV1/NjBkzqKysjCr36KOPmsddtmwZM2fO5PLLL6egoIDi4uKWNJWISFzsrV0BEZGOaOPGjYwfP57a2lpzm8fjYfny5axYsYJp06Zxxx13NHncV199xfvvv49hGOa2nTt3MmHChKgAXFFRQUVFBZs2bWLcuHGkpKQAMGvWLObOnRt1zN27dzN79my+/PJL/vKXv+B0Ops875QpU9i1a5d5/4svvmDr1q387W9/IyMjA4B//vOfTJkyBZ/PZ5YrLS3lH//4B59//jmzZs1i+PDhpKSkcP755/P999+zZcsWfD4fDoeDb775xnzchg0bGDx4MPv27aO0tBSAX/7yl+b+t99+m2nTphEMBs1tBw4cYOHChSxbtoy33nqLtLS0Jufx1FNPsW/fvibbRUROJvVAi4jE4b333jN7O8P/1q5dG1XGMAymTp1qhueRI0cyf/58HnjgAaxWK4Zh8Oyzz3Lo0KEmx9+/fz/5+fnMmjWLP//5zwwYMIBVq1aZ4fnOO+9k4cKFvPDCC/z2t78lPz8fi8UChEJ7ODxnZ2fzzDPPsGDBAq688kog1KO7cOHCmOfl8XiYMWMGs2bN4qyzzgLgp59+Yt68eUBoKMljjz1mhufbb7+d+fPnm18CfD4fjz32mDnkZODAgQA0NjaydetWIBSaw8Jhev369ea2AQMGmM87ffp0gsEgSUlJ/P73v+fll1/mpptuAmDXrl08//zzMc9j3759jBs3jgULFjB9+nSSkpJilhMR+b9QgBYROcm2bt3Kjz/+CISC7HPPPcewYcOYMmUK11xzDRAKnP/4xz+aPNbtdrNgwQJGjRrFiBEjuPTSS7Hbj/6xMDc3l/POO4+RI0dy//338+6779KjRw8APvjgA7PczTffTM+ePUlMTOS2224zt0eWifS73/2Om266iVGjRjF9+nRz+2effQbAypUrqaioAKBPnz48+eSTDBs2jCeeeII+ffoAoV7xVatWAdG9yRs2bKCqqoqdO3eSm5tLQkKCGaYjQ3U4QC9ZsgSv1wuEvnxccMEFJCQkcPPNN5OYmAjARx99FNU7HTZ69Ggef/xxrrjiCm699Vaz91xE5GTSEA4RkTgMHTqU++67L2rb008/bfayQmjIRNiFF16Iw+Ew71900UVmcI4sF3bxxReTnp4etW348OH86U9/wuPx8Oyzz/Lss8+SlpZGQUEBN998M9dee22T4xUWFlJYWNjk+Dt37ox5XgUFBTFvHzhwAMMwooZ39O3bt8ljt2zZAmCWC/dAQygk9+zZE8MwGDBgAPv372fdunXs2rXL7IlOTk7mggsuaHIeixcvjjk1YHV1NYcPH6Zr165R26+++uqY5ycicjIpQIuIxKFTp05mT2lYePxxS4SHWzQnKyurybbs7GwWL17Mm2++yfr169mxYwcej4cVK1awYsUKgsEg119/fYue3+/34/V6Y46D/rlinVNWVhY9e/Zk9+7dZoAG6NevH1lZWaxbt45Vq1bxww8/ANC/f3+s1vj+KBprhpJOnTrFfwIiInFSgBYROcnCYRHgu+++w+/3m8MwNm7cGLNcWKwwahgG3bt355FHHjG3bdq0iVtuuQWApUuXcv3119OzZ0+WL18OwIwZM8wxw5Hq6+tjhueNGzeSl5fXpI7du3fHYrFwzjnnRJU99rFhkeUGDBjA7t27OXDgAJ9++ikQCsrZ2dkAvPrqqwQCASC6xzrydXnooYeYNGlSzPMID+eIdKIvKCIiJ4MCtIjISda7d2/OPfdcduzYQUlJCY888gg33ngjGzduNIOkw+Fg5MiRLTrehx9+yKJFixgxYgS5ubkkJyezevVqc394vPANN9xAUVERgDndW15eHlVVVezdu5eVK1eSk5PDjBkzmjzH888/j91uJzExMeoCveHDhwOhqebS09PxeDxs3ryZ6dOnM2zYMJYvX25OYZeRkcFll11mPnbgwIG88847APzwww+43W569epl9rJHDguJHDM9atQonnvuObxeL/Pnz8disdCvXz8aGhrYv38/q1evprGxkb/85S8tev1ERE42BWgRkZPMYrEwc+ZMcxq7JUuWsGTJkqj9//Ef/0G3bt1adLxgMMi6detYt25dzP2jR48GQmORH3jgAebOnUtVVRUzZ85sUvbGG2+MeYzs7Owmy5RnZ2eb473dbjfPPPMMv/3tb/H5fLz++uu8/vrrZlmHw8EzzzyD2+02t0WG4nD9bDYbWVlZ5Obmsn//fgCcTmfUuOuuXbsybdo0pk2bhtfrZfbs2U3qO2jQoJjnISJyOihAi4icAgUFBSxevJjCwkJWrVpFWVkZSUlJFBQUcPfdd0f11J5I//79ufPOO/n66685ePAgVVVVuN1u8vLy+M1vfsN1111nlp0yZQp9+/bltddeY9OmTdTW1pKZmUlubi5XXnlls2Oln3/+eRYtWsRHH31EXV0dgwYN4vHHHyczM9MsM2LECBYtWsT8+fNZt24dlZWVpKamMmDAACZMmMBFF10UdcyzzjqLrl27mouZ9OvXz9zXr18/M0AXFBQ0GVYyduxYfvGLX7Bw4ULWr1+Px+MhPT2dnJwcLr/88haP+RYRORUsRuRs/SIicsYYN26cOYf1P//5T3Jzc1u5RiIi7YPmgRYRERERiYMCtIiIiIhIHBSgRURERETioDHQIiIiIiJxUA+0iIiIiEgcFKBFREREROKgAC0iIiIiEgcFaBERERGROChAi4iIiIjEQQFaRERERCQOCtAiIiIiInFQgBYRERERiYMCtIiIiIhIHP4/+KwrnVl8t/IAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlação entre Variáveis Numéricas**"
      ],
      "metadata": {
        "id": "rK9Skw9NWnJY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mapa de Calor da Matriz de Correlação**"
      ],
      "metadata": {
        "id": "zHUTkuHCpdfB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(df.corr(), annot=True, cmap=\"mako\", linewidths=.5, square=True, fmt=\".2f\",\n",
        "            cbar_kws={\"shrink\": 0.8}, vmin=-1, vmax=1)\n",
        "plt.title('Matriz de Correlação')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "VYZlmi86WjVW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "outputId": "d9e8dfc8-4a27-40d8-a45b-3c503b273dad"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Modelagem Preditiva**\n",
        "\n",
        "**Conclusão**\n",
        "\n",
        "O projeto visa fornecer uma compreensão prática de como aplicar a regressão linear para prever preços de carros com base em características específicas. A conclusão incluirá insights sobre o desempenho do modelo e possíveis melhorias para futuras iterações.\n",
        "\n",
        "**Avaliação do Processo de Tunagem do Modelo**\n",
        "\n",
        "Durante a etapa de tunagem do modelo, foram realizados experimentos com diferentes combinações de hiperparâmetros para otimizar o desempenho do modelo de regressão linear."
      ],
      "metadata": {
        "id": "jFxNB4hy0u3z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Configurar o ambiente\n",
        "exp = setup(df, target='price', session_id=42)"
      ],
      "metadata": {
        "id": "rn-x54gwjigp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "outputId": "50987464-da46-46a8-9de6-00f02436f36e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f75a4113940>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_b9f65_row10_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_b9f65\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b9f65_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_b9f65_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_b9f65_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_b9f65_row0_col1\" class=\"data row0 col1\" >42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_b9f65_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_b9f65_row1_col1\" class=\"data row1 col1\" >price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_b9f65_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_b9f65_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_b9f65_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_b9f65_row3_col1\" class=\"data row3 col1\" >(205, 24)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_b9f65_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_b9f65_row4_col1\" class=\"data row4 col1\" >(205, 47)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_b9f65_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_b9f65_row5_col1\" class=\"data row5 col1\" >(143, 47)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_b9f65_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_b9f65_row6_col1\" class=\"data row6 col1\" >(62, 47)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_b9f65_row7_col0\" class=\"data row7 col0\" >Ordinal features</td>\n",
              "      <td id=\"T_b9f65_row7_col1\" class=\"data row7 col1\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_b9f65_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
              "      <td id=\"T_b9f65_row8_col1\" class=\"data row8 col1\" >14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_b9f65_row9_col0\" class=\"data row9 col0\" >Categorical features</td>\n",
              "      <td id=\"T_b9f65_row9_col1\" class=\"data row9 col1\" >9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_b9f65_row10_col0\" class=\"data row10 col0\" >Preprocess</td>\n",
              "      <td id=\"T_b9f65_row10_col1\" class=\"data row10 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_b9f65_row11_col0\" class=\"data row11 col0\" >Imputation type</td>\n",
              "      <td id=\"T_b9f65_row11_col1\" class=\"data row11 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_b9f65_row12_col0\" class=\"data row12 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_b9f65_row12_col1\" class=\"data row12 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_b9f65_row13_col0\" class=\"data row13 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_b9f65_row13_col1\" class=\"data row13 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_b9f65_row14_col0\" class=\"data row14 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_b9f65_row14_col1\" class=\"data row14 col1\" >25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_b9f65_row15_col0\" class=\"data row15 col0\" >Encoding method</td>\n",
              "      <td id=\"T_b9f65_row15_col1\" class=\"data row15 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_b9f65_row16_col0\" class=\"data row16 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_b9f65_row16_col1\" class=\"data row16 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_b9f65_row17_col0\" class=\"data row17 col0\" >Fold Number</td>\n",
              "      <td id=\"T_b9f65_row17_col1\" class=\"data row17 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_b9f65_row18_col0\" class=\"data row18 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_b9f65_row18_col1\" class=\"data row18 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_b9f65_row19_col0\" class=\"data row19 col0\" >Use GPU</td>\n",
              "      <td id=\"T_b9f65_row19_col1\" class=\"data row19 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_b9f65_row20_col0\" class=\"data row20 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_b9f65_row20_col1\" class=\"data row20 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_b9f65_row21_col0\" class=\"data row21 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_b9f65_row21_col1\" class=\"data row21 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9f65_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_b9f65_row22_col0\" class=\"data row22 col0\" >USI</td>\n",
              "      <td id=\"T_b9f65_row22_col1\" class=\"data row22 col1\" >357c</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compare_models()"
      ],
      "metadata": {
        "id": "uHY87aCQjxWS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1326,
          "referenced_widgets": [
            "642821481c7f4fe5b53548f0246fc62b",
            "3a0c1f63f7e24255bf6214f5bc0c8ef4",
            "ab78e6b3f58149ee8e956de3f28d3396",
            "a1dad115a2934474af5c9b7a617e9e36",
            "a395a739f3bf4adbb1d582c7d6e099b3",
            "5ad3651695644a48954f6389d0baa56c",
            "94be97c83bfd4d789e8d08fd412fe9bf",
            "a6cddb549eb242e9a206709f687b96d5",
            "7c19f1eb2e504eab8c474eb7c7ce17ff",
            "d9c36d4de1ef423daae00cd6382cb6d1",
            "b4760b18ca90482e8f903836b783f7e0"
          ]
        },
        "outputId": "a73d80ab-bbec-4f43-c121-97609ad6f0f0"
      },
      "execution_count": 20,
      "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 0x7f75305af220>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_18eb3 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_18eb3_row0_col0, #T_18eb3_row0_col5, #T_18eb3_row0_col6, #T_18eb3_row1_col0, #T_18eb3_row1_col1, #T_18eb3_row1_col2, #T_18eb3_row1_col3, #T_18eb3_row1_col4, #T_18eb3_row1_col5, #T_18eb3_row1_col6, #T_18eb3_row2_col0, #T_18eb3_row2_col1, #T_18eb3_row2_col2, #T_18eb3_row2_col3, #T_18eb3_row2_col4, #T_18eb3_row2_col5, #T_18eb3_row2_col6, #T_18eb3_row3_col0, #T_18eb3_row3_col1, #T_18eb3_row3_col2, #T_18eb3_row3_col3, #T_18eb3_row3_col4, #T_18eb3_row3_col6, #T_18eb3_row4_col0, #T_18eb3_row4_col1, #T_18eb3_row4_col2, #T_18eb3_row4_col3, #T_18eb3_row4_col4, #T_18eb3_row4_col5, #T_18eb3_row5_col0, #T_18eb3_row5_col1, #T_18eb3_row5_col2, #T_18eb3_row5_col3, #T_18eb3_row5_col4, #T_18eb3_row5_col5, #T_18eb3_row5_col6, #T_18eb3_row6_col0, #T_18eb3_row6_col1, #T_18eb3_row6_col2, #T_18eb3_row6_col3, #T_18eb3_row6_col4, #T_18eb3_row6_col5, #T_18eb3_row6_col6, #T_18eb3_row7_col0, #T_18eb3_row7_col1, #T_18eb3_row7_col2, #T_18eb3_row7_col3, #T_18eb3_row7_col4, #T_18eb3_row7_col5, #T_18eb3_row7_col6, #T_18eb3_row8_col0, #T_18eb3_row8_col1, #T_18eb3_row8_col2, #T_18eb3_row8_col3, #T_18eb3_row8_col4, #T_18eb3_row8_col5, #T_18eb3_row8_col6, #T_18eb3_row9_col0, #T_18eb3_row9_col1, #T_18eb3_row9_col2, #T_18eb3_row9_col3, #T_18eb3_row9_col4, #T_18eb3_row9_col5, #T_18eb3_row9_col6, #T_18eb3_row10_col0, #T_18eb3_row10_col1, #T_18eb3_row10_col2, #T_18eb3_row10_col3, #T_18eb3_row10_col4, #T_18eb3_row10_col5, #T_18eb3_row10_col6, #T_18eb3_row11_col0, #T_18eb3_row11_col1, #T_18eb3_row11_col2, #T_18eb3_row11_col3, #T_18eb3_row11_col4, #T_18eb3_row11_col5, #T_18eb3_row11_col6, #T_18eb3_row12_col0, #T_18eb3_row12_col1, #T_18eb3_row12_col2, #T_18eb3_row12_col3, #T_18eb3_row12_col4, #T_18eb3_row12_col5, #T_18eb3_row12_col6, #T_18eb3_row13_col0, #T_18eb3_row13_col1, #T_18eb3_row13_col2, #T_18eb3_row13_col3, #T_18eb3_row13_col4, #T_18eb3_row13_col5, #T_18eb3_row13_col6, #T_18eb3_row14_col0, #T_18eb3_row14_col1, #T_18eb3_row14_col2, #T_18eb3_row14_col3, #T_18eb3_row14_col4, #T_18eb3_row14_col5, #T_18eb3_row14_col6, #T_18eb3_row15_col0, #T_18eb3_row15_col1, #T_18eb3_row15_col2, #T_18eb3_row15_col3, #T_18eb3_row15_col4, #T_18eb3_row15_col5, #T_18eb3_row15_col6, #T_18eb3_row16_col0, #T_18eb3_row16_col1, #T_18eb3_row16_col2, #T_18eb3_row16_col3, #T_18eb3_row16_col4, #T_18eb3_row16_col5, #T_18eb3_row16_col6, #T_18eb3_row17_col0, #T_18eb3_row17_col1, #T_18eb3_row17_col2, #T_18eb3_row17_col3, #T_18eb3_row17_col4, #T_18eb3_row17_col5, #T_18eb3_row17_col6, #T_18eb3_row18_col0, #T_18eb3_row18_col1, #T_18eb3_row18_col2, #T_18eb3_row18_col3, #T_18eb3_row18_col4, #T_18eb3_row18_col5, #T_18eb3_row18_col6, #T_18eb3_row19_col0, #T_18eb3_row19_col1, #T_18eb3_row19_col2, #T_18eb3_row19_col3, #T_18eb3_row19_col4, #T_18eb3_row19_col5, #T_18eb3_row19_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_18eb3_row0_col1, #T_18eb3_row0_col2, #T_18eb3_row0_col3, #T_18eb3_row0_col4, #T_18eb3_row3_col5, #T_18eb3_row4_col6 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_18eb3_row0_col7, #T_18eb3_row1_col7, #T_18eb3_row2_col7, #T_18eb3_row3_col7, #T_18eb3_row4_col7, #T_18eb3_row5_col7, #T_18eb3_row6_col7, #T_18eb3_row7_col7, #T_18eb3_row8_col7, #T_18eb3_row10_col7, #T_18eb3_row11_col7, #T_18eb3_row12_col7, #T_18eb3_row13_col7, #T_18eb3_row14_col7, #T_18eb3_row15_col7, #T_18eb3_row16_col7, #T_18eb3_row17_col7, #T_18eb3_row18_col7, #T_18eb3_row19_col7 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_18eb3_row9_col7 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_18eb3\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_18eb3_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_18eb3_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_18eb3_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_18eb3_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_18eb3_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_18eb3_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_18eb3_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_18eb3_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row0\" class=\"row_heading level0 row0\" >et</th>\n",
              "      <td id=\"T_18eb3_row0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_18eb3_row0_col1\" class=\"data row0 col1\" >0.1919</td>\n",
              "      <td id=\"T_18eb3_row0_col2\" class=\"data row0 col2\" >0.0806</td>\n",
              "      <td id=\"T_18eb3_row0_col3\" class=\"data row0 col3\" >0.2717</td>\n",
              "      <td id=\"T_18eb3_row0_col4\" class=\"data row0 col4\" >0.8869</td>\n",
              "      <td id=\"T_18eb3_row0_col5\" class=\"data row0 col5\" >0.1512</td>\n",
              "      <td id=\"T_18eb3_row0_col6\" class=\"data row0 col6\" >1.1938</td>\n",
              "      <td id=\"T_18eb3_row0_col7\" class=\"data row0 col7\" >0.2960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row1\" class=\"row_heading level0 row1\" >catboost</th>\n",
              "      <td id=\"T_18eb3_row1_col0\" class=\"data row1 col0\" >CatBoost Regressor</td>\n",
              "      <td id=\"T_18eb3_row1_col1\" class=\"data row1 col1\" >0.1987</td>\n",
              "      <td id=\"T_18eb3_row1_col2\" class=\"data row1 col2\" >0.0902</td>\n",
              "      <td id=\"T_18eb3_row1_col3\" class=\"data row1 col3\" >0.2840</td>\n",
              "      <td id=\"T_18eb3_row1_col4\" class=\"data row1 col4\" >0.8788</td>\n",
              "      <td id=\"T_18eb3_row1_col5\" class=\"data row1 col5\" >0.1505</td>\n",
              "      <td id=\"T_18eb3_row1_col6\" class=\"data row1 col6\" >1.1740</td>\n",
              "      <td id=\"T_18eb3_row1_col7\" class=\"data row1 col7\" >1.7180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
              "      <td id=\"T_18eb3_row2_col0\" class=\"data row2 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_18eb3_row2_col1\" class=\"data row2 col1\" >0.1979</td>\n",
              "      <td id=\"T_18eb3_row2_col2\" class=\"data row2 col2\" >0.0907</td>\n",
              "      <td id=\"T_18eb3_row2_col3\" class=\"data row2 col3\" >0.2844</td>\n",
              "      <td id=\"T_18eb3_row2_col4\" class=\"data row2 col4\" >0.8700</td>\n",
              "      <td id=\"T_18eb3_row2_col5\" class=\"data row2 col5\" >0.1528</td>\n",
              "      <td id=\"T_18eb3_row2_col6\" class=\"data row2 col6\" >0.9922</td>\n",
              "      <td id=\"T_18eb3_row2_col7\" class=\"data row2 col7\" >0.3410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row3\" class=\"row_heading level0 row3\" >gbr</th>\n",
              "      <td id=\"T_18eb3_row3_col0\" class=\"data row3 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_18eb3_row3_col1\" class=\"data row3 col1\" >0.1990</td>\n",
              "      <td id=\"T_18eb3_row3_col2\" class=\"data row3 col2\" >0.0911</td>\n",
              "      <td id=\"T_18eb3_row3_col3\" class=\"data row3 col3\" >0.2868</td>\n",
              "      <td id=\"T_18eb3_row3_col4\" class=\"data row3 col4\" >0.8678</td>\n",
              "      <td id=\"T_18eb3_row3_col5\" class=\"data row3 col5\" >0.1467</td>\n",
              "      <td id=\"T_18eb3_row3_col6\" class=\"data row3 col6\" >1.1369</td>\n",
              "      <td id=\"T_18eb3_row3_col7\" class=\"data row3 col7\" >0.2190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row4\" class=\"row_heading level0 row4\" >ada</th>\n",
              "      <td id=\"T_18eb3_row4_col0\" class=\"data row4 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_18eb3_row4_col1\" class=\"data row4 col1\" >0.2299</td>\n",
              "      <td id=\"T_18eb3_row4_col2\" class=\"data row4 col2\" >0.0927</td>\n",
              "      <td id=\"T_18eb3_row4_col3\" class=\"data row4 col3\" >0.2959</td>\n",
              "      <td id=\"T_18eb3_row4_col4\" class=\"data row4 col4\" >0.8651</td>\n",
              "      <td id=\"T_18eb3_row4_col5\" class=\"data row4 col5\" >0.1650</td>\n",
              "      <td id=\"T_18eb3_row4_col6\" class=\"data row4 col6\" >0.9165</td>\n",
              "      <td id=\"T_18eb3_row4_col7\" class=\"data row4 col7\" >0.3600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row5\" class=\"row_heading level0 row5\" >xgboost</th>\n",
              "      <td id=\"T_18eb3_row5_col0\" class=\"data row5 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_18eb3_row5_col1\" class=\"data row5 col1\" >0.1960</td>\n",
              "      <td id=\"T_18eb3_row5_col2\" class=\"data row5 col2\" >0.0925</td>\n",
              "      <td id=\"T_18eb3_row5_col3\" class=\"data row5 col3\" >0.2845</td>\n",
              "      <td id=\"T_18eb3_row5_col4\" class=\"data row5 col4\" >0.8625</td>\n",
              "      <td id=\"T_18eb3_row5_col5\" class=\"data row5 col5\" >0.1500</td>\n",
              "      <td id=\"T_18eb3_row5_col6\" class=\"data row5 col6\" >1.1880</td>\n",
              "      <td id=\"T_18eb3_row5_col7\" class=\"data row5 col7\" >0.2170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row6\" class=\"row_heading level0 row6\" >lightgbm</th>\n",
              "      <td id=\"T_18eb3_row6_col0\" class=\"data row6 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_18eb3_row6_col1\" class=\"data row6 col1\" >0.2288</td>\n",
              "      <td id=\"T_18eb3_row6_col2\" class=\"data row6 col2\" >0.1155</td>\n",
              "      <td id=\"T_18eb3_row6_col3\" class=\"data row6 col3\" >0.3187</td>\n",
              "      <td id=\"T_18eb3_row6_col4\" class=\"data row6 col4\" >0.8567</td>\n",
              "      <td id=\"T_18eb3_row6_col5\" class=\"data row6 col5\" >0.1612</td>\n",
              "      <td id=\"T_18eb3_row6_col6\" class=\"data row6 col6\" >1.2306</td>\n",
              "      <td id=\"T_18eb3_row6_col7\" class=\"data row6 col7\" >0.2400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
              "      <td id=\"T_18eb3_row7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_18eb3_row7_col1\" class=\"data row7 col1\" >0.2494</td>\n",
              "      <td id=\"T_18eb3_row7_col2\" class=\"data row7 col2\" >0.1152</td>\n",
              "      <td id=\"T_18eb3_row7_col3\" class=\"data row7 col3\" >0.3340</td>\n",
              "      <td id=\"T_18eb3_row7_col4\" class=\"data row7 col4\" >0.8541</td>\n",
              "      <td id=\"T_18eb3_row7_col5\" class=\"data row7 col5\" >0.1745</td>\n",
              "      <td id=\"T_18eb3_row7_col6\" class=\"data row7 col6\" >1.3086</td>\n",
              "      <td id=\"T_18eb3_row7_col7\" class=\"data row7 col7\" >0.3940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row8\" class=\"row_heading level0 row8\" >br</th>\n",
              "      <td id=\"T_18eb3_row8_col0\" class=\"data row8 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_18eb3_row8_col1\" class=\"data row8 col1\" >0.2602</td>\n",
              "      <td id=\"T_18eb3_row8_col2\" class=\"data row8 col2\" >0.1252</td>\n",
              "      <td id=\"T_18eb3_row8_col3\" class=\"data row8 col3\" >0.3456</td>\n",
              "      <td id=\"T_18eb3_row8_col4\" class=\"data row8 col4\" >0.8484</td>\n",
              "      <td id=\"T_18eb3_row8_col5\" class=\"data row8 col5\" >0.1819</td>\n",
              "      <td id=\"T_18eb3_row8_col6\" class=\"data row8 col6\" >1.3856</td>\n",
              "      <td id=\"T_18eb3_row8_col7\" class=\"data row8 col7\" >0.1570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row9\" class=\"row_heading level0 row9\" >dt</th>\n",
              "      <td id=\"T_18eb3_row9_col0\" class=\"data row9 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_18eb3_row9_col1\" class=\"data row9 col1\" >0.2314</td>\n",
              "      <td id=\"T_18eb3_row9_col2\" class=\"data row9 col2\" >0.1196</td>\n",
              "      <td id=\"T_18eb3_row9_col3\" class=\"data row9 col3\" >0.3322</td>\n",
              "      <td id=\"T_18eb3_row9_col4\" class=\"data row9 col4\" >0.8374</td>\n",
              "      <td id=\"T_18eb3_row9_col5\" class=\"data row9 col5\" >0.1660</td>\n",
              "      <td id=\"T_18eb3_row9_col6\" class=\"data row9 col6\" >1.1477</td>\n",
              "      <td id=\"T_18eb3_row9_col7\" class=\"data row9 col7\" >0.1540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row10\" class=\"row_heading level0 row10\" >lr</th>\n",
              "      <td id=\"T_18eb3_row10_col0\" class=\"data row10 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_18eb3_row10_col1\" class=\"data row10 col1\" >0.2569</td>\n",
              "      <td id=\"T_18eb3_row10_col2\" class=\"data row10 col2\" >0.1527</td>\n",
              "      <td id=\"T_18eb3_row10_col3\" class=\"data row10 col3\" >0.3695</td>\n",
              "      <td id=\"T_18eb3_row10_col4\" class=\"data row10 col4\" >0.8163</td>\n",
              "      <td id=\"T_18eb3_row10_col5\" class=\"data row10 col5\" >0.1839</td>\n",
              "      <td id=\"T_18eb3_row10_col6\" class=\"data row10 col6\" >1.3264</td>\n",
              "      <td id=\"T_18eb3_row10_col7\" class=\"data row10 col7\" >1.4820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row11\" class=\"row_heading level0 row11\" >omp</th>\n",
              "      <td id=\"T_18eb3_row11_col0\" class=\"data row11 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_18eb3_row11_col1\" class=\"data row11 col1\" >0.3079</td>\n",
              "      <td id=\"T_18eb3_row11_col2\" class=\"data row11 col2\" >0.1829</td>\n",
              "      <td id=\"T_18eb3_row11_col3\" class=\"data row11 col3\" >0.4167</td>\n",
              "      <td id=\"T_18eb3_row11_col4\" class=\"data row11 col4\" >0.7795</td>\n",
              "      <td id=\"T_18eb3_row11_col5\" class=\"data row11 col5\" >0.2073</td>\n",
              "      <td id=\"T_18eb3_row11_col6\" class=\"data row11 col6\" >1.5178</td>\n",
              "      <td id=\"T_18eb3_row11_col7\" class=\"data row11 col7\" >0.1560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row12\" class=\"row_heading level0 row12\" >en</th>\n",
              "      <td id=\"T_18eb3_row12_col0\" class=\"data row12 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_18eb3_row12_col1\" class=\"data row12 col1\" >0.3079</td>\n",
              "      <td id=\"T_18eb3_row12_col2\" class=\"data row12 col2\" >0.1832</td>\n",
              "      <td id=\"T_18eb3_row12_col3\" class=\"data row12 col3\" >0.4170</td>\n",
              "      <td id=\"T_18eb3_row12_col4\" class=\"data row12 col4\" >0.7771</td>\n",
              "      <td id=\"T_18eb3_row12_col5\" class=\"data row12 col5\" >0.2077</td>\n",
              "      <td id=\"T_18eb3_row12_col6\" class=\"data row12 col6\" >1.5246</td>\n",
              "      <td id=\"T_18eb3_row12_col7\" class=\"data row12 col7\" >0.3050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row13\" class=\"row_heading level0 row13\" >lasso</th>\n",
              "      <td id=\"T_18eb3_row13_col0\" class=\"data row13 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_18eb3_row13_col1\" class=\"data row13 col1\" >0.3082</td>\n",
              "      <td id=\"T_18eb3_row13_col2\" class=\"data row13 col2\" >0.1847</td>\n",
              "      <td id=\"T_18eb3_row13_col3\" class=\"data row13 col3\" >0.4184</td>\n",
              "      <td id=\"T_18eb3_row13_col4\" class=\"data row13 col4\" >0.7730</td>\n",
              "      <td id=\"T_18eb3_row13_col5\" class=\"data row13 col5\" >0.2089</td>\n",
              "      <td id=\"T_18eb3_row13_col6\" class=\"data row13 col6\" >1.5319</td>\n",
              "      <td id=\"T_18eb3_row13_col7\" class=\"data row13 col7\" >0.2840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row14\" class=\"row_heading level0 row14\" >llar</th>\n",
              "      <td id=\"T_18eb3_row14_col0\" class=\"data row14 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_18eb3_row14_col1\" class=\"data row14 col1\" >0.3082</td>\n",
              "      <td id=\"T_18eb3_row14_col2\" class=\"data row14 col2\" >0.1847</td>\n",
              "      <td id=\"T_18eb3_row14_col3\" class=\"data row14 col3\" >0.4184</td>\n",
              "      <td id=\"T_18eb3_row14_col4\" class=\"data row14 col4\" >0.7730</td>\n",
              "      <td id=\"T_18eb3_row14_col5\" class=\"data row14 col5\" >0.2089</td>\n",
              "      <td id=\"T_18eb3_row14_col6\" class=\"data row14 col6\" >1.5320</td>\n",
              "      <td id=\"T_18eb3_row14_col7\" class=\"data row14 col7\" >0.1590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row15\" class=\"row_heading level0 row15\" >knn</th>\n",
              "      <td id=\"T_18eb3_row15_col0\" class=\"data row15 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_18eb3_row15_col1\" class=\"data row15 col1\" >0.2711</td>\n",
              "      <td id=\"T_18eb3_row15_col2\" class=\"data row15 col2\" >0.1961</td>\n",
              "      <td id=\"T_18eb3_row15_col3\" class=\"data row15 col3\" >0.4167</td>\n",
              "      <td id=\"T_18eb3_row15_col4\" class=\"data row15 col4\" >0.7268</td>\n",
              "      <td id=\"T_18eb3_row15_col5\" class=\"data row15 col5\" >0.1970</td>\n",
              "      <td id=\"T_18eb3_row15_col6\" class=\"data row15 col6\" >1.3694</td>\n",
              "      <td id=\"T_18eb3_row15_col7\" class=\"data row15 col7\" >0.1640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row16\" class=\"row_heading level0 row16\" >huber</th>\n",
              "      <td id=\"T_18eb3_row16_col0\" class=\"data row16 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_18eb3_row16_col1\" class=\"data row16 col1\" >0.4035</td>\n",
              "      <td id=\"T_18eb3_row16_col2\" class=\"data row16 col2\" >0.3054</td>\n",
              "      <td id=\"T_18eb3_row16_col3\" class=\"data row16 col3\" >0.5344</td>\n",
              "      <td id=\"T_18eb3_row16_col4\" class=\"data row16 col4\" >0.5951</td>\n",
              "      <td id=\"T_18eb3_row16_col5\" class=\"data row16 col5\" >0.2610</td>\n",
              "      <td id=\"T_18eb3_row16_col6\" class=\"data row16 col6\" >1.9132</td>\n",
              "      <td id=\"T_18eb3_row16_col7\" class=\"data row16 col7\" >0.2810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row17\" class=\"row_heading level0 row17\" >par</th>\n",
              "      <td id=\"T_18eb3_row17_col0\" class=\"data row17 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_18eb3_row17_col1\" class=\"data row17 col1\" >0.5392</td>\n",
              "      <td id=\"T_18eb3_row17_col2\" class=\"data row17 col2\" >0.5611</td>\n",
              "      <td id=\"T_18eb3_row17_col3\" class=\"data row17 col3\" >0.7330</td>\n",
              "      <td id=\"T_18eb3_row17_col4\" class=\"data row17 col4\" >0.2145</td>\n",
              "      <td id=\"T_18eb3_row17_col5\" class=\"data row17 col5\" >0.3222</td>\n",
              "      <td id=\"T_18eb3_row17_col6\" class=\"data row17 col6\" >2.1791</td>\n",
              "      <td id=\"T_18eb3_row17_col7\" class=\"data row17 col7\" >0.1860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n",
              "      <td id=\"T_18eb3_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_18eb3_row18_col1\" class=\"data row18 col1\" >0.7403</td>\n",
              "      <td id=\"T_18eb3_row18_col2\" class=\"data row18 col2\" >0.9740</td>\n",
              "      <td id=\"T_18eb3_row18_col3\" class=\"data row18 col3\" >0.9554</td>\n",
              "      <td id=\"T_18eb3_row18_col4\" class=\"data row18 col4\" >-0.0917</td>\n",
              "      <td id=\"T_18eb3_row18_col5\" class=\"data row18 col5\" >0.5509</td>\n",
              "      <td id=\"T_18eb3_row18_col6\" class=\"data row18 col6\" >1.0525</td>\n",
              "      <td id=\"T_18eb3_row18_col7\" class=\"data row18 col7\" >0.2400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_18eb3_level0_row19\" class=\"row_heading level0 row19\" >lar</th>\n",
              "      <td id=\"T_18eb3_row19_col0\" class=\"data row19 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_18eb3_row19_col1\" class=\"data row19 col1\" >55214133149658270777668881237482890464057114641348512246461780098809856.0000</td>\n",
              "      <td id=\"T_18eb3_row19_col2\" class=\"data row19 col2\" >64742001386185403855238868252173722631348390376246008018134654300914114956891345913514972806366418959283009401252667623820780433050920730755072.0000</td>\n",
              "      <td id=\"T_18eb3_row19_col3\" class=\"data row19 col3\" >80462414446861712345058592266747208800565974505719373765457198895333376.0000</td>\n",
              "      <td id=\"T_18eb3_row19_col4\" class=\"data row19 col4\" >-49380861128856028252444719546141927631850272103415946447434903971402537474482148648669850453920995245735675768829335468948463580185866251796480.0000</td>\n",
              "      <td id=\"T_18eb3_row19_col5\" class=\"data row19 col5\" >21.5747</td>\n",
              "      <td id=\"T_18eb3_row19_col6\" class=\"data row19 col6\" >102359056182939206271126815933676364721083615999524627280521133052395520.0000</td>\n",
              "      <td id=\"T_18eb3_row19_col7\" class=\"data row19 col7\" >0.1760</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/85 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "642821481c7f4fe5b53548f0246fc62b"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/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": [
              "ExtraTreesRegressor(n_jobs=-1, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor(n_jobs=-1, random_state=42)</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 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 sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor(n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = create_model('lr')"
      ],
      "metadata": {
        "id": "n-chMPHhjzIT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "4e1b54a0ea5c40dba2681a3cd19168fb",
            "e2ce61ef16e748f0a8eae41f778fa990",
            "a6363a1d4a284e9b86ee5fef7781f80d",
            "d0379b3ceb9641ba91149d03ee072cd7",
            "216a611881184d94ba7eb89cb98f2905",
            "2429a041551e4068a943628bdb720db1",
            "366b78279b6e4069afcec972f6457489",
            "e1c635fb6211491080d6839bcac1e43d",
            "5c6eae138a734cea8d76645cc78ed239",
            "adebae5782f54053b95ff4987c714db9",
            "393be709f1324a12a1d8ffcba27cae8a"
          ]
        },
        "outputId": "23dc01cd-67be-44f5-e37d-527c5433afd2"
      },
      "execution_count": 21,
      "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 0x7f753a5f6c20>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_86f9b_row10_col0, #T_86f9b_row10_col1, #T_86f9b_row10_col2, #T_86f9b_row10_col3, #T_86f9b_row10_col4, #T_86f9b_row10_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_86f9b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_86f9b_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_86f9b_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_86f9b_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_86f9b_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_86f9b_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_86f9b_level0_col5\" class=\"col_heading level0 col5\" >MAPE</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_86f9b_row0_col0\" class=\"data row0 col0\" >0.2453</td>\n",
              "      <td id=\"T_86f9b_row0_col1\" class=\"data row0 col1\" >0.0941</td>\n",
              "      <td id=\"T_86f9b_row0_col2\" class=\"data row0 col2\" >0.3067</td>\n",
              "      <td id=\"T_86f9b_row0_col3\" class=\"data row0 col3\" >0.7751</td>\n",
              "      <td id=\"T_86f9b_row0_col4\" class=\"data row0 col4\" >0.1815</td>\n",
              "      <td id=\"T_86f9b_row0_col5\" class=\"data row0 col5\" >0.9464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_86f9b_row1_col0\" class=\"data row1 col0\" >0.2703</td>\n",
              "      <td id=\"T_86f9b_row1_col1\" class=\"data row1 col1\" >0.1485</td>\n",
              "      <td id=\"T_86f9b_row1_col2\" class=\"data row1 col2\" >0.3854</td>\n",
              "      <td id=\"T_86f9b_row1_col3\" class=\"data row1 col3\" >0.8273</td>\n",
              "      <td id=\"T_86f9b_row1_col4\" class=\"data row1 col4\" >0.1726</td>\n",
              "      <td id=\"T_86f9b_row1_col5\" class=\"data row1 col5\" >0.7036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_86f9b_row2_col0\" class=\"data row2 col0\" >0.2528</td>\n",
              "      <td id=\"T_86f9b_row2_col1\" class=\"data row2 col1\" >0.0894</td>\n",
              "      <td id=\"T_86f9b_row2_col2\" class=\"data row2 col2\" >0.2990</td>\n",
              "      <td id=\"T_86f9b_row2_col3\" class=\"data row2 col3\" >0.8693</td>\n",
              "      <td id=\"T_86f9b_row2_col4\" class=\"data row2 col4\" >0.1927</td>\n",
              "      <td id=\"T_86f9b_row2_col5\" class=\"data row2 col5\" >0.8409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_86f9b_row3_col0\" class=\"data row3 col0\" >0.2371</td>\n",
              "      <td id=\"T_86f9b_row3_col1\" class=\"data row3 col1\" >0.1542</td>\n",
              "      <td id=\"T_86f9b_row3_col2\" class=\"data row3 col2\" >0.3927</td>\n",
              "      <td id=\"T_86f9b_row3_col3\" class=\"data row3 col3\" >0.7749</td>\n",
              "      <td id=\"T_86f9b_row3_col4\" class=\"data row3 col4\" >0.1814</td>\n",
              "      <td id=\"T_86f9b_row3_col5\" class=\"data row3 col5\" >0.9829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_86f9b_row4_col0\" class=\"data row4 col0\" >0.1993</td>\n",
              "      <td id=\"T_86f9b_row4_col1\" class=\"data row4 col1\" >0.0621</td>\n",
              "      <td id=\"T_86f9b_row4_col2\" class=\"data row4 col2\" >0.2492</td>\n",
              "      <td id=\"T_86f9b_row4_col3\" class=\"data row4 col3\" >0.7503</td>\n",
              "      <td id=\"T_86f9b_row4_col4\" class=\"data row4 col4\" >0.1683</td>\n",
              "      <td id=\"T_86f9b_row4_col5\" class=\"data row4 col5\" >4.3386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_86f9b_row5_col0\" class=\"data row5 col0\" >0.1963</td>\n",
              "      <td id=\"T_86f9b_row5_col1\" class=\"data row5 col1\" >0.0717</td>\n",
              "      <td id=\"T_86f9b_row5_col2\" class=\"data row5 col2\" >0.2677</td>\n",
              "      <td id=\"T_86f9b_row5_col3\" class=\"data row5 col3\" >0.9453</td>\n",
              "      <td id=\"T_86f9b_row5_col4\" class=\"data row5 col4\" >0.1074</td>\n",
              "      <td id=\"T_86f9b_row5_col5\" class=\"data row5 col5\" >0.6592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_86f9b_row6_col0\" class=\"data row6 col0\" >0.1962</td>\n",
              "      <td id=\"T_86f9b_row6_col1\" class=\"data row6 col1\" >0.0888</td>\n",
              "      <td id=\"T_86f9b_row6_col2\" class=\"data row6 col2\" >0.2981</td>\n",
              "      <td id=\"T_86f9b_row6_col3\" class=\"data row6 col3\" >0.9167</td>\n",
              "      <td id=\"T_86f9b_row6_col4\" class=\"data row6 col4\" >0.1335</td>\n",
              "      <td id=\"T_86f9b_row6_col5\" class=\"data row6 col5\" >0.5038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_86f9b_row7_col0\" class=\"data row7 col0\" >0.4573</td>\n",
              "      <td id=\"T_86f9b_row7_col1\" class=\"data row7 col1\" >0.5048</td>\n",
              "      <td id=\"T_86f9b_row7_col2\" class=\"data row7 col2\" >0.7105</td>\n",
              "      <td id=\"T_86f9b_row7_col3\" class=\"data row7 col3\" >0.5887</td>\n",
              "      <td id=\"T_86f9b_row7_col4\" class=\"data row7 col4\" >0.3230</td>\n",
              "      <td id=\"T_86f9b_row7_col5\" class=\"data row7 col5\" >1.5012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_86f9b_row8_col0\" class=\"data row8 col0\" >0.2490</td>\n",
              "      <td id=\"T_86f9b_row8_col1\" class=\"data row8 col1\" >0.1202</td>\n",
              "      <td id=\"T_86f9b_row8_col2\" class=\"data row8 col2\" >0.3468</td>\n",
              "      <td id=\"T_86f9b_row8_col3\" class=\"data row8 col3\" >0.8172</td>\n",
              "      <td id=\"T_86f9b_row8_col4\" class=\"data row8 col4\" >0.2024</td>\n",
              "      <td id=\"T_86f9b_row8_col5\" class=\"data row8 col5\" >2.3542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_86f9b_row9_col0\" class=\"data row9 col0\" >0.2658</td>\n",
              "      <td id=\"T_86f9b_row9_col1\" class=\"data row9 col1\" >0.1927</td>\n",
              "      <td id=\"T_86f9b_row9_col2\" class=\"data row9 col2\" >0.4390</td>\n",
              "      <td id=\"T_86f9b_row9_col3\" class=\"data row9 col3\" >0.8982</td>\n",
              "      <td id=\"T_86f9b_row9_col4\" class=\"data row9 col4\" >0.1763</td>\n",
              "      <td id=\"T_86f9b_row9_col5\" class=\"data row9 col5\" >0.4329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_86f9b_row10_col0\" class=\"data row10 col0\" >0.2569</td>\n",
              "      <td id=\"T_86f9b_row10_col1\" class=\"data row10 col1\" >0.1527</td>\n",
              "      <td id=\"T_86f9b_row10_col2\" class=\"data row10 col2\" >0.3695</td>\n",
              "      <td id=\"T_86f9b_row10_col3\" class=\"data row10 col3\" >0.8163</td>\n",
              "      <td id=\"T_86f9b_row10_col4\" class=\"data row10 col4\" >0.1839</td>\n",
              "      <td id=\"T_86f9b_row10_col5\" class=\"data row10 col5\" >1.3264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_86f9b_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_86f9b_row11_col0\" class=\"data row11 col0\" >0.0719</td>\n",
              "      <td id=\"T_86f9b_row11_col1\" class=\"data row11 col1\" >0.1237</td>\n",
              "      <td id=\"T_86f9b_row11_col2\" class=\"data row11 col2\" >0.1270</td>\n",
              "      <td id=\"T_86f9b_row11_col3\" class=\"data row11 col3\" >0.0980</td>\n",
              "      <td id=\"T_86f9b_row11_col4\" class=\"data row11 col4\" >0.0535</td>\n",
              "      <td id=\"T_86f9b_row11_col5\" class=\"data row11 col5\" >1.1387</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": "4e1b54a0ea5c40dba2681a3cd19168fb"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_lr = tune_model(lr)"
      ],
      "metadata": {
        "id": "f6jEuRpkj1FA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494,
          "referenced_widgets": [
            "b29f87ca323d4f8298d5aeff23da0742",
            "b6f8d40f3a204fa4b4d95f4cc62ca09e",
            "f8c68960145c43c79e4ba9b735f2c042",
            "480bf6a8f7d445bb8aa5e83038aca53c",
            "517d90412b4847e08f2c58ac18cf62cf",
            "c44806a1276c47b49516279230213683",
            "6f40799620db414d94c68f295c77a8f5",
            "1811635d839944ed966e7bbfff79edd0",
            "74ffab70830d4368820194734f55cf2b",
            "9a2865621d6741d5bc1c1e9fb982c9df",
            "919e18045b424cfebb91578df649eb1c"
          ]
        },
        "outputId": "497fcd60-1861-47c9-ef40-a83dbcd5e052"
      },
      "execution_count": 22,
      "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 0x7f75305adbd0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_621ad_row10_col0, #T_621ad_row10_col1, #T_621ad_row10_col2, #T_621ad_row10_col3, #T_621ad_row10_col4, #T_621ad_row10_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_621ad\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_621ad_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_621ad_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_621ad_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_621ad_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_621ad_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_621ad_level0_col5\" class=\"col_heading level0 col5\" >MAPE</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_621ad_row0_col0\" class=\"data row0 col0\" >0.2453</td>\n",
              "      <td id=\"T_621ad_row0_col1\" class=\"data row0 col1\" >0.0941</td>\n",
              "      <td id=\"T_621ad_row0_col2\" class=\"data row0 col2\" >0.3067</td>\n",
              "      <td id=\"T_621ad_row0_col3\" class=\"data row0 col3\" >0.7751</td>\n",
              "      <td id=\"T_621ad_row0_col4\" class=\"data row0 col4\" >0.1815</td>\n",
              "      <td id=\"T_621ad_row0_col5\" class=\"data row0 col5\" >0.9464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_621ad_row1_col0\" class=\"data row1 col0\" >0.2703</td>\n",
              "      <td id=\"T_621ad_row1_col1\" class=\"data row1 col1\" >0.1485</td>\n",
              "      <td id=\"T_621ad_row1_col2\" class=\"data row1 col2\" >0.3854</td>\n",
              "      <td id=\"T_621ad_row1_col3\" class=\"data row1 col3\" >0.8273</td>\n",
              "      <td id=\"T_621ad_row1_col4\" class=\"data row1 col4\" >0.1726</td>\n",
              "      <td id=\"T_621ad_row1_col5\" class=\"data row1 col5\" >0.7036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_621ad_row2_col0\" class=\"data row2 col0\" >0.2528</td>\n",
              "      <td id=\"T_621ad_row2_col1\" class=\"data row2 col1\" >0.0894</td>\n",
              "      <td id=\"T_621ad_row2_col2\" class=\"data row2 col2\" >0.2990</td>\n",
              "      <td id=\"T_621ad_row2_col3\" class=\"data row2 col3\" >0.8693</td>\n",
              "      <td id=\"T_621ad_row2_col4\" class=\"data row2 col4\" >0.1927</td>\n",
              "      <td id=\"T_621ad_row2_col5\" class=\"data row2 col5\" >0.8409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_621ad_row3_col0\" class=\"data row3 col0\" >0.2371</td>\n",
              "      <td id=\"T_621ad_row3_col1\" class=\"data row3 col1\" >0.1542</td>\n",
              "      <td id=\"T_621ad_row3_col2\" class=\"data row3 col2\" >0.3927</td>\n",
              "      <td id=\"T_621ad_row3_col3\" class=\"data row3 col3\" >0.7749</td>\n",
              "      <td id=\"T_621ad_row3_col4\" class=\"data row3 col4\" >0.1814</td>\n",
              "      <td id=\"T_621ad_row3_col5\" class=\"data row3 col5\" >0.9829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_621ad_row4_col0\" class=\"data row4 col0\" >0.1993</td>\n",
              "      <td id=\"T_621ad_row4_col1\" class=\"data row4 col1\" >0.0621</td>\n",
              "      <td id=\"T_621ad_row4_col2\" class=\"data row4 col2\" >0.2492</td>\n",
              "      <td id=\"T_621ad_row4_col3\" class=\"data row4 col3\" >0.7503</td>\n",
              "      <td id=\"T_621ad_row4_col4\" class=\"data row4 col4\" >0.1683</td>\n",
              "      <td id=\"T_621ad_row4_col5\" class=\"data row4 col5\" >4.3386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_621ad_row5_col0\" class=\"data row5 col0\" >0.1963</td>\n",
              "      <td id=\"T_621ad_row5_col1\" class=\"data row5 col1\" >0.0717</td>\n",
              "      <td id=\"T_621ad_row5_col2\" class=\"data row5 col2\" >0.2677</td>\n",
              "      <td id=\"T_621ad_row5_col3\" class=\"data row5 col3\" >0.9453</td>\n",
              "      <td id=\"T_621ad_row5_col4\" class=\"data row5 col4\" >0.1074</td>\n",
              "      <td id=\"T_621ad_row5_col5\" class=\"data row5 col5\" >0.6592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_621ad_row6_col0\" class=\"data row6 col0\" >0.1962</td>\n",
              "      <td id=\"T_621ad_row6_col1\" class=\"data row6 col1\" >0.0888</td>\n",
              "      <td id=\"T_621ad_row6_col2\" class=\"data row6 col2\" >0.2981</td>\n",
              "      <td id=\"T_621ad_row6_col3\" class=\"data row6 col3\" >0.9167</td>\n",
              "      <td id=\"T_621ad_row6_col4\" class=\"data row6 col4\" >0.1335</td>\n",
              "      <td id=\"T_621ad_row6_col5\" class=\"data row6 col5\" >0.5038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_621ad_row7_col0\" class=\"data row7 col0\" >0.4573</td>\n",
              "      <td id=\"T_621ad_row7_col1\" class=\"data row7 col1\" >0.5048</td>\n",
              "      <td id=\"T_621ad_row7_col2\" class=\"data row7 col2\" >0.7105</td>\n",
              "      <td id=\"T_621ad_row7_col3\" class=\"data row7 col3\" >0.5887</td>\n",
              "      <td id=\"T_621ad_row7_col4\" class=\"data row7 col4\" >0.3230</td>\n",
              "      <td id=\"T_621ad_row7_col5\" class=\"data row7 col5\" >1.5012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_621ad_row8_col0\" class=\"data row8 col0\" >0.2490</td>\n",
              "      <td id=\"T_621ad_row8_col1\" class=\"data row8 col1\" >0.1202</td>\n",
              "      <td id=\"T_621ad_row8_col2\" class=\"data row8 col2\" >0.3468</td>\n",
              "      <td id=\"T_621ad_row8_col3\" class=\"data row8 col3\" >0.8172</td>\n",
              "      <td id=\"T_621ad_row8_col4\" class=\"data row8 col4\" >0.2024</td>\n",
              "      <td id=\"T_621ad_row8_col5\" class=\"data row8 col5\" >2.3542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_621ad_row9_col0\" class=\"data row9 col0\" >0.2658</td>\n",
              "      <td id=\"T_621ad_row9_col1\" class=\"data row9 col1\" >0.1927</td>\n",
              "      <td id=\"T_621ad_row9_col2\" class=\"data row9 col2\" >0.4390</td>\n",
              "      <td id=\"T_621ad_row9_col3\" class=\"data row9 col3\" >0.8982</td>\n",
              "      <td id=\"T_621ad_row9_col4\" class=\"data row9 col4\" >0.1763</td>\n",
              "      <td id=\"T_621ad_row9_col5\" class=\"data row9 col5\" >0.4329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_621ad_row10_col0\" class=\"data row10 col0\" >0.2569</td>\n",
              "      <td id=\"T_621ad_row10_col1\" class=\"data row10 col1\" >0.1527</td>\n",
              "      <td id=\"T_621ad_row10_col2\" class=\"data row10 col2\" >0.3695</td>\n",
              "      <td id=\"T_621ad_row10_col3\" class=\"data row10 col3\" >0.8163</td>\n",
              "      <td id=\"T_621ad_row10_col4\" class=\"data row10 col4\" >0.1839</td>\n",
              "      <td id=\"T_621ad_row10_col5\" class=\"data row10 col5\" >1.3264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_621ad_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_621ad_row11_col0\" class=\"data row11 col0\" >0.0719</td>\n",
              "      <td id=\"T_621ad_row11_col1\" class=\"data row11 col1\" >0.1237</td>\n",
              "      <td id=\"T_621ad_row11_col2\" class=\"data row11 col2\" >0.1270</td>\n",
              "      <td id=\"T_621ad_row11_col3\" class=\"data row11 col3\" >0.0980</td>\n",
              "      <td id=\"T_621ad_row11_col4\" class=\"data row11 col4\" >0.0535</td>\n",
              "      <td id=\"T_621ad_row11_col5\" class=\"data row11 col5\" >1.1387</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b29f87ca323d4f8298d5aeff23da0742"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(tuned_lr, plot='residuals')"
      ],
      "metadata": {
        "id": "WMbuX-Mcj2u_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "21e4f9da-c1af-40ec-b222-2fa3561f3330"
      },
      "execution_count": 23,
      "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 800x550 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gerar dados artificiais para prever (10 vezes maior)\n",
        "artificial_data = df.copy()\n",
        "artificial_data.drop('price', axis=1, inplace=True)  # Remover a variável alvo\n",
        "artificial_data = pd.concat([artificial_data] * 100, ignore_index=True)  # Criar dados 100 vezes maiores\n",
        "\n",
        "# Fazer previsões no conjunto de dados artificial\n",
        "predictions = predict_model(tuned_lr, data=artificial_data)\n",
        "\n",
        "# Visualizar as previsões\n",
        "print(predictions)"
      ],
      "metadata": {
        "id": "R7wfQKykj4SJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 995
        },
        "outputId": "02bbcfb2-0dc5-4b3f-e577-6e76a36e7eb3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       symboling fueltype aspiration doornumber      carbody drivewheel  \\\n",
            "0              3      gas        std        two  convertible        rwd   \n",
            "1              3      gas        std        two  convertible        rwd   \n",
            "2              1      gas        std        two    hatchback        rwd   \n",
            "3              2      gas        std       four        sedan        fwd   \n",
            "4              2      gas        std       four        sedan        4wd   \n",
            "...          ...      ...        ...        ...          ...        ...   \n",
            "20495         -1      gas        std       four        sedan        rwd   \n",
            "20496         -1      gas      turbo       four        sedan        rwd   \n",
            "20497         -1      gas        std       four        sedan        rwd   \n",
            "20498         -1   diesel      turbo       four        sedan        rwd   \n",
            "20499         -1      gas      turbo       four        sedan        rwd   \n",
            "\n",
            "      enginelocation   wheelbase   carlength   carwidth  ...  enginesize  \\\n",
            "0              front   88.599998  168.800003  64.099998  ...         130   \n",
            "1              front   88.599998  168.800003  64.099998  ...         130   \n",
            "2              front   94.500000  171.199997  65.500000  ...         152   \n",
            "3              front   99.800003  176.600006  66.199997  ...         109   \n",
            "4              front   99.400002  176.600006  66.400002  ...         136   \n",
            "...              ...         ...         ...        ...  ...         ...   \n",
            "20495          front  109.099998  188.800003  68.900002  ...         141   \n",
            "20496          front  109.099998  188.800003  68.800003  ...         141   \n",
            "20497          front  109.099998  188.800003  68.900002  ...         173   \n",
            "20498          front  109.099998  188.800003  68.900002  ...         145   \n",
            "20499          front  109.099998  188.800003  68.900002  ...         141   \n",
            "\n",
            "       fuelsystem boreratio stroke  compressionratio horsepower  peakrpm  \\\n",
            "0            mpfi      3.47   2.68               9.0        111     5000   \n",
            "1            mpfi      3.47   2.68               9.0        111     5000   \n",
            "2            mpfi      2.68   3.47               9.0        154     5000   \n",
            "3            mpfi      3.19   3.40              10.0        102     5500   \n",
            "4            mpfi      3.19   3.40               8.0        115     5500   \n",
            "...           ...       ...    ...               ...        ...      ...   \n",
            "20495        mpfi      3.78   3.15               9.5        114     5400   \n",
            "20496        mpfi      3.78   3.15               8.7        160     5300   \n",
            "20497        mpfi      3.58   2.87               8.8        134     5500   \n",
            "20498         idi      3.01   3.40              23.0        106     4800   \n",
            "20499        mpfi      3.78   3.15               9.5        114     5400   \n",
            "\n",
            "       citympg  highwaympg  prediction_label  \n",
            "0           21          27          0.114125  \n",
            "1           21          27          0.114125  \n",
            "2           19          26         -0.151092  \n",
            "3           24          30         -0.263031  \n",
            "4           18          22          0.621484  \n",
            "...        ...         ...               ...  \n",
            "20495       23          28          0.743887  \n",
            "20496       19          25          1.069716  \n",
            "20497       18          23          0.844193  \n",
            "20498       26          27          1.318196  \n",
            "20499       19          25          1.081318  \n",
            "\n",
            "[20500 rows x 24 columns]\n"
          ]
        }
      ]
    }
  ]
}