Bike_Sharing_Demand.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKqZqhSPbJmZ"
      },
      "source": [
        "# Predict Bike Sharing Demand with AutoGluon Template"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRrDCZIsbJme"
      },
      "source": [
        "## Project: Predict Bike Sharing Demand with AutoGluon\n",
        "This notebook is a template with each step that you need to complete for the project.\n",
        "\n",
        "Please fill in your code where there are explicit `?` markers in the notebook. You are welcome to add more cells and code as you see fit.\n",
        "\n",
        "Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.\n",
        "\n",
        "`File-> Export Notebook As... -> Export Notebook as HTML`\n",
        "\n",
        "There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.\n",
        "\n",
        "Completing the code template and writeup template will cover all of the rubric points for this project.\n",
        "\n",
        "The rubric contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this notebook and also discuss the results in the writeup file."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tS0N3buDbJmf"
      },
      "source": [
        "## Step 1: Create an account with Kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBy2NeErbJmg"
      },
      "source": [
        "### Create Kaggle Account and download API key\n",
        "Below is example of steps to get the API username and key. Each student will have their own username and key."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzG036BFbJmh"
      },
      "source": [
        "## Step 2: Download the Kaggle dataset using the kaggle python library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6hQVoccbJmh"
      },
      "source": [
        "### Open up Sagemaker Studio and use starter template"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m804_FGLbJmh"
      },
      "source": [
        "1. Notebook should be using a `ml.t3.medium` instance (2 vCPU + 4 GiB)\n",
        "2. Notebook should be using kernal: `Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukckBAwWbJmi"
      },
      "source": [
        "### Install packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Lmv-RkiLbJmi",
        "outputId": "89317e13-d2a7-4de0-ba63-9c755a8ee900"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.3.1)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (60.2.0)\n",
            "Collecting setuptools\n",
            "  Using cached setuptools-68.2.2-py3-none-any.whl.metadata (6.3 kB)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (0.41.2)\n",
            "Using cached setuptools-68.2.2-py3-none-any.whl (807 kB)\n",
            "Installing collected packages: setuptools\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 60.2.0\n",
            "    Uninstalling setuptools-60.2.0:\n",
            "      Successfully uninstalled setuptools-60.2.0\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",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "openxlab 0.0.28 requires setuptools~=60.2.0, but you have setuptools 68.2.2 which is incompatible.\n",
            "tensorflow 2.14.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\n",
            "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed setuptools-68.2.2\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "_distutils_hack",
                  "pkg_resources",
                  "setuptools"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mxnet<2.0.0 in /usr/local/lib/python3.10/dist-packages (1.9.1)\n",
            "Requirement already satisfied: bokeh==2.0.1 in /usr/local/lib/python3.10/dist-packages (2.0.1)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (6.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (2.8.2)\n",
            "Requirement already satisfied: Jinja2>=2.7 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (3.1.2)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (1.23.5)\n",
            "Requirement already satisfied: pillow>=4.0 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (9.4.0)\n",
            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (23.2)\n",
            "Requirement already satisfied: tornado>=5 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (6.3.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.10/dist-packages (from bokeh==2.0.1) (4.5.0)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.10/dist-packages (from mxnet<2.0.0) (2.28.2)\n",
            "Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from mxnet<2.0.0) (0.8.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=2.7->bokeh==2.0.1) (2.1.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.1->bokeh==2.0.1) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (2023.7.22)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: autogluon in /usr/local/lib/python3.10/dist-packages (0.8.2)\n",
            "Requirement already satisfied: autogluon.core==0.8.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==0.8.2->autogluon) (0.8.2)\n",
            "Requirement already satisfied: autogluon.features==0.8.2 in /usr/local/lib/python3.10/dist-packages (from autogluon) (0.8.2)\n",
            "Requirement already satisfied: autogluon.tabular==0.8.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==0.8.2->autogluon) (0.8.2)\n",
            "Requirement already satisfied: autogluon.multimodal==0.8.2 in /usr/local/lib/python3.10/dist-packages (from autogluon) (0.8.2)\n",
            "Requirement already satisfied: autogluon.timeseries==0.8.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries[all]==0.8.2->autogluon) (0.8.2)\n",
            "Requirement already satisfied: numpy<1.27,>=1.21 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.23.5)\n",
            "Requirement already satisfied: scipy<1.12,>=1.5.4 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.11.3)\n",
            "Requirement already satisfied: scikit-learn<1.3,>=1.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.2.2)\n",
            "Requirement already satisfied: networkx<4,>=3.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.2)\n",
            "Requirement already satisfied: pandas<1.6,>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.5.3)\n",
            "Requirement already satisfied: tqdm<5,>=4.38 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (4.65.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (2.28.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.7.1)\n",
            "Requirement already satisfied: boto3<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.28.72)\n",
            "Requirement already satisfied: autogluon.common==0.8.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (0.8.2)\n",
            "Requirement already satisfied: hyperopt<0.2.8,>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==0.8.2->autogluon) (0.2.7)\n",
            "Requirement already satisfied: ray<2.4,>=2.3 in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (2.3.1)\n",
            "Requirement already satisfied: pydantic<2.0,>=1.10.4 in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==0.8.2->autogluon) (1.10.13)\n",
            "Requirement already satisfied: grpcio<=1.50.0,>=1.42.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.core[all]==0.8.2->autogluon) (1.50.0)\n",
            "Requirement already satisfied: Pillow<9.6,>=9.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (9.4.0)\n",
            "Requirement already satisfied: jsonschema<4.18,>=4.14 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (4.17.3)\n",
            "Requirement already satisfied: seqeval<1.3.0,>=1.2.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (1.2.2)\n",
            "Requirement already satisfied: evaluate<0.4.0,>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.3.0)\n",
            "Requirement already satisfied: accelerate<0.17,>=0.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.16.0)\n",
            "Requirement already satisfied: timm<0.10.0,>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.9.8)\n",
            "Requirement already satisfied: torch<1.14,>=1.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (1.13.1)\n",
            "Requirement already satisfied: torchvision<0.15.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.14.1)\n",
            "Requirement already satisfied: scikit-image<0.20.0,>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.19.3)\n",
            "Requirement already satisfied: pytorch-lightning<1.10.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (1.9.5)\n",
            "Requirement already satisfied: text-unidecode<1.4,>=1.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (1.3)\n",
            "Requirement already satisfied: torchmetrics<0.12.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.11.4)\n",
            "Requirement already satisfied: transformers<4.27.0,>=4.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]<4.27.0,>=4.23.0->autogluon.multimodal==0.8.2->autogluon) (4.26.1)\n",
            "Requirement already satisfied: nptyping<2.5.0,>=1.4.4 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (2.4.1)\n",
            "Requirement already satisfied: omegaconf<2.3.0,>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (2.2.3)\n",
            "Requirement already satisfied: pytorch-metric-learning<2.0,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (1.7.3)\n",
            "Requirement already satisfied: nlpaug<1.2.0,>=1.1.10 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (1.1.11)\n",
            "Requirement already satisfied: nltk<4.0.0,>=3.4.5 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (3.8.1)\n",
            "Requirement already satisfied: openmim<0.4.0,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.3.9)\n",
            "Requirement already satisfied: defusedxml<0.7.2,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.7.1)\n",
            "Requirement already satisfied: jinja2<3.2,>=3.0.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (3.1.2)\n",
            "Requirement already satisfied: tensorboard<3,>=2.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (2.14.1)\n",
            "Requirement already satisfied: pytesseract<0.3.11,>=0.3.9 in /usr/local/lib/python3.10/dist-packages (from autogluon.multimodal==0.8.2->autogluon) (0.3.10)\n",
            "Requirement already satisfied: lightgbm<3.4,>=3.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==0.8.2->autogluon) (3.3.5)\n",
            "Requirement already satisfied: xgboost<1.8,>=1.6 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==0.8.2->autogluon) (1.7.6)\n",
            "Requirement already satisfied: fastai<2.8,>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==0.8.2->autogluon) (2.7.13)\n",
            "Requirement already satisfied: catboost<1.3,>=1.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.tabular[all]==0.8.2->autogluon) (1.2.2)\n",
            "Requirement already satisfied: joblib<2,>=1.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (1.3.2)\n",
            "Requirement already satisfied: statsmodels<0.15,>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.14.0)\n",
            "Requirement already satisfied: gluonts<0.14,>=0.13.1 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.13.7)\n",
            "Requirement already satisfied: statsforecast<1.5,>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (1.4.0)\n",
            "Requirement already satisfied: mlforecast<0.7.4,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.7.3)\n",
            "Requirement already satisfied: ujson<6,>=5 in /usr/local/lib/python3.10/dist-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (5.8.0)\n",
            "Requirement already satisfied: psutil<6,>=5.7.3 in /usr/local/lib/python3.10/dist-packages (from autogluon.common==0.8.2->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (5.9.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from autogluon.common==0.8.2->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (68.2.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate<0.17,>=0.9->autogluon.multimodal==0.8.2->autogluon) (23.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate<0.17,>=0.9->autogluon.multimodal==0.8.2->autogluon) (6.0.1)\n",
            "Requirement already satisfied: botocore<1.32.0,>=1.31.72 in /usr/local/lib/python3.10/dist-packages (from boto3<2,>=1.10->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.31.72)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from boto3<2,>=1.10->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (0.10.0)\n",
            "Requirement already satisfied: s3transfer<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from boto3<2,>=1.10->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (0.7.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==0.8.2->autogluon) (0.8.4)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==0.8.2->autogluon) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==0.8.2->autogluon) (1.16.0)\n",
            "Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (2.14.6)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (0.3.7)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (0.70.15)\n",
            "Requirement already satisfied: fsspec>=2021.05.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]>=2021.05.0->evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (2023.6.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (0.18.0)\n",
            "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.10/dist-packages (from evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (0.18.0)\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (23.3.1)\n",
            "Requirement already satisfied: fastdownload<2,>=0.0.5 in /usr/local/lib/python3.10/dist-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (0.0.7)\n",
            "Requirement already satisfied: fastcore<1.6,>=1.5.29 in /usr/local/lib/python3.10/dist-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (1.5.29)\n",
            "Requirement already satisfied: fastprogress>=0.2.4 in /usr/local/lib/python3.10/dist-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (1.0.3)\n",
            "Requirement already satisfied: spacy<4 in /usr/local/lib/python3.10/dist-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (3.6.1)\n",
            "Requirement already satisfied: toolz~=0.10 in /usr/local/lib/python3.10/dist-packages (from gluonts<0.14,>=0.13.1->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.12.0)\n",
            "Requirement already satisfied: typing-extensions~=4.0 in /usr/local/lib/python3.10/dist-packages (from gluonts<0.14,>=0.13.1->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (4.5.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==0.8.2->autogluon) (0.18.3)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==0.8.2->autogluon) (2.2.1)\n",
            "Requirement already satisfied: py4j in /usr/local/lib/python3.10/dist-packages (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==0.8.2->autogluon) (0.10.9.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2<3.2,>=3.0.3->autogluon.multimodal==0.8.2->autogluon) (2.1.3)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema<4.18,>=4.14->autogluon.multimodal==0.8.2->autogluon) (23.1.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema<4.18,>=4.14->autogluon.multimodal==0.8.2->autogluon) (0.20.0)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from lightgbm<3.4,>=3.3->autogluon.tabular[all]==0.8.2->autogluon) (0.41.2)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from mlforecast<0.7.4,>=0.7.0->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.56.4)\n",
            "Requirement already satisfied: window-ops in /usr/local/lib/python3.10/dist-packages (from mlforecast<0.7.4,>=0.7.0->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.0.14)\n",
            "Requirement already satisfied: gdown>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==0.8.2->autogluon) (4.6.6)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk<4.0.0,>=3.4.5->autogluon.multimodal==0.8.2->autogluon) (8.1.7)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk<4.0.0,>=3.4.5->autogluon.multimodal==0.8.2->autogluon) (2023.6.3)\n",
            "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /usr/local/lib/python3.10/dist-packages (from omegaconf<2.3.0,>=2.1.1->autogluon.multimodal==0.8.2->autogluon) (4.9.3)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.10/dist-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (0.4.6)\n",
            "Requirement already satisfied: model-index in /usr/local/lib/python3.10/dist-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (0.1.11)\n",
            "Requirement already satisfied: opendatalab in /usr/local/lib/python3.10/dist-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (0.0.10)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (13.4.2)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (0.9.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas<1.6,>=1.4.1->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<1.6,>=1.4.1->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (2023.3.post1)\n",
            "Requirement already satisfied: lightning-utilities>=0.6.0.post0 in /usr/local/lib/python3.10/dist-packages (from pytorch-lightning<1.10.0,>=1.9.0->autogluon.multimodal==0.8.2->autogluon) (0.9.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (3.12.4)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (1.0.7)\n",
            "Requirement already satisfied: protobuf!=3.19.5,>=3.15.3 in /usr/local/lib/python3.10/dist-packages (from ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (3.20.2)\n",
            "Requirement already satisfied: aiosignal in /usr/local/lib/python3.10/dist-packages (from ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (1.3.1)\n",
            "Requirement already satisfied: frozenlist in /usr/local/lib/python3.10/dist-packages (from ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (1.4.0)\n",
            "Requirement already satisfied: virtualenv>=20.0.24 in /usr/local/lib/python3.10/dist-packages (from ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (20.24.6)\n",
            "Requirement already satisfied: aiohttp>=3.7 in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (3.8.6)\n",
            "Requirement already satisfied: aiohttp-cors in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.7.0)\n",
            "Requirement already satisfied: colorful in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.5.5)\n",
            "Requirement already satisfied: py-spy>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.3.14)\n",
            "Requirement already satisfied: gpustat>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (1.1.1)\n",
            "Requirement already satisfied: opencensus in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.11.3)\n",
            "Requirement already satisfied: prometheus-client>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.17.1)\n",
            "Requirement already satisfied: smart-open in /usr/local/lib/python3.10/dist-packages (from ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (6.4.0)\n",
            "Requirement already satisfied: tensorboardX>=1.9 in /usr/local/lib/python3.10/dist-packages (from ray[tune]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (2.6.2.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (2023.7.22)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image<0.20.0,>=0.19.1->autogluon.multimodal==0.8.2->autogluon) (2.31.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image<0.20.0,>=0.19.1->autogluon.multimodal==0.8.2->autogluon) (2023.9.26)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image<0.20.0,>=0.19.1->autogluon.multimodal==0.8.2->autogluon) (1.4.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<1.3,>=1.0->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.2.0)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels<0.15,>=0.13.0->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.5.3)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.10/dist-packages (from tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (1.4.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (3.5)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (3.0.1)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from timm<0.10.0,>=0.9.2->autogluon.multimodal==0.8.2->autogluon) (0.4.0)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch<1.14,>=1.9->autogluon.multimodal==0.8.2->autogluon) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.10/dist-packages (from torch<1.14,>=1.9->autogluon.multimodal==0.8.2->autogluon) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.10/dist-packages (from torch<1.14,>=1.9->autogluon.multimodal==0.8.2->autogluon) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch<1.14,>=1.9->autogluon.multimodal==0.8.2->autogluon) (11.7.99)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<4.27.0,>=4.23.0->transformers[sentencepiece]<4.27.0,>=4.23.0->autogluon.multimodal==0.8.2->autogluon) (0.13.3)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]<4.27.0,>=4.23.0->autogluon.multimodal==0.8.2->autogluon) (0.1.99)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (4.43.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.1.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.7->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (1.9.2)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate<0.4.0,>=0.2.2->autogluon.multimodal==0.8.2->autogluon) (9.0.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==0.8.2->autogluon) (4.11.2)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (1.3.1)\n",
            "Requirement already satisfied: nvidia-ml-py>=11.450.129 in /usr/local/lib/python3.10/dist-packages (from gpustat>=1.0.0->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (12.535.108)\n",
            "Requirement already satisfied: blessed>=1.17.1 in /usr/local/lib/python3.10/dist-packages (from gpustat>=1.0.0->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (1.20.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->mlforecast<0.7.4,>=0.7.0->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.39.1)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (2.0.10)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (0.9.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (0.10.3)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (3.3.0)\n",
            "Requirement already satisfied: distlib<1,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.0.24->ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.3.7)\n",
            "Requirement already satisfied: platformdirs<4,>=3.9.1 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.0.24->ray<2.4,>=2.3->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (3.11.0)\n",
            "Requirement already satisfied: ordered-set in /usr/local/lib/python3.10/dist-packages (from model-index->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (4.1.0)\n",
            "Requirement already satisfied: opencensus-context>=0.1.3 in /usr/local/lib/python3.10/dist-packages (from opencensus->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.1.3)\n",
            "Requirement already satisfied: google-api-core<3.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from opencensus->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (2.11.1)\n",
            "Requirement already satisfied: pycryptodome in /usr/local/lib/python3.10/dist-packages (from opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (3.19.0)\n",
            "Requirement already satisfied: openxlab in /usr/local/lib/python3.10/dist-packages (from opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (0.0.28)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost<1.3,>=1.1->autogluon.tabular[all]==0.8.2->autogluon) (8.2.3)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (2.16.1)\n",
            "Requirement already satisfied: wcwidth>=0.1.4 in /usr/local/lib/python3.10/dist-packages (from blessed>=1.17.1->gpustat>=1.0.0->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (0.2.8)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]<2.4,>=2.3; extra == \"all\"->autogluon.core[all]==0.8.2->autogluon) (1.61.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (0.1.2)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<3,>=2.9->autogluon.multimodal==0.8.2->autogluon) (3.2.2)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (0.1.3)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==0.8.2->autogluon) (2.5)\n",
            "Requirement already satisfied: oss2~=2.17.0 in /usr/local/lib/python3.10/dist-packages (from openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (2.17.0)\n",
            "Collecting setuptools (from autogluon.common==0.8.2->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon)\n",
            "  Downloading setuptools-60.2.0-py3-none-any.whl (953 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m953.1/953.1 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==0.8.2->autogluon) (1.7.1)\n",
            "Requirement already satisfied: crcmod>=1.7 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (1.7)\n",
            "Requirement already satisfied: aliyun-python-sdk-kms>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (2.16.2)\n",
            "Requirement already satisfied: aliyun-python-sdk-core>=2.13.12 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (2.14.0)\n",
            "Requirement already satisfied: cryptography>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (41.0.5)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==0.8.2->autogluon) (2.21)\n",
            "Installing collected packages: setuptools\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 68.2.2\n",
            "    Uninstalling setuptools-68.2.2:\n",
            "      Successfully uninstalled setuptools-68.2.2\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",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "cvxpy 1.3.2 requires setuptools>65.5.1, but you have setuptools 60.2.0 which is incompatible.\n",
            "tensorflow 2.14.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\n",
            "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed setuptools-60.2.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "_distutils_hack",
                  "pkg_resources",
                  "setuptools"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install -U pip\n",
        "!pip install -U setuptools wheel\n",
        "!pip install -U \"mxnet<2.0.0\" bokeh==2.0.1\n",
        "!pip install autogluon --no-cache-dir\n",
        "# Without --no-cache-dir, smaller aws instances may have trouble installing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpgmF7SYbJmk"
      },
      "source": [
        "### Setup Kaggle API Key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XxtMS1ObbJmk"
      },
      "outputs": [],
      "source": [
        "# create the .kaggle directory and an empty kaggle.json file\n",
        "!mkdir -p /root/.kaggle\n",
        "!touch /root/.kaggle/kaggle.json\n",
        "!chmod 600 /root/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P1YzxdAGbJmk"
      },
      "outputs": [],
      "source": [
        "# Fill in your user name and key from creating the kaggle account and API token file\n",
        "import json\n",
        "kaggle_username = \"ganeshchowdhary\"\n",
        "kaggle_key = \"9e46a5a30ab2b72b30f74f817f96761c\"\n",
        "\n",
        "# Save API token the kaggle.json file\n",
        "with open(\"/root/.kaggle/kaggle.json\", \"w\") as f:\n",
        "    f.write(json.dumps({\"username\": kaggle_username, \"key\": kaggle_key}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWliIwjUbJml"
      },
      "source": [
        "### Download and explore dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fn6XHwVdbJml",
        "outputId": "0f9f4b8a-d2ef-4a8f-99f2-57f3b056e3f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading bike-sharing-demand.zip to /content\n",
            "\r  0% 0.00/189k [00:00<?, ?B/s]\n",
            "\r100% 189k/189k [00:00<00:00, 80.4MB/s]\n",
            "Archive:  bike-sharing-demand.zip\n",
            "  inflating: sampleSubmission.csv    \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ],
      "source": [
        "# Download the dataset, it will be in a .zip file so you'll need to unzip it as well.\n",
        "!kaggle competitions download -c bike-sharing-demand\n",
        "# If you already downloaded it you can use the -o command to overwrite the file\n",
        "!unzip -o bike-sharing-demand.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OF3JKVqSbJmm"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from autogluon.tabular import TabularPredictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "9-_475tSbJmm",
        "outputId": "63e9132e-af58-4cc2-d1b3-6196b5f4303c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a06f8fd5-22bd-40cc-89ea-b157ff1bcf4c\" 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>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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-a06f8fd5-22bd-40cc-89ea-b157ff1bcf4c')\"\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-a06f8fd5-22bd-40cc-89ea-b157ff1bcf4c 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-a06f8fd5-22bd-40cc-89ea-b157ff1bcf4c');\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-4e4837b2-60dc-43fe-8bb4-f46064cc090a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e4837b2-60dc-43fe-8bb4-f46064cc090a')\"\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-4e4837b2-60dc-43fe-8bb4-f46064cc090a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
              "0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
              "1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
              "2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
              "3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
              "4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  casual  registered  count  \n",
              "0        81        0.0       3          13     16  \n",
              "1        80        0.0       8          32     40  \n",
              "2        80        0.0       5          27     32  \n",
              "3        75        0.0       3          10     13  \n",
              "4        75        0.0       0           1      1  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create the train dataset in pandas by reading the csv\n",
        "# Set the parsing of the datetime column so you can use some of the `dt` features in pandas later\n",
        "train =pd.read_csv('train.csv')\n",
        "train['datetime'] = pd.to_datetime(train['datetime'])\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "z2uv1vbkbJmm",
        "outputId": "c8c877cf-6af4-4fe7-a30a-792b44842401"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5c81fef9-f886-4a16-a2e9-5ae492fce426\" 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>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.00000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.506614</td>\n",
              "      <td>0.028569</td>\n",
              "      <td>0.680875</td>\n",
              "      <td>1.418427</td>\n",
              "      <td>20.23086</td>\n",
              "      <td>23.655084</td>\n",
              "      <td>61.886460</td>\n",
              "      <td>12.799395</td>\n",
              "      <td>36.021955</td>\n",
              "      <td>155.552177</td>\n",
              "      <td>191.574132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.116174</td>\n",
              "      <td>0.166599</td>\n",
              "      <td>0.466159</td>\n",
              "      <td>0.633839</td>\n",
              "      <td>7.79159</td>\n",
              "      <td>8.474601</td>\n",
              "      <td>19.245033</td>\n",
              "      <td>8.164537</td>\n",
              "      <td>49.960477</td>\n",
              "      <td>151.039033</td>\n",
              "      <td>181.144454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.82000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.94000</td>\n",
              "      <td>16.665000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>7.001500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>42.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.50000</td>\n",
              "      <td>24.240000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>12.998000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>145.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.24000</td>\n",
              "      <td>31.060000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>16.997900</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>222.000000</td>\n",
              "      <td>284.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>41.00000</td>\n",
              "      <td>45.455000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>56.996900</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</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-5c81fef9-f886-4a16-a2e9-5ae492fce426')\"\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-5c81fef9-f886-4a16-a2e9-5ae492fce426 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-5c81fef9-f886-4a16-a2e9-5ae492fce426');\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-3f372957-d5a1-4c2e-be21-2f0c6221042a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f372957-d5a1-4c2e-be21-2f0c6221042a')\"\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-3f372957-d5a1-4c2e-be21-2f0c6221042a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             season       holiday    workingday       weather         temp  \\\n",
              "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.00000   \n",
              "mean       2.506614      0.028569      0.680875      1.418427     20.23086   \n",
              "std        1.116174      0.166599      0.466159      0.633839      7.79159   \n",
              "min        1.000000      0.000000      0.000000      1.000000      0.82000   \n",
              "25%        2.000000      0.000000      0.000000      1.000000     13.94000   \n",
              "50%        3.000000      0.000000      1.000000      1.000000     20.50000   \n",
              "75%        4.000000      0.000000      1.000000      2.000000     26.24000   \n",
              "max        4.000000      1.000000      1.000000      4.000000     41.00000   \n",
              "\n",
              "              atemp      humidity     windspeed        casual    registered  \\\n",
              "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.000000   \n",
              "mean      23.655084     61.886460     12.799395     36.021955    155.552177   \n",
              "std        8.474601     19.245033      8.164537     49.960477    151.039033   \n",
              "min        0.760000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%       16.665000     47.000000      7.001500      4.000000     36.000000   \n",
              "50%       24.240000     62.000000     12.998000     17.000000    118.000000   \n",
              "75%       31.060000     77.000000     16.997900     49.000000    222.000000   \n",
              "max       45.455000    100.000000     56.996900    367.000000    886.000000   \n",
              "\n",
              "              count  \n",
              "count  10886.000000  \n",
              "mean     191.574132  \n",
              "std      181.144454  \n",
              "min        1.000000  \n",
              "25%       42.000000  \n",
              "50%      145.000000  \n",
              "75%      284.000000  \n",
              "max      977.000000  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Simple output of the train dataset to view some of the min/max/varition of the dataset features.\n",
        "train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yh4ISJ9sbJmn",
        "outputId": "87b613d2-b6cb-4de2-9540-500939e73088"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-fc79890e-5034-4d12-80aa-e973de12a42e\" 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>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>11.365</td>\n",
              "      <td>56</td>\n",
              "      <td>26.0027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>13.635</td>\n",
              "      <td>56</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>13.635</td>\n",
              "      <td>56</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</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-fc79890e-5034-4d12-80aa-e973de12a42e')\"\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-fc79890e-5034-4d12-80aa-e973de12a42e 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-fc79890e-5034-4d12-80aa-e973de12a42e');\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-93ee614d-e5cb-40f2-b650-b8aa49017ecf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93ee614d-e5cb-40f2-b650-b8aa49017ecf')\"\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-93ee614d-e5cb-40f2-b650-b8aa49017ecf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
              "0 2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
              "1 2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
              "2 2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
              "3 2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
              "4 2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
              "\n",
              "   humidity  windspeed  \n",
              "0        56    26.0027  \n",
              "1        56     0.0000  \n",
              "2        56     0.0000  \n",
              "3        56    11.0014  \n",
              "4        56    11.0014  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create the test pandas dataframe in pandas by reading the csv, remember to parse the datetime!\n",
        "test = pd.read_csv('test.csv')\n",
        "test['datetime'] = pd.to_datetime(test['datetime'])\n",
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "3YRkVZ6N73wa",
        "outputId": "fdbab786-d5b1-4771-d0f0-1e5768abc5f2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a70fae77-9db1-403d-a062-5ae7ff967330\" 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>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.493300</td>\n",
              "      <td>0.029108</td>\n",
              "      <td>0.685815</td>\n",
              "      <td>1.436778</td>\n",
              "      <td>20.620607</td>\n",
              "      <td>24.012865</td>\n",
              "      <td>64.125212</td>\n",
              "      <td>12.631157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.091258</td>\n",
              "      <td>0.168123</td>\n",
              "      <td>0.464226</td>\n",
              "      <td>0.648390</td>\n",
              "      <td>8.059583</td>\n",
              "      <td>8.782741</td>\n",
              "      <td>19.293391</td>\n",
              "      <td>8.250151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.940000</td>\n",
              "      <td>16.665000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>7.001500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.320000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>11.001400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>27.060000</td>\n",
              "      <td>31.060000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>16.997900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>40.180000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>55.998600</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-a70fae77-9db1-403d-a062-5ae7ff967330')\"\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-a70fae77-9db1-403d-a062-5ae7ff967330 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-a70fae77-9db1-403d-a062-5ae7ff967330');\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-03e60055-0e02-4884-8fc5-82e005b5126f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-03e60055-0e02-4884-8fc5-82e005b5126f')\"\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-03e60055-0e02-4884-8fc5-82e005b5126f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            season      holiday   workingday      weather         temp  \\\n",
              "count  6493.000000  6493.000000  6493.000000  6493.000000  6493.000000   \n",
              "mean      2.493300     0.029108     0.685815     1.436778    20.620607   \n",
              "std       1.091258     0.168123     0.464226     0.648390     8.059583   \n",
              "min       1.000000     0.000000     0.000000     1.000000     0.820000   \n",
              "25%       2.000000     0.000000     0.000000     1.000000    13.940000   \n",
              "50%       3.000000     0.000000     1.000000     1.000000    21.320000   \n",
              "75%       3.000000     0.000000     1.000000     2.000000    27.060000   \n",
              "max       4.000000     1.000000     1.000000     4.000000    40.180000   \n",
              "\n",
              "             atemp     humidity    windspeed  \n",
              "count  6493.000000  6493.000000  6493.000000  \n",
              "mean     24.012865    64.125212    12.631157  \n",
              "std       8.782741    19.293391     8.250151  \n",
              "min       0.000000    16.000000     0.000000  \n",
              "25%      16.665000    49.000000     7.001500  \n",
              "50%      25.000000    65.000000    11.001400  \n",
              "75%      31.060000    81.000000    16.997900  \n",
              "max      50.000000   100.000000    55.998600  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WEPRBhIEbJmn",
        "outputId": "0c8a5537-c1a5-41d0-8215-a8ea78c014bd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-53d5c09f-a06c-4e9e-a01a-c74bc40f6837\" 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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>0</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-53d5c09f-a06c-4e9e-a01a-c74bc40f6837')\"\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-53d5c09f-a06c-4e9e-a01a-c74bc40f6837 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-53d5c09f-a06c-4e9e-a01a-c74bc40f6837');\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-c6314024-1ffb-41be-be47-d25217ae8de5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6314024-1ffb-41be-be47-d25217ae8de5')\"\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-c6314024-1ffb-41be-be47-d25217ae8de5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             datetime  count\n",
              "0 2011-01-20 00:00:00      0\n",
              "1 2011-01-20 01:00:00      0\n",
              "2 2011-01-20 02:00:00      0\n",
              "3 2011-01-20 03:00:00      0\n",
              "4 2011-01-20 04:00:00      0"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Same thing as train and test dataset\n",
        "submission = pd.read_csv('sampleSubmission.csv')\n",
        "submission['datetime'] = pd.to_datetime(submission['datetime'])\n",
        "submission.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "M_ppMsL573wb",
        "outputId": "22f2c411-d15d-4fac-ed94-861952715837"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-09a2f869-9324-4a82-86b4-ccc53b189003\" 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>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6493.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</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-09a2f869-9324-4a82-86b4-ccc53b189003')\"\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-09a2f869-9324-4a82-86b4-ccc53b189003 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-09a2f869-9324-4a82-86b4-ccc53b189003');\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-c74ce1b5-d9e4-4c5e-b376-f1a82e34233d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c74ce1b5-d9e4-4c5e-b376-f1a82e34233d')\"\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-c74ce1b5-d9e4-4c5e-b376-f1a82e34233d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        count\n",
              "count  6493.0\n",
              "mean      0.0\n",
              "std       0.0\n",
              "min       0.0\n",
              "25%       0.0\n",
              "50%       0.0\n",
              "75%       0.0\n",
              "max       0.0"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "submission.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pk0QDk-LExsN",
        "outputId": "d05589af-ffb0-4823-fea4-f5a816618064"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   datetime    10886 non-null  datetime64[ns]\n",
            " 1   season      10886 non-null  int64         \n",
            " 2   holiday     10886 non-null  int64         \n",
            " 3   workingday  10886 non-null  int64         \n",
            " 4   weather     10886 non-null  int64         \n",
            " 5   temp        10886 non-null  float64       \n",
            " 6   atemp       10886 non-null  float64       \n",
            " 7   humidity    10886 non-null  int64         \n",
            " 8   windspeed   10886 non-null  float64       \n",
            " 9   casual      10886 non-null  int64         \n",
            " 10  registered  10886 non-null  int64         \n",
            " 11  count       10886 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(8)\n",
            "memory usage: 1020.7 KB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIkjXahMig-1",
        "outputId": "41d84075-1a81-49d0-baae-a4e3029bbbe5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6493 entries, 0 to 6492\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   datetime    6493 non-null   datetime64[ns]\n",
            " 1   season      6493 non-null   int64         \n",
            " 2   holiday     6493 non-null   int64         \n",
            " 3   workingday  6493 non-null   int64         \n",
            " 4   weather     6493 non-null   int64         \n",
            " 5   temp        6493 non-null   float64       \n",
            " 6   atemp       6493 non-null   float64       \n",
            " 7   humidity    6493 non-null   int64         \n",
            " 8   windspeed   6493 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(3), int64(5)\n",
            "memory usage: 456.7 KB\n"
          ]
        }
      ],
      "source": [
        "test.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6TiDctl73wc",
        "outputId": "df1d8fc4-b0a1-40aa-ef05-4fa37c901577"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6493 entries, 0 to 6492\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   datetime  6493 non-null   datetime64[ns]\n",
            " 1   count     6493 non-null   int64         \n",
            "dtypes: datetime64[ns](1), int64(1)\n",
            "memory usage: 101.6 KB\n"
          ]
        }
      ],
      "source": [
        "submission.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jk8Qdi9ebJmn"
      },
      "source": [
        "## Step 3: Train a model using AutoGluon’s Tabular Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CFccBXobJmo"
      },
      "source": [
        "Requirements:\n",
        "* We are prediting `count`, so it is the label we are setting.\n",
        "* Ignore `casual` and `registered` columns as they are also not present in the test dataset.\n",
        "* Use the `root_mean_squared_error` as the metric to use for evaluation.\n",
        "* Set a time limit of 10 minutes (600 seconds).\n",
        "* Use the preset `best_quality` to focus on creating the best model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g9SeC-KTFEBz"
      },
      "outputs": [],
      "source": [
        "# dropping the columns of casual and registered\n",
        "columns_to_drop = ['casual', 'registered']\n",
        "train.drop(columns_to_drop, axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6kgp0Cy73wd",
        "outputId": "18fd1c1e-5e34-4f6d-b5f2-dd9f58fad05d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
              "0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
              "1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
              "2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
              "3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
              "4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  count  \n",
              "0        81        0.0     16  \n",
              "1        80        0.0     40  \n",
              "2        80        0.0     32  \n",
              "3        75        0.0     13  \n",
              "4        75        0.0      1  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQOXk93rbJmo",
        "outputId": "60bf03ee-2359-464e-afb2-b6130cb6ee0c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20231027_140501/\"\n",
            "Presets specified: ['best_quality']\n",
            "Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20\n",
            "Beginning AutoGluon training ... Time limit = 600s\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20231027_140501/\"\n",
            "AutoGluon Version:  0.8.2\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Wed Aug 30 11:19:59 UTC 2023\n",
            "Disk Space Avail:   81.74 GB / 115.66 GB (70.7%)\n",
            "Train Data Rows:    10886\n",
            "Train Data Columns: 9\n",
            "Label Column: count\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).\n",
            "\tLabel info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)\n",
            "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11814.03 MB\n",
            "\tTrain Data (Original)  Memory Usage: 0.78 MB (0.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\t\tFitting DatetimeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tStage 5 Generators:\n",
            "\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('datetime', []) : 1 | ['datetime']\n",
            "\t\t('float', [])    : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])      : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "\t\t('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
            "\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "\t\t('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n",
            "\t0.2s = Fit runtime\n",
            "\t9 features in original data used to generate 13 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.98 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.28s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "User-specified model hyperparameters to be fit:\n",
            "{\n",
            "\t'NN_TORCH': {},\n",
            "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n",
            "\t'CAT': {},\n",
            "\t'XGB': {},\n",
            "\t'FASTAI': {},\n",
            "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n",
            "}\n",
            "AutoGluon will fit 2 stack levels (L1 to L2) ...\n",
            "Fitting 11 L1 models ...\n",
            "Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.71s of the 599.71s of remaining time.\n",
            "\t-101.5462\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.06s\t = Training   runtime\n",
            "\t0.1s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.49s of the 599.49s of remaining time.\n",
            "\t-84.1251\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.06s\t = Training   runtime\n",
            "\t0.09s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 399.3s of the 599.3s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-131.4609\t = Validation score   (-root_mean_squared_error)\n",
            "\t100.1s\t = Training   runtime\n",
            "\t18.66s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 283.62s of the 483.62s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-131.0542\t = Validation score   (-root_mean_squared_error)\n",
            "\t46.8s\t = Training   runtime\n",
            "\t2.75s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 232.38s of the 432.38s of remaining time.\n",
            "\t-116.5484\t = Validation score   (-root_mean_squared_error)\n",
            "\t20.19s\t = Training   runtime\n",
            "\t0.58s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 210.44s of the 410.44s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-130.7977\t = Validation score   (-root_mean_squared_error)\n",
            "\t181.39s\t = Training   runtime\n",
            "\t0.14s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 25.78s of the 225.77s of remaining time.\n",
            "\t-124.6007\t = Validation score   (-root_mean_squared_error)\n",
            "\t9.13s\t = Training   runtime\n",
            "\t0.6s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 14.97s of the 214.97s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-146.4528\t = Validation score   (-root_mean_squared_error)\n",
            "\t54.31s\t = Training   runtime\n",
            "\t0.86s\t = Validation runtime\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 359.99s of the 157.36s of remaining time.\n",
            "\t-84.1251\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.85s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "Fitting 9 L2 models ...\n",
            "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 156.47s of the 156.44s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-60.629\t = Validation score   (-root_mean_squared_error)\n",
            "\t91.43s\t = Training   runtime\n",
            "\t9.03s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 53.23s of the 53.21s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-55.063\t = Validation score   (-root_mean_squared_error)\n",
            "\t39.56s\t = Training   runtime\n",
            "\t0.38s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 10.06s of the 10.02s of remaining time.\n"
          ]
        }
      ],
      "source": [
        "predictor = TabularPredictor(label=\"count\",eval_metric=\"root_mean_squared_error\").fit(\n",
        "    train_data=train,\n",
        "    time_limit=600,\n",
        "    presets=\"best_quality\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6P5zSnxbJmp"
      },
      "source": [
        "### Review AutoGluon's training run with ranking of models that did the best."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dja5LkCBbJmq",
        "outputId": "0d730d72-4554-4464-b608-64dc67b18b0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                    model   score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0     WeightedEnsemble_L3  -52.549823       2.142198  58.676298                0.000000           0.279531            3       True          8\n",
            "1    ExtraTreesMSE_BAG_L2  -53.164395       1.640887  29.218662                0.550222           8.746243            2       True          7\n",
            "2  RandomForestMSE_BAG_L2  -53.187018       1.591976  49.650523                0.501311          29.178104            2       True          6\n",
            "3   KNeighborsDist_BAG_L1  -84.146423       0.032578   0.080386                0.032578           0.080386            1       True          2\n",
            "4     WeightedEnsemble_L2  -84.146423       0.038107   0.670419                0.005528           0.590033            2       True          5\n",
            "5   KNeighborsUnif_BAG_L1 -101.588176       0.025082   0.062524                0.025082           0.062524            1       True          1\n",
            "6  RandomForestMSE_BAG_L1 -116.552091       0.486802  13.011343                0.486802          13.011343            1       True          3\n",
            "7    ExtraTreesMSE_BAG_L1 -124.602302       0.546203   7.318166                0.546203           7.318166            1       True          4\n",
            "Number of models trained: 8\n",
            "Types of models trained:\n",
            "{'StackerEnsembleModel_XT', 'WeightedEnsembleModel', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_RF'}\n",
            "Bagging used: True  (with 8 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
            "('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
            "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
            "('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\ganes\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\autogluon\\core\\utils\\plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n",
            "  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
              "  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',\n",
              "  'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',\n",
              "  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},\n",
              " 'model_performance': {'KNeighborsUnif_BAG_L1': -101.58817625927213,\n",
              "  'KNeighborsDist_BAG_L1': -84.14642264302962,\n",
              "  'RandomForestMSE_BAG_L1': -116.55209143577434,\n",
              "  'ExtraTreesMSE_BAG_L1': -124.60230209225125,\n",
              "  'WeightedEnsemble_L2': -84.14642264302962,\n",
              "  'RandomForestMSE_BAG_L2': -53.187018324854016,\n",
              "  'ExtraTreesMSE_BAG_L2': -53.16439524550508,\n",
              "  'WeightedEnsemble_L3': -52.54982338728797},\n",
              " 'model_best': 'WeightedEnsemble_L3',\n",
              " 'model_paths': {'KNeighborsUnif_BAG_L1': ['KNeighborsUnif_BAG_L1'],\n",
              "  'KNeighborsDist_BAG_L1': ['KNeighborsDist_BAG_L1'],\n",
              "  'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],\n",
              "  'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],\n",
              "  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],\n",
              "  'RandomForestMSE_BAG_L2': ['RandomForestMSE_BAG_L2'],\n",
              "  'ExtraTreesMSE_BAG_L2': ['ExtraTreesMSE_BAG_L2'],\n",
              "  'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},\n",
              " 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.06252408027648926,\n",
              "  'KNeighborsDist_BAG_L1': 0.08038616180419922,\n",
              "  'RandomForestMSE_BAG_L1': 13.011343479156494,\n",
              "  'ExtraTreesMSE_BAG_L1': 7.3181657791137695,\n",
              "  'WeightedEnsemble_L2': 0.5900332927703857,\n",
              "  'RandomForestMSE_BAG_L2': 29.178103923797607,\n",
              "  'ExtraTreesMSE_BAG_L2': 8.746242761611938,\n",
              "  'WeightedEnsemble_L3': 0.27953147888183594},\n",
              " 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.025082111358642578,\n",
              "  'KNeighborsDist_BAG_L1': 0.032578468322753906,\n",
              "  'RandomForestMSE_BAG_L1': 0.4868016242980957,\n",
              "  'ExtraTreesMSE_BAG_L1': 0.5462026596069336,\n",
              "  'WeightedEnsemble_L2': 0.00552821159362793,\n",
              "  'RandomForestMSE_BAG_L2': 0.5013108253479004,\n",
              "  'ExtraTreesMSE_BAG_L2': 0.5502219200134277,\n",
              "  'WeightedEnsemble_L3': 0.0},\n",
              " 'num_bag_folds': 8,\n",
              " 'max_stack_level': 3,\n",
              " 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'KNeighborsDist_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'RandomForestMSE_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'WeightedEnsemble_L2': {'use_orig_features': False,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'RandomForestMSE_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'WeightedEnsemble_L3': {'use_orig_features': False,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True}},\n",
              " 'leaderboard':                     model   score_val  pred_time_val   fit_time  \\\n",
              " 0     WeightedEnsemble_L3  -52.549823       2.142198  58.676298   \n",
              " 1    ExtraTreesMSE_BAG_L2  -53.164395       1.640887  29.218662   \n",
              " 2  RandomForestMSE_BAG_L2  -53.187018       1.591976  49.650523   \n",
              " 3   KNeighborsDist_BAG_L1  -84.146423       0.032578   0.080386   \n",
              " 4     WeightedEnsemble_L2  -84.146423       0.038107   0.670419   \n",
              " 5   KNeighborsUnif_BAG_L1 -101.588176       0.025082   0.062524   \n",
              " 6  RandomForestMSE_BAG_L1 -116.552091       0.486802  13.011343   \n",
              " 7    ExtraTreesMSE_BAG_L1 -124.602302       0.546203   7.318166   \n",
              " \n",
              "    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
              " 0                0.000000           0.279531            3       True   \n",
              " 1                0.550222           8.746243            2       True   \n",
              " 2                0.501311          29.178104            2       True   \n",
              " 3                0.032578           0.080386            1       True   \n",
              " 4                0.005528           0.590033            2       True   \n",
              " 5                0.025082           0.062524            1       True   \n",
              " 6                0.486802          13.011343            1       True   \n",
              " 7                0.546203           7.318166            1       True   \n",
              " \n",
              "    fit_order  \n",
              " 0          8  \n",
              " 1          7  \n",
              " 2          6  \n",
              " 3          2  \n",
              " 4          5  \n",
              " 5          1  \n",
              " 6          3  \n",
              " 7          4  }"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor.fit_summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P6138oaI73we",
        "outputId": "a351f45b-2d17-4136-c161-ff6f0c51378e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'WeightedEnsemble_L3'"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Best_model = predictor.get_model_best()\n",
        "Best_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRLrtXhvbJmq"
      },
      "source": [
        "### Create predictions from test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgyYIwATbJmq",
        "outputId": "49f33ffb-e030-4dee-b8ef-e6c6492c7556"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    21.875324\n",
              "1    40.775326\n",
              "2    46.401787\n",
              "3    48.398968\n",
              "4    51.721954\n",
              "Name: count, dtype: float32"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = predictor.predict(test)\n",
        "predictions.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZFe4h_HbJmr"
      },
      "source": [
        "#### NOTE: Kaggle will reject the submission if we don't set everything to be > 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19wNJ39UbJmr",
        "outputId": "f2af3812-7cbc-48ea-c7cc-46db19ffdab8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    6493.000000\n",
              "mean      100.124626\n",
              "std        90.543274\n",
              "min         2.349966\n",
              "25%        18.697525\n",
              "50%        62.859482\n",
              "75%       169.989685\n",
              "max       363.852570\n",
              "Name: count, dtype: float64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Describe the `predictions` series to see if there are any negative values\n",
        "predictions.describe()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kcavLCsV73wg",
        "outputId": "9d0389f4-343a-4e3e-9fbe-0b6ef97807c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "k = 0\n",
        "#lessthan_zero = (predictions.describe() < k ).any() --> my_approach_1\n",
        "lessthan_zero = (predictions < k).any()\n",
        "lessthan_zero\n",
        "\n",
        "## there are no values that are less than zero"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPwbGo6hbJms"
      },
      "source": [
        "### Set predictions to submission dataframe, save, and submit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NjRthFxFbJms"
      },
      "outputs": [],
      "source": [
        "submission[\"count\"] = predictions\n",
        "submission.to_csv(\"submission.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jDuivFDZJmWd",
        "outputId": "550677f4-c263-4bfd-c291-a80ad2357c3b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>21.875324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>40.775326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>46.401787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>48.398968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>51.721954</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             datetime      count\n",
              "0 2011-01-20 00:00:00  21.875324\n",
              "1 2011-01-20 01:00:00  40.775326\n",
              "2 2011-01-20 02:00:00  46.401787\n",
              "3 2011-01-20 03:00:00  48.398968\n",
              "4 2011-01-20 04:00:00  51.721954"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "submission.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8I1vteUUbJms",
        "outputId": "bf77f88c-7eb6-413f-d6e2-917494407dda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "100% 188k/188k [00:02<00:00, 83.0kB/s]\n",
            "Successfully submitted to Bike Sharing Demand"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m \"first raw submission\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGbOfWP2bJms"
      },
      "source": [
        "#### View submission via the command line or in the web browser under the competition's page - `My Submissions`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPLm-putbJmt",
        "outputId": "7b043a2d-e289-4862-a587-ba9bd69da8a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fileName        date                 description           status    publicScore  privateScore  \n",
            "--------------  -------------------  --------------------  --------  -----------  ------------  \n",
            "submission.csv  2023-06-05 06:44:01  first raw submission  pending                              \n",
            "submission.csv  2023-06-05 06:03:28  first raw submission  complete  1.80367      1.80367       \n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWKyzWikbJmt"
      },
      "source": [
        "#### Initial score of 1.80367"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQe4TNfbbJmt"
      },
      "source": [
        "## Step 4: Exploratory Data Analysis and Creating an additional feature\n",
        "* Any additional feature will do, but a great suggestion would be to separate out the datetime into hour, day, or month parts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pJFj-x5n73wn"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "v25awMCEbJmv",
        "outputId": "f2b72e30-0138-4eb2-fc63-70e30f77ec00"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis\n",
        "train.hist()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qj_N83hKbJmv"
      },
      "outputs": [],
      "source": [
        "# create a new feature\n",
        "# Extract year, month, and day as separate features into train\n",
        "train['year'] = train['datetime'].dt.year\n",
        "train['month'] = train['datetime'].dt.month\n",
        "train['day'] = train['datetime'].dt.day\n",
        "\n",
        "# Extract year, month, and day as separate features into test\n",
        "test['year'] = test['datetime'].dt.year\n",
        "test['month'] = test['datetime'].dt.month\n",
        "test['day'] = test['datetime'].dt.day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNrWSUzaKvG4",
        "outputId": "c35cea1b-a3d4-4d2f-8a2a-51a47cb3454a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 13 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   datetime    10886 non-null  datetime64[ns]\n",
            " 1   season      10886 non-null  int64         \n",
            " 2   holiday     10886 non-null  int64         \n",
            " 3   workingday  10886 non-null  int64         \n",
            " 4   weather     10886 non-null  int64         \n",
            " 5   temp        10886 non-null  float64       \n",
            " 6   atemp       10886 non-null  float64       \n",
            " 7   humidity    10886 non-null  int64         \n",
            " 8   windspeed   10886 non-null  float64       \n",
            " 9   count       10886 non-null  int64         \n",
            " 10  year        10886 non-null  int64         \n",
            " 11  month       10886 non-null  int64         \n",
            " 12  day         10886 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(9)\n",
            "memory usage: 1.1 MB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OB8eyGmhK56H",
        "outputId": "7419db8e-16c2-417c-fb5c-ec89a72736e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6493 entries, 0 to 6492\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   datetime    6493 non-null   datetime64[ns]\n",
            " 1   season      6493 non-null   int64         \n",
            " 2   holiday     6493 non-null   int64         \n",
            " 3   workingday  6493 non-null   int64         \n",
            " 4   weather     6493 non-null   int64         \n",
            " 5   temp        6493 non-null   float64       \n",
            " 6   atemp       6493 non-null   float64       \n",
            " 7   humidity    6493 non-null   int64         \n",
            " 8   windspeed   6493 non-null   float64       \n",
            " 9   year        6493 non-null   int64         \n",
            " 10  month       6493 non-null   int64         \n",
            " 11  day         6493 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(8)\n",
            "memory usage: 608.8 KB\n"
          ]
        }
      ],
      "source": [
        "test.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nSPdbU94K8lr",
        "outputId": "276b9e6f-4f7a-466a-a5fa-ce071dd06374"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dcb02928-1d0b-4fe8-bc22-b5618e5aec22\" 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>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>count</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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-dcb02928-1d0b-4fe8-bc22-b5618e5aec22')\"\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-dcb02928-1d0b-4fe8-bc22-b5618e5aec22 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-dcb02928-1d0b-4fe8-bc22-b5618e5aec22');\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-e9d6180d-288d-4eb5-973a-ed826daad2ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9d6180d-288d-4eb5-973a-ed826daad2ce')\"\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-e9d6180d-288d-4eb5-973a-ed826daad2ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
              "0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
              "1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
              "2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
              "3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
              "4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  count  year  month  day  \n",
              "0        81        0.0     16  2011      1    1  \n",
              "1        80        0.0     40  2011      1    1  \n",
              "2        80        0.0     32  2011      1    1  \n",
              "3        75        0.0     13  2011      1    1  \n",
              "4        75        0.0      1  2011      1    1  "
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7Ljpld1kK8pU",
        "outputId": "8d68c6cb-e337-448b-9927-69ca22daaa16"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-44ceb6d4-38b4-4578-a184-b900d4ef943e\" 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>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>11.365</td>\n",
              "      <td>56</td>\n",
              "      <td>26.0027</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>13.635</td>\n",
              "      <td>56</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>13.635</td>\n",
              "      <td>56</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>20</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-44ceb6d4-38b4-4578-a184-b900d4ef943e')\"\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-44ceb6d4-38b4-4578-a184-b900d4ef943e 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-44ceb6d4-38b4-4578-a184-b900d4ef943e');\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-c329b0c9-e35d-4cba-a68f-cfcf67d33d30\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c329b0c9-e35d-4cba-a68f-cfcf67d33d30')\"\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-c329b0c9-e35d-4cba-a68f-cfcf67d33d30 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
              "0 2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
              "1 2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
              "2 2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
              "3 2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
              "4 2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
              "\n",
              "   humidity  windspeed  year  month  day  \n",
              "0        56    26.0027  2011      1   20  \n",
              "1        56     0.0000  2011      1   20  \n",
              "2        56     0.0000  2011      1   20  \n",
              "3        56    11.0014  2011      1   20  \n",
              "4        56    11.0014  2011      1   20  "
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFe65mkYbJmx"
      },
      "source": [
        "## Make category types for these so models know they are not just numbers\n",
        "* AutoGluon originally sees these as ints, but in reality they are int representations of a category.\n",
        "* Setting the dtype to category will classify these as categories in AutoGluon."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29dtbg9xbJmy"
      },
      "outputs": [],
      "source": [
        "train[\"season\"] = train['season'].astype(\"category\")\n",
        "\n",
        "train[\"weather\"] = train['weather'].astype(\"category\")\n",
        "\n",
        "\n",
        "test[\"season\"] = train['season'].astype(\"category\")\n",
        "\n",
        "test[\"weather\"] = train['weather'].astype(\"category\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ezxj-JwNmdLe"
      },
      "outputs": [],
      "source": [
        "# Extract time-based features\n",
        "train['hour_of_day'] = train['datetime'].dt.hour\n",
        "\n",
        "test['hour_of_day'] = test['datetime'].dt.hour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "SAOvPRkGbJmy",
        "outputId": "d68ecb91-2db9-43e7-a12d-33b750ee783a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-95190550-a7cd-4afd-8290-a8077d4787fb\" 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>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>count</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour_of_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</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-95190550-a7cd-4afd-8290-a8077d4787fb')\"\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-95190550-a7cd-4afd-8290-a8077d4787fb 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-95190550-a7cd-4afd-8290-a8077d4787fb');\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-0232e70d-77cd-4bfc-b92c-4146314b3ea1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0232e70d-77cd-4bfc-b92c-4146314b3ea1')\"\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-0232e70d-77cd-4bfc-b92c-4146314b3ea1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             datetime season  holiday  workingday weather  temp   atemp  \\\n",
              "0 2011-01-01 00:00:00      1        0           0       1  9.84  14.395   \n",
              "1 2011-01-01 01:00:00      1        0           0       1  9.02  13.635   \n",
              "2 2011-01-01 02:00:00      1        0           0       1  9.02  13.635   \n",
              "3 2011-01-01 03:00:00      1        0           0       1  9.84  14.395   \n",
              "4 2011-01-01 04:00:00      1        0           0       1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  count  year  month  day  hour_of_day  \n",
              "0        81        0.0     16  2011      1    1            0  \n",
              "1        80        0.0     40  2011      1    1            1  \n",
              "2        80        0.0     32  2011      1    1            2  \n",
              "3        75        0.0     13  2011      1    1            3  \n",
              "4        75        0.0      1  2011      1    1            4  "
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View are new feature\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "VKmz-9R8bJmy",
        "outputId": "cddb94f6-2951-4a3a-fc69-2f66e1fe0168"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# View histogram of all features again now with the hour feature\n",
        "train.hist();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lLALIN0Gj6CQ"
      },
      "outputs": [],
      "source": [
        "# Drop the original 'datetime' column since it's now not much needed\n",
        "train.drop('datetime', axis=1, inplace=True)\n",
        "test.drop('datetime', axis=1, inplace=True)\n",
        "\n",
        "\n",
        "# Drop the 'atemp' column since it's highly correlated with 'temp' column\n",
        "train.drop('atemp', axis=1, inplace=True)\n",
        "test.drop('atemp', axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Vl_Bttuej6K0",
        "outputId": "40b6254a-812f-49fb-c0af-f468ba941d5e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-71412c96-c4d1-4d0f-9bac-d6ba7330921c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>count</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour_of_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71412c96-c4d1-4d0f-9bac-d6ba7330921c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-71412c96-c4d1-4d0f-9bac-d6ba7330921c 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-71412c96-c4d1-4d0f-9bac-d6ba7330921c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  season  holiday  workingday weather  temp  humidity  windspeed  count  year  \\\n",
              "0      1        0           0       1  9.84        81        0.0     16  2011   \n",
              "1      1        0           0       1  9.02        80        0.0     40  2011   \n",
              "2      1        0           0       1  9.02        80        0.0     32  2011   \n",
              "3      1        0           0       1  9.84        75        0.0     13  2011   \n",
              "4      1        0           0       1  9.84        75        0.0      1  2011   \n",
              "\n",
              "   month  day  hour_of_day  \n",
              "0      1    1            0  \n",
              "1      1    1            1  \n",
              "2      1    1            2  \n",
              "3      1    1            3  \n",
              "4      1    1            4  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "##\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzqH7R89bJmz"
      },
      "source": [
        "## Step 5: Rerun the model with the same settings as before, just with more features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2FIqEq9bJmz",
        "outputId": "4556808a-36d7-442a-f167-f979d9c977dd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20231027_142210/\"\n",
            "Presets specified: ['best_quality']\n",
            "Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20\n",
            "Beginning AutoGluon training ... Time limit = 600s\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20231027_142210/\"\n",
            "AutoGluon Version:  0.8.2\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Wed Aug 30 11:19:59 UTC 2023\n",
            "Disk Space Avail:   80.77 GB / 115.66 GB (69.8%)\n",
            "Train Data Rows:    10886\n",
            "Train Data Columns: 11\n",
            "Label Column: count\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).\n",
            "\tLabel info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)\n",
            "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11275.57 MB\n",
            "\tTrain Data (Original)  Memory Usage: 0.81 MB (0.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\t\t\tNote: Converting 3 features to boolean dtype as they only contain 2 unique values.\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tStage 5 Generators:\n",
            "\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('category', []) : 2 | ['season', 'weather']\n",
            "\t\t('float', [])    : 2 | ['temp', 'windspeed']\n",
            "\t\t('int', [])      : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])  : 2 | ['season', 'weather']\n",
            "\t\t('float', [])     : 2 | ['temp', 'windspeed']\n",
            "\t\t('int', [])       : 4 | ['humidity', 'month', 'day', 'hour_of_day']\n",
            "\t\t('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']\n",
            "\t0.2s = Fit runtime\n",
            "\t11 features in original data used to generate 11 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.58 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.29s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "User-specified model hyperparameters to be fit:\n",
            "{\n",
            "\t'NN_TORCH': {},\n",
            "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n",
            "\t'CAT': {},\n",
            "\t'XGB': {},\n",
            "\t'FASTAI': {},\n",
            "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n",
            "}\n",
            "AutoGluon will fit 2 stack levels (L1 to L2) ...\n",
            "Fitting 11 L1 models ...\n",
            "Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.71s of the 599.7s of remaining time.\n",
            "\t-122.8565\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.05s\t = Training   runtime\n",
            "\t0.25s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.34s of the 599.34s of remaining time.\n",
            "\t-118.5979\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.04s\t = Training   runtime\n",
            "\t0.26s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 398.97s of the 598.97s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-37.2316\t = Validation score   (-root_mean_squared_error)\n",
            "\t150.65s\t = Training   runtime\n",
            "\t36.03s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 240.37s of the 440.37s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-38.1609\t = Validation score   (-root_mean_squared_error)\n",
            "\t72.21s\t = Training   runtime\n",
            "\t8.09s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 162.76s of the 362.76s of remaining time.\n",
            "\t-42.2878\t = Validation score   (-root_mean_squared_error)\n",
            "\t17.68s\t = Training   runtime\n",
            "\t0.63s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 143.35s of the 343.35s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-39.7824\t = Validation score   (-root_mean_squared_error)\n",
            "\t131.21s\t = Training   runtime\n",
            "\t0.44s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 8.96s of the 208.96s of remaining time.\n",
            "\t-41.1665\t = Validation score   (-root_mean_squared_error)\n",
            "\t10.78s\t = Training   runtime\n",
            "\t0.61s\t = Validation runtime\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 196.51s of remaining time.\n",
            "\t-36.1219\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.51s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "Fitting 9 L2 models ...\n",
            "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 195.98s of the 195.96s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-37.1413\t = Validation score   (-root_mean_squared_error)\n",
            "\t36.85s\t = Training   runtime\n",
            "\t0.58s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 155.49s of the 155.45s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-36.6553\t = Validation score   (-root_mean_squared_error)\n",
            "\t31.58s\t = Training   runtime\n",
            "\t0.18s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 117.06s of the 117.04s of remaining time.\n",
            "\t-36.6514\t = Validation score   (-root_mean_squared_error)\n",
            "\t45.43s\t = Training   runtime\n",
            "\t0.69s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 70.1s of the 70.08s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-36.424\t = Validation score   (-root_mean_squared_error)\n",
            "\t66.03s\t = Training   runtime\n",
            "\t0.12s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 0.9s of the 0.88s of remaining time.\n",
            "\t-36.2251\t = Validation score   (-root_mean_squared_error)\n",
            "\t9.57s\t = Training   runtime\n",
            "\t1.01s\t = Validation runtime\n",
            "Completed 1/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -11.29s of remaining time.\n",
            "\t-35.9978\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.45s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 611.79s ... Best model: \"WeightedEnsemble_L3\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20231027_142210/\")\n"
          ]
        }
      ],
      "source": [
        "predictor_new_features = TabularPredictor(label=\"count\", eval_metric=\"root_mean_squared_error\").fit(\n",
        "      train_data=train,\n",
        "      time_limit=600,\n",
        "      presets=\"best_quality\"\n",
        "\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcqpTU-mbJmz",
        "outputId": "2f2e9e50-e015-4f89-fc00-af145291f46b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0      WeightedEnsemble_L3  -35.997792      48.324065  535.683589                0.001325           0.451583            3       True         14\n",
            "1      WeightedEnsemble_L2  -36.121880      45.806661  383.035081                0.000950           0.507227            2       True          8\n",
            "2     ExtraTreesMSE_BAG_L2  -36.225059      47.329136  392.190303                1.008260           9.566547            2       True         13\n",
            "3          CatBoost_BAG_L2  -36.424036      46.442425  448.654925                0.121550          66.031170            2       True         12\n",
            "4   RandomForestMSE_BAG_L2  -36.651424      47.009740  428.052200                0.688864          45.428445            2       True         11\n",
            "5          LightGBM_BAG_L2  -36.655292      46.504066  414.205843                0.183190          31.582088            2       True         10\n",
            "6        LightGBMXT_BAG_L2  -37.141321      46.898870  419.474327                0.577995          36.850571            2       True          9\n",
            "7        LightGBMXT_BAG_L1  -37.231571      36.034460  150.650322               36.034460         150.650322            1       True          3\n",
            "8          LightGBM_BAG_L1  -38.160901       8.085391   72.205995                8.085391          72.205995            1       True          4\n",
            "9          CatBoost_BAG_L1  -39.782367       0.438799  131.213314                0.438799         131.213314            1       True          6\n",
            "10    ExtraTreesMSE_BAG_L1  -41.166506       0.612445   10.776107                0.612445          10.776107            1       True          7\n",
            "11  RandomForestMSE_BAG_L1  -42.287846       0.634616   17.682117                0.634616          17.682117            1       True          5\n",
            "12   KNeighborsDist_BAG_L1 -118.597890       0.262043    0.042556                0.262043           0.042556            1       True          2\n",
            "13   KNeighborsUnif_BAG_L1 -122.856549       0.253121    0.053345                0.253121           0.053345            1       True          1\n",
            "Number of models trained: 14\n",
            "Types of models trained:\n",
            "{'WeightedEnsembleModel', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_LGB', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_XT'}\n",
            "Bagging used: True  (with 8 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('category', [])  : 2 | ['season', 'weather']\n",
            "('float', [])     : 2 | ['temp', 'windspeed']\n",
            "('int', [])       : 4 | ['humidity', 'month', 'day', 'hour_of_day']\n",
            "('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n",
            "  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',\n",
              "  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
              "  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',\n",
              "  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',\n",
              "  'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',\n",
              "  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},\n",
              " 'model_performance': {'KNeighborsUnif_BAG_L1': -122.85654908089322,\n",
              "  'KNeighborsDist_BAG_L1': -118.59789000570196,\n",
              "  'LightGBMXT_BAG_L1': -37.231570691243626,\n",
              "  'LightGBM_BAG_L1': -38.16090059656444,\n",
              "  'RandomForestMSE_BAG_L1': -42.28784598731294,\n",
              "  'CatBoost_BAG_L1': -39.782366885272744,\n",
              "  'ExtraTreesMSE_BAG_L1': -41.166505929625785,\n",
              "  'WeightedEnsemble_L2': -36.12187995109878,\n",
              "  'LightGBMXT_BAG_L2': -37.14132071938984,\n",
              "  'LightGBM_BAG_L2': -36.65529233883672,\n",
              "  'RandomForestMSE_BAG_L2': -36.65142413061866,\n",
              "  'CatBoost_BAG_L2': -36.424035847546,\n",
              "  'ExtraTreesMSE_BAG_L2': -36.22505901965931,\n",
              "  'WeightedEnsemble_L3': -35.99779231177814},\n",
              " 'model_best': 'WeightedEnsemble_L3',\n",
              " 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20231027_142210/models/KNeighborsUnif_BAG_L1/',\n",
              "  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20231027_142210/models/KNeighborsDist_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20231027_142210/models/LightGBMXT_BAG_L1/',\n",
              "  'LightGBM_BAG_L1': 'AutogluonModels/ag-20231027_142210/models/LightGBM_BAG_L1/',\n",
              "  'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20231027_142210/models/RandomForestMSE_BAG_L1/',\n",
              "  'CatBoost_BAG_L1': 'AutogluonModels/ag-20231027_142210/models/CatBoost_BAG_L1/',\n",
              "  'ExtraTreesMSE_BAG_L1': 'AutogluonModels/ag-20231027_142210/models/ExtraTreesMSE_BAG_L1/',\n",
              "  'WeightedEnsemble_L2': 'AutogluonModels/ag-20231027_142210/models/WeightedEnsemble_L2/',\n",
              "  'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20231027_142210/models/LightGBMXT_BAG_L2/',\n",
              "  'LightGBM_BAG_L2': 'AutogluonModels/ag-20231027_142210/models/LightGBM_BAG_L2/',\n",
              "  'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20231027_142210/models/RandomForestMSE_BAG_L2/',\n",
              "  'CatBoost_BAG_L2': 'AutogluonModels/ag-20231027_142210/models/CatBoost_BAG_L2/',\n",
              "  'ExtraTreesMSE_BAG_L2': 'AutogluonModels/ag-20231027_142210/models/ExtraTreesMSE_BAG_L2/',\n",
              "  'WeightedEnsemble_L3': 'AutogluonModels/ag-20231027_142210/models/WeightedEnsemble_L3/'},\n",
              " 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.0533447265625,\n",
              "  'KNeighborsDist_BAG_L1': 0.042556047439575195,\n",
              "  'LightGBMXT_BAG_L1': 150.65032196044922,\n",
              "  'LightGBM_BAG_L1': 72.20599460601807,\n",
              "  'RandomForestMSE_BAG_L1': 17.682116746902466,\n",
              "  'CatBoost_BAG_L1': 131.21331405639648,\n",
              "  'ExtraTreesMSE_BAG_L1': 10.7761070728302,\n",
              "  'WeightedEnsemble_L2': 0.5072269439697266,\n",
              "  'LightGBMXT_BAG_L2': 36.850571393966675,\n",
              "  'LightGBM_BAG_L2': 31.582087993621826,\n",
              "  'RandomForestMSE_BAG_L2': 45.4284451007843,\n",
              "  'CatBoost_BAG_L2': 66.03116989135742,\n",
              "  'ExtraTreesMSE_BAG_L2': 9.566547393798828,\n",
              "  'WeightedEnsemble_L3': 0.4515833854675293},\n",
              " 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.25312089920043945,\n",
              "  'KNeighborsDist_BAG_L1': 0.26204347610473633,\n",
              "  'LightGBMXT_BAG_L1': 36.0344603061676,\n",
              "  'LightGBM_BAG_L1': 8.08539080619812,\n",
              "  'RandomForestMSE_BAG_L1': 0.6346158981323242,\n",
              "  'CatBoost_BAG_L1': 0.4387993812561035,\n",
              "  'ExtraTreesMSE_BAG_L1': 0.6124448776245117,\n",
              "  'WeightedEnsemble_L2': 0.0009500980377197266,\n",
              "  'LightGBMXT_BAG_L2': 0.5779945850372314,\n",
              "  'LightGBM_BAG_L2': 0.18319010734558105,\n",
              "  'RandomForestMSE_BAG_L2': 0.6888642311096191,\n",
              "  'CatBoost_BAG_L2': 0.12154984474182129,\n",
              "  'ExtraTreesMSE_BAG_L2': 1.0082604885101318,\n",
              "  'WeightedEnsemble_L3': 0.0013248920440673828},\n",
              " 'num_bag_folds': 8,\n",
              " 'max_stack_level': 3,\n",
              " 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'KNeighborsDist_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'LightGBMXT_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'LightGBM_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'RandomForestMSE_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'CatBoost_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'WeightedEnsemble_L2': {'use_orig_features': False,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'LightGBMXT_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'LightGBM_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'RandomForestMSE_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'CatBoost_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'WeightedEnsemble_L3': {'use_orig_features': False,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True}},\n",
              " 'leaderboard':                      model   score_val  pred_time_val    fit_time  \\\n",
              " 0      WeightedEnsemble_L3  -35.997792      48.324065  535.683589   \n",
              " 1      WeightedEnsemble_L2  -36.121880      45.806661  383.035081   \n",
              " 2     ExtraTreesMSE_BAG_L2  -36.225059      47.329136  392.190303   \n",
              " 3          CatBoost_BAG_L2  -36.424036      46.442425  448.654925   \n",
              " 4   RandomForestMSE_BAG_L2  -36.651424      47.009740  428.052200   \n",
              " 5          LightGBM_BAG_L2  -36.655292      46.504066  414.205843   \n",
              " 6        LightGBMXT_BAG_L2  -37.141321      46.898870  419.474327   \n",
              " 7        LightGBMXT_BAG_L1  -37.231571      36.034460  150.650322   \n",
              " 8          LightGBM_BAG_L1  -38.160901       8.085391   72.205995   \n",
              " 9          CatBoost_BAG_L1  -39.782367       0.438799  131.213314   \n",
              " 10    ExtraTreesMSE_BAG_L1  -41.166506       0.612445   10.776107   \n",
              " 11  RandomForestMSE_BAG_L1  -42.287846       0.634616   17.682117   \n",
              " 12   KNeighborsDist_BAG_L1 -118.597890       0.262043    0.042556   \n",
              " 13   KNeighborsUnif_BAG_L1 -122.856549       0.253121    0.053345   \n",
              " \n",
              "     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
              " 0                 0.001325           0.451583            3       True   \n",
              " 1                 0.000950           0.507227            2       True   \n",
              " 2                 1.008260           9.566547            2       True   \n",
              " 3                 0.121550          66.031170            2       True   \n",
              " 4                 0.688864          45.428445            2       True   \n",
              " 5                 0.183190          31.582088            2       True   \n",
              " 6                 0.577995          36.850571            2       True   \n",
              " 7                36.034460         150.650322            1       True   \n",
              " 8                 8.085391          72.205995            1       True   \n",
              " 9                 0.438799         131.213314            1       True   \n",
              " 10                0.612445          10.776107            1       True   \n",
              " 11                0.634616          17.682117            1       True   \n",
              " 12                0.262043           0.042556            1       True   \n",
              " 13                0.253121           0.053345            1       True   \n",
              " \n",
              "     fit_order  \n",
              " 0          14  \n",
              " 1           8  \n",
              " 2          13  \n",
              " 3          12  \n",
              " 4          11  \n",
              " 5          10  \n",
              " 6           9  \n",
              " 7           3  \n",
              " 8           4  \n",
              " 9           6  \n",
              " 10          7  \n",
              " 11          5  \n",
              " 12          2  \n",
              " 13          1  }"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor_new_features.fit_summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSspra3-j0TQ",
        "outputId": "e473b2b0-8d4d-4a9f-ddcc-3675121ed26f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    14.162282\n",
              "1     5.988323\n",
              "2     4.924861\n",
              "3     4.310821\n",
              "4     4.192080\n",
              "Name: count, dtype: float32"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions_new_features = predictor_new_features.predict(test)\n",
        "predictions_new_features.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYlYs4hybJm0",
        "outputId": "d9ac2562-ca50-423e-e090-a2b671f17ca3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Remember to set all negative values to zero\n",
        "negative_values = (predictions_new_features < 0).any()\n",
        "negative_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vXS0PieCbJm0"
      },
      "outputs": [],
      "source": [
        "# Same submitting predictions\n",
        "submission_new_features= submission\n",
        "submission_new_features[\"count\"] = predictions_new_features\n",
        "submission_new_features.to_csv(\"submission_new_features.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gYGDwpLK2_--",
        "outputId": "008973e3-7968-4919-ceb8-a8d7f9ac5351"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ffc2f244-dbf1-4f20-a938-a990119f5e04\" 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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>14.162282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>5.988323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>4.924861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>4.310821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>4.192080</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-ffc2f244-dbf1-4f20-a938-a990119f5e04')\"\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-ffc2f244-dbf1-4f20-a938-a990119f5e04 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-ffc2f244-dbf1-4f20-a938-a990119f5e04');\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-da53548a-a48d-4b72-a286-a9bd4f54e966\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da53548a-a48d-4b72-a286-a9bd4f54e966')\"\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-da53548a-a48d-4b72-a286-a9bd4f54e966 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             datetime      count\n",
              "0 2011-01-20 00:00:00  14.162282\n",
              "1 2011-01-20 01:00:00   5.988323\n",
              "2 2011-01-20 02:00:00   4.924861\n",
              "3 2011-01-20 03:00:00   4.310821\n",
              "4 2011-01-20 04:00:00   4.192080"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "submission_new_features.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-Tm7XHKbJm0",
        "outputId": "3f6bbf2e-c8dd-468d-812b-6ba4ed1d005b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "100% 188k/188k [00:00<00:00, 339kB/s]\n",
            "Successfully submitted to Bike Sharing Demand"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m \"new features\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VWstF4fbJm1",
        "outputId": "6dcd1ae7-1c25-4f10-cd4f-b9448be4652d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fileName                     date                 description           status    publicScore  privateScore  \n",
            "---------------------------  -------------------  --------------------  --------  -----------  ------------  \n",
            "submission_new_features.csv  2023-06-05 14:01:41  new features          complete  0.51204      0.51204       \n",
            "submission_new_features.csv  2023-06-05 13:34:38  new features          error                                \n",
            "submission.csv               2023-06-05 06:44:01  first raw submission  complete  1.81151      1.81151       \n",
            "submission.csv               2023-06-05 06:03:28  first raw submission  complete  1.80367      1.80367       \n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcQ0-ltEbJm1"
      },
      "source": [
        "#### New Score of 0.51204"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLg43AQcbJm1"
      },
      "source": [
        "## Step 6: Hyper parameter optimization\n",
        "* There are many options for hyper parameter optimization.\n",
        "* Options are to change the AutoGluon higher level parameters or the individual model hyperparameters.\n",
        "* The hyperparameters of the models themselves that are in AutoGluon. Those need the `hyperparameter` and `hyperparameter_tune_kwargs` arguments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vYHEnhrBkwIk"
      },
      "outputs": [],
      "source": [
        "# hyperparameters = {\n",
        "#     'NN': {'num_epochs': 10},\n",
        "#     'GBM': {'num_boost_round': 100, 'learning_rate': 0.01},\n",
        "#     'CAT': {'iterations': 50, 'learning_rate': 0.05},\n",
        "#     'RF': {'n_estimators': 100, 'max_depth': 6},\n",
        "# }\n",
        "\n",
        "hyperparameters = {\n",
        "    'GBM': {'num_boost_round': 100, 'learning_rate': 0.01},\n",
        "    'CAT': {'iterations': 50, 'learning_rate': 0.05},\n",
        "    'RF': {'n_estimators': 100, 'max_depth': 6},\n",
        "\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvB4ZAjubJm1",
        "outputId": "ea2e9922-a233-49a6-c91e-8b1b732b4f99"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20231027_144642/\"\n",
            "Presets specified: ['best_quality']\n",
            "Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20\n",
            "Beginning AutoGluon training ... Time limit = 1000s\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20231027_144642/\"\n",
            "AutoGluon Version:  0.8.2\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Wed Aug 30 11:19:59 UTC 2023\n",
            "Disk Space Avail:   79.54 GB / 115.66 GB (68.8%)\n",
            "Train Data Rows:    10886\n",
            "Train Data Columns: 11\n",
            "Label Column: count\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).\n",
            "\tLabel info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)\n",
            "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11047.46 MB\n",
            "\tTrain Data (Original)  Memory Usage: 0.81 MB (0.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\t\t\tNote: Converting 3 features to boolean dtype as they only contain 2 unique values.\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tStage 5 Generators:\n",
            "\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('category', []) : 2 | ['season', 'weather']\n",
            "\t\t('float', [])    : 2 | ['temp', 'windspeed']\n",
            "\t\t('int', [])      : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])  : 2 | ['season', 'weather']\n",
            "\t\t('float', [])     : 2 | ['temp', 'windspeed']\n",
            "\t\t('int', [])       : 4 | ['humidity', 'month', 'day', 'hour_of_day']\n",
            "\t\t('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']\n",
            "\t0.3s = Fit runtime\n",
            "\t11 features in original data used to generate 11 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.58 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.43s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "User-specified model hyperparameters to be fit:\n",
            "{\n",
            "\t'GBM': {'num_boost_round': 100, 'learning_rate': 0.01},\n",
            "\t'CAT': {'iterations': 50, 'learning_rate': 0.05},\n",
            "\t'RF': {'n_estimators': 100, 'max_depth': 6},\n",
            "}\n",
            "AutoGluon will fit 2 stack levels (L1 to L2) ...\n",
            "Fitting 3 L1 models ...\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 666.21s of the 999.56s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.4418\t = Validation score   (-root_mean_squared_error)\n",
            "\t28.19s\t = Training   runtime\n",
            "\t0.29s\t = Validation runtime\n",
            "Fitting model: RandomForest_BAG_L1 ... Training model for up to 630.76s of the 964.11s of remaining time.\n",
            "\t-97.1445\t = Validation score   (-root_mean_squared_error)\n",
            "\t1.25s\t = Training   runtime\n",
            "\t0.16s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 629.26s of the 962.61s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.5915\t = Validation score   (-root_mean_squared_error)\n",
            "\t22.51s\t = Training   runtime\n",
            "\t0.07s\t = Validation runtime\n",
            "Repeating k-fold bagging: 2/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 603.14s of the 936.49s of remaining time.\n",
            "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3386\t = Validation score   (-root_mean_squared_error)\n",
            "\t58.27s\t = Training   runtime\n",
            "\t0.45s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 567.4s of the 900.75s of remaining time.\n",
            "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-76.03\t = Validation score   (-root_mean_squared_error)\n",
            "\t43.52s\t = Training   runtime\n",
            "\t0.14s\t = Validation runtime\n",
            "Repeating k-fold bagging: 3/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 539.51s of the 872.86s of remaining time.\n",
            "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3177\t = Validation score   (-root_mean_squared_error)\n",
            "\t85.86s\t = Training   runtime\n",
            "\t0.62s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 505.58s of the 838.92s of remaining time.\n",
            "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.8785\t = Validation score   (-root_mean_squared_error)\n",
            "\t66.89s\t = Training   runtime\n",
            "\t0.23s\t = Validation runtime\n",
            "Repeating k-fold bagging: 4/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 478.22s of the 811.57s of remaining time.\n",
            "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3447\t = Validation score   (-root_mean_squared_error)\n",
            "\t116.69s\t = Training   runtime\n",
            "\t0.95s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 442.4s of the 775.75s of remaining time.\n",
            "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.7882\t = Validation score   (-root_mean_squared_error)\n",
            "\t89.91s\t = Training   runtime\n",
            "\t0.3s\t = Validation runtime\n",
            "Repeating k-fold bagging: 5/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 412.17s of the 745.52s of remaining time.\n",
            "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3378\t = Validation score   (-root_mean_squared_error)\n",
            "\t145.03s\t = Training   runtime\n",
            "\t1.09s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 379.98s of the 713.33s of remaining time.\n",
            "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.8077\t = Validation score   (-root_mean_squared_error)\n",
            "\t111.47s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Repeating k-fold bagging: 6/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 351.56s of the 684.91s of remaining time.\n",
            "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.342\t = Validation score   (-root_mean_squared_error)\n",
            "\t175.63s\t = Training   runtime\n",
            "\t1.33s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 312.22s of the 645.56s of remaining time.\n",
            "\tFitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.8407\t = Validation score   (-root_mean_squared_error)\n",
            "\t134.03s\t = Training   runtime\n",
            "\t0.45s\t = Validation runtime\n",
            "Repeating k-fold bagging: 7/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 284.88s of the 618.23s of remaining time.\n",
            "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3297\t = Validation score   (-root_mean_squared_error)\n",
            "\t215.74s\t = Training   runtime\n",
            "\t1.61s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 240.88s of the 574.23s of remaining time.\n",
            "\tFitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.8278\t = Validation score   (-root_mean_squared_error)\n",
            "\t156.36s\t = Training   runtime\n",
            "\t0.53s\t = Validation runtime\n",
            "Repeating k-fold bagging: 8/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 213.92s of the 547.27s of remaining time.\n",
            "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3379\t = Validation score   (-root_mean_squared_error)\n",
            "\t244.33s\t = Training   runtime\n",
            "\t1.92s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 181.47s of the 514.82s of remaining time.\n",
            "\tFitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.8177\t = Validation score   (-root_mean_squared_error)\n",
            "\t178.67s\t = Training   runtime\n",
            "\t0.63s\t = Validation runtime\n",
            "Repeating k-fold bagging: 9/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 151.88s of the 485.23s of remaining time.\n",
            "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3401\t = Validation score   (-root_mean_squared_error)\n",
            "\t271.41s\t = Training   runtime\n",
            "\t2.17s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 117.97s of the 451.32s of remaining time.\n",
            "\tFitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.8616\t = Validation score   (-root_mean_squared_error)\n",
            "\t201.27s\t = Training   runtime\n",
            "\t0.73s\t = Validation runtime\n",
            "Repeating k-fold bagging: 10/20\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 91.36s of the 424.71s of remaining time.\n",
            "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-96.3362\t = Validation score   (-root_mean_squared_error)\n",
            "\t302.08s\t = Training   runtime\n",
            "\t2.34s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 54.77s of the 388.12s of remaining time.\n",
            "\tFitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-75.8062\t = Validation score   (-root_mean_squared_error)\n",
            "\t223.77s\t = Training   runtime\n",
            "\t0.93s\t = Validation runtime\n",
            "Completed 10/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 360.59s of remaining time.\n",
            "\t-75.8062\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.24s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "Fitting 3 L2 models ...\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 360.33s of the 360.31s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-82.3833\t = Validation score   (-root_mean_squared_error)\n",
            "\t27.88s\t = Training   runtime\n",
            "\t0.22s\t = Validation runtime\n",
            "Fitting model: RandomForest_BAG_L2 ... Training model for up to 327.84s of the 327.82s of remaining time.\n",
            "\t-51.3849\t = Validation score   (-root_mean_squared_error)\n",
            "\t4.7s\t = Training   runtime\n",
            "\t0.3s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 322.73s of the 322.71s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-58.1868\t = Validation score   (-root_mean_squared_error)\n",
            "\t22.98s\t = Training   runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Repeating k-fold bagging: 2/20\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 291.82s of the 291.8s of remaining time.\n",
            "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-82.3714\t = Validation score   (-root_mean_squared_error)\n",
            "\t55.72s\t = Training   runtime\n",
            "\t0.43s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 259.05s of the 259.04s of remaining time.\n",
            "\tFitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-58.1242\t = Validation score   (-root_mean_squared_error)\n",
            "\t45.33s\t = Training   runtime\n",
            "\t0.19s\t = Validation runtime\n",
            "Repeating k-fold bagging: 3/20\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 230.16s of the 230.14s of remaining time.\n",
            "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-82.3536\t = Validation score   (-root_mean_squared_error)\n",
            "\t84.33s\t = Training   runtime\n",
            "\t0.6s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 193.47s of the 193.46s of remaining time.\n",
            "\tFitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-58.1039\t = Validation score   (-root_mean_squared_error)\n",
            "\t68.13s\t = Training   runtime\n",
            "\t0.25s\t = Validation runtime\n",
            "Repeating k-fold bagging: 4/20\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 165.72s of the 165.71s of remaining time.\n",
            "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-82.334\t = Validation score   (-root_mean_squared_error)\n",
            "\t115.56s\t = Training   runtime\n",
            "\t0.76s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 130.39s of the 130.37s of remaining time.\n",
            "\tFitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-58.1012\t = Validation score   (-root_mean_squared_error)\n",
            "\t90.92s\t = Training   runtime\n",
            "\t0.34s\t = Validation runtime\n",
            "Repeating k-fold bagging: 5/20\n",
            "Fitting model: LightGBM_BAG_L2 ... Training model for up to 101.16s of the 101.15s of remaining time.\n",
            "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-82.3199\t = Validation score   (-root_mean_squared_error)\n",
            "\t143.33s\t = Training   runtime\n",
            "\t1.0s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ... Training model for up to 68.76s of the 68.75s of remaining time.\n",
            "\tFitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t-58.0984\t = Validation score   (-root_mean_squared_error)\n",
            "\t112.94s\t = Training   runtime\n",
            "\t0.43s\t = Validation runtime\n",
            "Completed 5/20 k-fold bagging repeats ...\n",
            "Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 39.54s of remaining time.\n",
            "\t-51.3849\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.37s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 960.89s ... Best model: \"WeightedEnsemble_L3\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20231027_144642/\")\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<autogluon.tabular.predictor.predictor.TabularPredictor at 0x7ec59475fc40>"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# from autogluon.tabular import RMSLEMetric\n",
        "predictor_new_hpo = TabularPredictor(label='count', eval_metric=\"root_mean_squared_error\")\n",
        "predictor_new_hpo.fit(train,\n",
        "                      hyperparameters=hyperparameters,\n",
        "                      time_limit=1000,\n",
        "                      presets=\"best_quality\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saRSORGzbJm2",
        "outputId": "8ecc4cdd-bc87-4bc4-a002-9865bca89e04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                 model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0  RandomForest_BAG_L2 -51.384852       3.727229  531.796634                0.295079           4.700830            2       True          6\n",
            "1  WeightedEnsemble_L3 -51.384852       3.728483  532.170988                0.001255           0.374354            3       True          8\n",
            "2      CatBoost_BAG_L2 -58.098401       3.864426  640.031986                0.432276         112.936183            2       True          7\n",
            "3      CatBoost_BAG_L1 -75.806152       0.929440  223.771625                0.929440         223.771625            1       True          3\n",
            "4  WeightedEnsemble_L2 -75.806152       0.930159  224.010853                0.000719           0.239228            2       True          4\n",
            "5      LightGBM_BAG_L2 -82.319923       4.430006  670.426717                0.997857         143.330914            2       True          5\n",
            "6      LightGBM_BAG_L1 -96.336243       2.343704  302.076898                2.343704         302.076898            1       True          1\n",
            "7  RandomForest_BAG_L1 -97.144507       0.159005    1.247281                0.159005           1.247281            1       True          2\n",
            "Number of models trained: 8\n",
            "Types of models trained:\n",
            "{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost'}\n",
            "Bagging used: True  (with 8 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('category', [])  : 2 | ['season', 'weather']\n",
            "('float', [])     : 2 | ['temp', 'windspeed']\n",
            "('int', [])       : 4 | ['humidity', 'month', 'day', 'hour_of_day']\n",
            "('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"\n",
            "  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: \"pip install bokeh==2.0.1\"')\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'model_types': {'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'RandomForest_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
              "  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n",
              "  'RandomForest_BAG_L2': 'StackerEnsembleModel_RF',\n",
              "  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',\n",
              "  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},\n",
              " 'model_performance': {'LightGBM_BAG_L1': -96.33624280603883,\n",
              "  'RandomForest_BAG_L1': -97.1445067449418,\n",
              "  'CatBoost_BAG_L1': -75.80615157027592,\n",
              "  'WeightedEnsemble_L2': -75.80615157027592,\n",
              "  'LightGBM_BAG_L2': -82.31992326498917,\n",
              "  'RandomForest_BAG_L2': -51.38485248338163,\n",
              "  'CatBoost_BAG_L2': -58.098400595259506,\n",
              "  'WeightedEnsemble_L3': -51.38485248338163},\n",
              " 'model_best': 'WeightedEnsemble_L3',\n",
              " 'model_paths': {'LightGBM_BAG_L1': 'AutogluonModels/ag-20231027_144642/models/LightGBM_BAG_L1/',\n",
              "  'RandomForest_BAG_L1': 'AutogluonModels/ag-20231027_144642/models/RandomForest_BAG_L1/',\n",
              "  'CatBoost_BAG_L1': 'AutogluonModels/ag-20231027_144642/models/CatBoost_BAG_L1/',\n",
              "  'WeightedEnsemble_L2': 'AutogluonModels/ag-20231027_144642/models/WeightedEnsemble_L2/',\n",
              "  'LightGBM_BAG_L2': 'AutogluonModels/ag-20231027_144642/models/LightGBM_BAG_L2/',\n",
              "  'RandomForest_BAG_L2': 'AutogluonModels/ag-20231027_144642/models/RandomForest_BAG_L2/',\n",
              "  'CatBoost_BAG_L2': 'AutogluonModels/ag-20231027_144642/models/CatBoost_BAG_L2/',\n",
              "  'WeightedEnsemble_L3': 'AutogluonModels/ag-20231027_144642/models/WeightedEnsemble_L3/'},\n",
              " 'model_fit_times': {'LightGBM_BAG_L1': 302.07689809799194,\n",
              "  'RandomForest_BAG_L1': 1.2472805976867676,\n",
              "  'CatBoost_BAG_L1': 223.7716248035431,\n",
              "  'WeightedEnsemble_L2': 0.2392284870147705,\n",
              "  'LightGBM_BAG_L2': 143.33091354370117,\n",
              "  'RandomForest_BAG_L2': 4.700830459594727,\n",
              "  'CatBoost_BAG_L2': 112.93618297576904,\n",
              "  'WeightedEnsemble_L3': 0.37435412406921387},\n",
              " 'model_pred_times': {'LightGBM_BAG_L1': 2.3437039852142334,\n",
              "  'RandomForest_BAG_L1': 0.15900492668151855,\n",
              "  'CatBoost_BAG_L1': 0.9294402599334717,\n",
              "  'WeightedEnsemble_L2': 0.0007185935974121094,\n",
              "  'LightGBM_BAG_L2': 0.997856616973877,\n",
              "  'RandomForest_BAG_L2': 0.29507946968078613,\n",
              "  'CatBoost_BAG_L2': 0.43227648735046387,\n",
              "  'WeightedEnsemble_L3': 0.0012547969818115234},\n",
              " 'num_bag_folds': 8,\n",
              " 'max_stack_level': 3,\n",
              " 'model_hyperparams': {'LightGBM_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'RandomForest_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'CatBoost_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'WeightedEnsemble_L2': {'use_orig_features': False,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'LightGBM_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'RandomForest_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'CatBoost_BAG_L2': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'WeightedEnsemble_L3': {'use_orig_features': False,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True}},\n",
              " 'leaderboard':                  model  score_val  pred_time_val    fit_time  \\\n",
              " 0  RandomForest_BAG_L2 -51.384852       3.727229  531.796634   \n",
              " 1  WeightedEnsemble_L3 -51.384852       3.728483  532.170988   \n",
              " 2      CatBoost_BAG_L2 -58.098401       3.864426  640.031986   \n",
              " 3      CatBoost_BAG_L1 -75.806152       0.929440  223.771625   \n",
              " 4  WeightedEnsemble_L2 -75.806152       0.930159  224.010853   \n",
              " 5      LightGBM_BAG_L2 -82.319923       4.430006  670.426717   \n",
              " 6      LightGBM_BAG_L1 -96.336243       2.343704  302.076898   \n",
              " 7  RandomForest_BAG_L1 -97.144507       0.159005    1.247281   \n",
              " \n",
              "    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
              " 0                0.295079           4.700830            2       True   \n",
              " 1                0.001255           0.374354            3       True   \n",
              " 2                0.432276         112.936183            2       True   \n",
              " 3                0.929440         223.771625            1       True   \n",
              " 4                0.000719           0.239228            2       True   \n",
              " 5                0.997857         143.330914            2       True   \n",
              " 6                2.343704         302.076898            1       True   \n",
              " 7                0.159005           1.247281            1       True   \n",
              " \n",
              "    fit_order  \n",
              " 0          6  \n",
              " 1          8  \n",
              " 2          7  \n",
              " 3          3  \n",
              " 4          4  \n",
              " 5          5  \n",
              " 6          1  \n",
              " 7          2  }"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor_new_hpo.fit_summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2bQaKKilC2Y",
        "outputId": "843108e4-e04f-490f-e9cc-610b8cc4561d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    12.195380\n",
              "1     6.596550\n",
              "2     5.042928\n",
              "3     5.042928\n",
              "4     5.042928\n",
              "Name: count, dtype: float32"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor_new_hpo = predictor_new_hpo.predict(test)\n",
        "predictor_new_hpo.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfmBeBwVbJm2",
        "outputId": "98b4b67e-be01-4610-9531-2d4675b567cf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Remember to set all negative values to ze\n",
        "negative_values = (predictor_new_hpo < 0).any()\n",
        "negative_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Debm8NOPbJm2"
      },
      "outputs": [],
      "source": [
        "# Same submitting predictions\n",
        "submission_new_hpo = submission\n",
        "submission_new_hpo[\"count\"] =predictor_new_hpo\n",
        "submission_new_hpo.to_csv(\"submission_new_hpo.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JlPtoV12bJm2",
        "outputId": "6b87ad7e-6286-4bf3-faee-a4016017634f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "100% 188k/188k [00:00<00:00, 317kB/s]\n",
            "Successfully submitted to Bike Sharing Demand"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m \"new features with hyperparameters\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aitRXmLVbJm3",
        "outputId": "97df00b7-ef85-43ce-be51-66c34d320283"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fileName                     date                 description                        status    publicScore  privateScore  \n",
            "---------------------------  -------------------  ---------------------------------  --------  -----------  ------------  \n",
            "submission_new_hpo.csv       2023-06-05 14:23:31  new features with hyperparameters  complete  0.54099      0.54099       \n",
            "submission_new_features.csv  2023-06-05 14:01:41  new features                       complete  0.51204      0.51204       \n",
            "submission_new_features.csv  2023-06-05 13:34:38  new features                       error                                \n",
            "submission.csv               2023-06-05 06:44:01  first raw submission               complete  1.81151      1.81151       \n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgLaqShvbJm3"
      },
      "source": [
        "#### New Score of 0.54099"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoDuHbxfbJm3"
      },
      "source": [
        "## Step 7: Write a Report\n",
        "### Refer to the markdown file for the full report\n",
        "### Creating plots and table for report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "2npXooVybJm5",
        "outputId": "0869d687-7c81-4fdf-d853-7174b4cd5cb8"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Taking the top model score from each training run and creating a line plot to show improvement\n",
        "# You can create these in the notebook and save them to PNG or use some other tool (e.g. google sheets, excel)\n",
        "fig = pd.DataFrame(\n",
        "    {\n",
        "        \"model\": [\"initial\", \"add_features\", \"hpo\"],\n",
        "        \"score\": [-53.074650,-36.018923,-51.384852 ]\n",
        "    }\n",
        ").plot(x=\"model\", y=\"score\", figsize=(8, 6)).get_figure()\n",
        "fig.savefig('model_train_score.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "RaCdesjEbJm5",
        "outputId": "3a2fb23e-3ca6-449e-a26d-980e9bf76b2c"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Take the 3 kaggle scores and creating a line plot to show improvement\n",
        "fig = pd.DataFrame(\n",
        "    {\n",
        "        \"test_eval\": [\"initial\", \"add_features\", \"hpo\"],\n",
        "        \"score\": [1.81151, 0.51204,0.54099]\n",
        "    }\n",
        ").plot(x=\"test_eval\", y=\"score\", figsize=(8, 6)).get_figure()\n",
        "fig.savefig('model_test_score.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhgEWYYMbJm5"
      },
      "source": [
        "### Hyperparameter table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "xu4bvUh0bJm6",
        "outputId": "785b5162-5046-4166-9ab1-07f1119ff9c3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-aed069b4-6044-464a-8f49-0a12eb1b5fe1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>model</th>\n",
              "      <th>hpo1</th>\n",
              "      <th>hpo2</th>\n",
              "      <th>hpo3</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>initial</td>\n",
              "      <td>Default</td>\n",
              "      <td>Default</td>\n",
              "      <td>Default</td>\n",
              "      <td>1.81151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>add_features</td>\n",
              "      <td>Default</td>\n",
              "      <td>Default</td>\n",
              "      <td>Default</td>\n",
              "      <td>0.51204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hpo</td>\n",
              "      <td>'NN': {'num_epochs': 10</td>\n",
              "      <td>'GBM': {'num_boost_round': 100, 'learning_rate...</td>\n",
              "      <td>'CAT': {'iterations': 50, 'learning_rate': 0.05}</td>\n",
              "      <td>0.54099</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aed069b4-6044-464a-8f49-0a12eb1b5fe1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-aed069b4-6044-464a-8f49-0a12eb1b5fe1 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-aed069b4-6044-464a-8f49-0a12eb1b5fe1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          model                     hpo1  \\\n",
              "0       initial                  Default   \n",
              "1  add_features                  Default   \n",
              "2           hpo  'NN': {'num_epochs': 10   \n",
              "\n",
              "                                                hpo2  \\\n",
              "0                                            Default   \n",
              "1                                            Default   \n",
              "2  'GBM': {'num_boost_round': 100, 'learning_rate...   \n",
              "\n",
              "                                               hpo3    score  \n",
              "0                                           Default  1.81151  \n",
              "1                                           Default  0.51204  \n",
              "2  'CAT': {'iterations': 50, 'learning_rate': 0.05}  0.54099  "
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The 3 hyperparameters we tuned with the kaggle score as the result\n",
        "pd.DataFrame({\n",
        "    \"model\": [\"initial\", \"add_features\", \"hpo\"],\n",
        "        \"hpo1\": [\"Default\", \"Default\", \"'NN': {'num_epochs': 10\"],\n",
        "    \"hpo2\": [\"Default\", \"Default\", \"'GBM': {'num_boost_round': 100, 'learning_rate': 0.01}\"],\n",
        "    \"hpo3\": [\"Default\", \"Default\", \"'CAT': {'iterations': 50, 'learning_rate': 0.05}\"],\n",
        "    \"score\": [1.81151, 0.51204,0.54099 ]\n",
        "\n",
        "})"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}