{"id":20305323,"url":"https://github.com/jahnvisahni31/predict_bike_sharing_demand","last_synced_at":"2026-06-12T09:31:15.816Z","repository":{"id":236581822,"uuid":"792901722","full_name":"jahnvisahni31/Predict_bike_sharing_demand","owner":"jahnvisahni31","description":" This project leverages AutoGluon in AWS SageMaker Studio to predict bike sharing demand, automating model training and tuning for accurate forecasting.","archived":false,"fork":false,"pushed_at":"2024-05-18T17:30:05.000Z","size":800,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-14T11:23:08.189Z","etag":null,"topics":["autogluon","automl","aws","bikesharing","datascience","jupyternotebook","jupyternotebooks","machinelearning","predictiveanalytics","sagemaker"],"latest_commit_sha":null,"homepage":"https://colab.research.google.com/github/jahnvisahni31/predict_bike_sharing_with_autogluon/blob/main/predict_bike_sharing_with_autogluon.ipynb","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jahnvisahni31.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-27T21:49:20.000Z","updated_at":"2024-11-25T14:13:17.000Z","dependencies_parsed_at":"2024-04-27T22:32:33.496Z","dependency_job_id":"e3586bd1-2f97-4c60-9d4d-731549d35979","html_url":"https://github.com/jahnvisahni31/Predict_bike_sharing_demand","commit_stats":null,"previous_names":["jahnvisahni31/predict_bike_sharing_with_autogluon","jahnvisahni31/predict_bike_sharing_demand"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jahnvisahni31%2FPredict_bike_sharing_demand","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jahnvisahni31%2FPredict_bike_sharing_demand/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jahnvisahni31%2FPredict_bike_sharing_demand/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jahnvisahni31%2FPredict_bike_sharing_demand/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jahnvisahni31","download_url":"https://codeload.github.com/jahnvisahni31/Predict_bike_sharing_demand/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241801983,"owners_count":20022562,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["autogluon","automl","aws","bikesharing","datascience","jupyternotebook","jupyternotebooks","machinelearning","predictiveanalytics","sagemaker"],"created_at":"2024-11-14T17:08:07.686Z","updated_at":"2026-06-12T09:31:15.768Z","avatar_url":"https://github.com/jahnvisahni31.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Predict Bike Sharing with AutoGluon in AWS SageMaker Studio\n\nThis repository contains a Jupyter Notebook (`.ipynb` file) that demonstrates how to use AutoGluon to predict bike sharing demand in AWS SageMaker Studio. AutoGluon is a powerful AutoML toolkit that automates the process of training and tuning machine learning models.\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [links](#links)\n- [Installation](#installation)\n- [Dataset](#dataset)\n- [Usage](#usage)\n- [AutoGluon Workflow](#autogluon-workflow)\n- [Evaluation](#evaluation)\n- [Cleanup](#cleanup)\n- [References](#references)\n\n## Prerequisites\n\nBefore you begin, ensure you have the following:\n\n- An AWS account with access to SageMaker Studio.\n- SageMaker Studio set up in your AWS environment.\n- Basic knowledge of Jupyter Notebooks and Python.\n\n## Links\n[Open in Google Colab](https://colab.research.google.com/github/jahnvisahni31/predict_bike_sharing_with_autogluon/blob/main/predict_bike_sharing_with_autogluon.ipynb)\n\n\n## Installation\n\n1. **Clone the repository:**\n\n    ```bash\n    git clone https://github.com/jahnvisahni31/predict_bike_sharing_with_autogluon.git\n    cd predict_bike_sharing_with_autogluon\n    ```\n\n2. **Open SageMaker Studio:**\n\n    Launch SageMaker Studio from the AWS Management Console.\n\n3. **Upload the Notebook:**\n\n    Upload the `predict_bike_sharing_with_autogluon.ipynb` file to your SageMaker Studio environment.\n\n4. **Install Required Libraries:**\n\n    Open a terminal in SageMaker Studio and run the following command to install AutoGluon:\n\n    ```bash\n    pip install autogluon\n    ```\n\n## Dataset\n\nThe dataset used in this example is the [Bike Sharing Demand dataset](https://www.kaggle.com/c/bike-sharing-demand) from Kaggle. You can download the dataset and upload it to your SageMaker Studio environment.\n\n## Usage\n\n1. **Open the Notebook:**\n\n    Open the `predict_bike_sharing_with_autogluon.ipynb` file in SageMaker Studio.\n\n2. **Follow the Steps:**\n\n    Follow the steps in the notebook to:\n\n    - Load the dataset.\n    - Preprocess the data.\n    - Train the model using AutoGluon.\n    - Evaluate the model's performance.\n\n## AutoGluon Workflow\n\nThe notebook demonstrates the following AutoGluon workflow:\n\n1. **Import Libraries:**\n    ```python\n    from autogluon.tabular import TabularPredictor\n    ```\n\n2. **Load Dataset:**\n    ```python\n    import pandas as pd\n    train_data = pd.read_csv('train.csv')\n    test_data = pd.read_csv('test.csv')\n    ```\n\n3. **Train Model:**\n    ```python\n    predictor = TabularPredictor(label='count').fit(train_data)\n    ```\n\n4. **Evaluate Model:**\n    ```python\n    performance = predictor.evaluate(test_data)\n    print(performance)\n    ```\n\n## Evaluation\n\nThe notebook includes steps to evaluate the trained model on a test set, providing metrics such as RMSE (Root Mean Squared Error) to measure the model's performance.\n\n## Cleanup\n\nAfter completing the notebook, remember to clean up any resources to avoid unnecessary charges:\n\n- Delete any endpoints or instances created during the process.\n- Remove datasets and notebooks from your SageMaker Studio environment if no longer needed.\n\n## References\n\n- [AutoGluon Documentation](https://auto.gluon.ai/stable/index.html)\n- [AWS SageMaker Studio Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/studio.html)\n- [Bike Sharing Demand Dataset on Kaggle](https://www.kaggle.com/c/bike-sharing-demand)\n\n---\n\nThis README provides a high-level overview of using AutoGluon for bike sharing prediction in AWS SageMaker Studio. For detailed instructions and code, please refer to the included Jupyter Notebook.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjahnvisahni31%2Fpredict_bike_sharing_demand","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjahnvisahni31%2Fpredict_bike_sharing_demand","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjahnvisahni31%2Fpredict_bike_sharing_demand/lists"}