Welcome to the "Bike-Sharing-Algorithm" repository! This project utilizes GBM (Gradient Boosting Model) to predict bike demand and explores customer trends with features like day of the week, holiday, weather conditions, hour of the day, season, etc. Analyze registered, casual, and total customers to gain insights. The repository includes various files, such as project report, R script, Jupyter notebooks, and dataset files.
"Bike-Sharing-Algorithm" leverages a Gradient Boosting Model (GBM) to predict bike demand and explores customer behavior trends. The project provides a comprehensive analysis of registered, casual, and total customers based on various factors, offering valuable insights for optimizing bike-sharing operations.
- Bike Sharing Algorithm_ProjectReport.pdf: Project report detailing methodologies, findings, and conclusions.
- Bike_Sharing_Algorithm.R: R script for implementing the bike-sharing algorithm.
- GBM_BikeSharingDemand.ipynb: Jupyter notebook focusing on the GBM model for bike demand prediction.
- RandomForest_BikeSharingAlgorithm.ipynb: Jupyter notebook exploring a Random Forest model for bike-sharing predictions.
- train.csv: Dataset containing training data.
- test.csv: Dataset containing test data.
# Example installation steps
git clone https://github.com/Praveen76/Bike-Sharing-Algorithm.git
cd Bike-Sharing-AlgorithmIf you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.
Please adhere to our Code of Conduct in all your interactions with the project.
This project is licensed under the MIT License.
For questions or inquiries, feel free to contact me on Linkedin.
I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.