Seoul bike demand prediction artifacts

This project aims to predict bike rental demand using machine learning, specifically focusing on hourly predictions based on various environmental and temporal features. The dataset used for this analysis is the publicly available "Seoul Bike Sharing Demand" dataset, which includes factors like temperature, humidity, wind speed, and historical rental counts.

Key elements of the project:

1. Model: A trained XGBoost regression model that predicts bike rental counts for each hour, given the relevant environmental and temporal features. This model is built to optimize fleet distribution for bike-sharing companies, helping them efficiently manage resources and reduce operational costs.

2. Visualization: A plot that visualizes the comparison between the ground truth (actual bike rentals) and the predictions made by the XGBoost model. The plot provides insights into how well the model captures patterns in bike rental demand and the accuracy of its forecasts.

3. Predictions (CSV): A CSV file containing the model's predictions for the test set. The CSV includes the predicted bike rental counts, along with relevant features such as date, hour, temperature, and humidity. This dataset is intended for evaluating the performance of the trained model and for further analysis.

4. CodeMeta: A metadata file that provides essential information about the project's code, ensuring it adheres to best practices for reproducibility and transparency in computational research.

5. FAIR4ML: The project follows the FAIR4ML principles to ensure that the machine learning models, datasets, and results are Findable, Accessible, Interoperable, and Reproducible. All code, models, and results are made publicly available for further research and re-use.