Bike-Sharing Rental Prediction with Regression🚲🚲
This repository predicts hourly bike rental demand by combining historical usage patterns with weather data.
Project Goal:
The goal is to develop a regression model that accurately forecasts the number of bikes rented for a given hour.
This information can be valuable for bike-sharing companies to:
Optimize bike distribution across stations Manage staffing levels Improve user experience by ensuring bike availability Data
🚲This project utilizes two datasets:
🚲Historical Bike Rental Data: This data likely includes information like:
Timestamp (hourly) Number of rentals User type (registered/casual) Day of week Season
🚲Weather Data: This data might contain:
Temperature Feels-like temperature Humidity Wind speed Weather condition (categorical)
Note: The specific data format and variables might differ depending on the source.
🚲Methodology
This project employs regression analysis to build a model that maps historical usage patterns and weather data to hourly bike rental demand. Here's a potential workflow:
Data Loading and Cleaning: Load both datasets, handle missing values, and address any inconsistencies. Feature Engineering: Create new features if necessary (e.g., combining temperature and wind speed into a "wind chill" factor). Model Building: Train a regression model (e.g., Linear Regression, Random Forest) on the combined dataset. Model Evaluation: Evaluate the model's performance using metrics like Mean Squared Error (MSE) or R-squared on a separate test set. Model Tuning (Optional): Fine-tune the model's hyperparameters to improve prediction accuracy. Running the Code This section should detail the steps to set up the project and run the code. It might involve:
Installing required libraries (e.g., pandas, scikit-learn) Specifying the data location Running scripts for data pre-processing, model training, and evaluation Note: Specific instructions will depend on the chosen implementation.
Further Exploration Model Selection: Compare the performance of different regression models. Feature Importance: Analyze which features contribute most to the model's predictions. Seasonality: Account for seasonal variations in bike rental demand. Real-Time Integration: Integrate the model into a system that consumes real-time weather data for continuous predictions. Additional Information The readme can include:
Authors and contributors License information References to relevant datasets or weather data sources This readme provides a starting point for your bike-sharing rental prediction project using regression. Feel free to adapt it based on your specific data and chosen approach.