This project leverages machine learning to predict the number of bike rentals in a bike-sharing program. The dataset contains data from a bike-sharing company, capturing rental counts over a period of time, along with various features like weather conditions, time of day, and other factors that influence the rental demand.
- Modeling Count Data: Implement Poisson Regression and Random Forest Regression to model the count of bike rentals based on predictors.
- Comparing Models: Compare Poisson regression with Random Forest Regression to assess performance on predicting bike rentals.
- Data Analysis: Explore how features such as temperature, weather, and time of day affect bike rental frequency.