Predicting Daily Bike Rentals Using Linear Regression and Decision Forests: A Comparative Analysis of Model Performance

This increasing need for bike rentals has raised the importance of proper demand forecasting for better allocation of resources, improved customer satisfaction, and increased operational efficiencies. The purpose of this research is to predict the number of daily bike rental rentals based on historical counts collected by a open source data of bike rental company. It contains various attributes, including time intervals (year and day of the week), weather (temperature, humidity, wind speed), and seasonal patterns. The aim is to compare the performance of linear regression and decision forest algorithms to detect these trends and produce accurate predictions. We used a process of data preprocessing to resolve missing values, outliers and feature multicollinearity, as well as feature engineering to ensure model accuracy. They compare the effectiveness of linear regression, which is straightforward and easy to understand, with decision forests, a powerful ensemble that can account for non-linear relations and multi-feature interactions. Model evaluation was performed using standard performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and R-squared values to get a clear view of each algorithm's strengths and weaknesses. Results indicate that while linear regression helps us to understand the linear dependence between features, decision forests are better at detecting complex non-linear patterns. This comparison highlights the balancing act between model readability and predictive power and gives practical insights to data scientists and business analysts working on bike rental. By presenting examples of advanced machine learning methods being used, the research highlights their capabilities to inform data-based decision making and enhance service delivery in fast-paced and competitive service environments.