Predicting Daily Bike Rentals Using Linear Regression and Decision Forests: A Comparative Analysis of Model Performance
Bike rentals, predictive modelling, linear regression, decision forests, seasonal trends, machine learning, demand forecasting, operational efficiency, ensemble methods, feature engineering
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.
"Predicting Daily Bike Rentals Using Linear Regression and Decision Forests: A Comparative Analysis of Model Performance", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 1, page no.a155-a162, January-2025, Available :https://ijsdr.org/papers/IJSDR2501015.pdf
Volume 10
Issue 1,
January-2025
Pages : a155-a162
Paper Reg. ID: IJSDR_300151
Published Paper Id: IJSDR2501015
Downloads: 000347351
Research Area: Science and Technology
Country: Chennai, Tamil Nadu, India
DOI: https://doi.org/10.5281/zenodo.14637542
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
