In this repository, we explore how linear regression, a fundamental machine learning algorithm, can accurately forecast the number of bike rentals based on various factors.
We analyze historical bike sharing data, including attributes like time, weather conditions, and holidays, to build a predictive model. By employing linear regression, we aim to capture the relationships between these variables and the bike rental demand, enabling us to make precise predictions.