This project, undertaken as part of an Upgrad course assignment, focuses on predicting bike sharing demand using a multiple linear regression model. Leveraging the Bike Sharing dataset provided by Upgrad, the goal was to develop a robust model capable of accurately forecasting the demand for shared bikes.
explores the application of various statistical and machine learning techniques within the Python ecosystem. Key libraries employed include pandas for data manipulation, seaborn and matplotlib for data visualization, scikit-learn (sklearn) for model building and evaluation, and statsmodels for statistical modeling and analysis.
