Bike Rental Demand Prediction Using Machine Learning

In this project, I built a machine learning model to predict the number of rented bicycles based on various features such as temperature, weather conditions, day of the week, and season.

The model was trained using a public dataset and evaluated using metrics like Mean Absolute Error (MAE). This project showcases my skills in data preprocessing, feature engineering, model training, and evaluation using Python and libraries like scikit-learn and pandas.

The goal was to demonstrate how machine learning can be used in real-life applications to support decision-making and resource planning.