This project is focused on forecasting the prices of bikes using a variety of features such as country of origin, aesthetics, engine displacement, power, torque, and more. The project encompasses data cleaning, thorough analysis, and machine learning modeling to achieve accurate predictions. Additionally, it features an Android interface, allowing users to input specific details about bikes. This holistic approach combines data science and user interaction, providing a comprehensive tool for predicting bike prices.