Smartwatches have become increasingly popular in recent years due to their ability to provide a range of functionalities beyond traditional timekeeping, such as fitness tracking, communication, and entertainment. As with any electronic device, the cost of a smartwatch can vary significantly depending on its features, brand, and other factors.

In this project, we aim to develop a machine learning model that predicts the price of a smartwatch based on a given set of features. Our dataset contains information on various attributes of smartwatches, including brand, model, operating system, connectivity, display type and size, resolution, water resistance, battery life, heart rate monitor, GPS, NFC, and price. We will use this dataset to train and evaluate our predictive model, which has the potential to transform the way consumers and manufacturers approach pricing and purchasing decisions.

The development of an accurate smartwatch price prediction model using machine learning can have significant implications in various domains, including e-commerce, marketing, and consumer research.

Technical Architechture