Local Reach aims to enhance viewer experience and optimize advertising strategies by developing an algorithm capable of real-time classification of TV content and commercial breaks. The project involves creating a machine learning model that can accurately distinguish between TV shows, movies, news segments, and commercial advertisements as they are broadcasted. This will help in dynamically adjusting content delivery and ad placements, ensuring that viewers receive a seamless experience while advertisers can target their audience more effectively. The project will require the team to gather and preprocess relevant data, train and validate the model, and integrate the algorithm into a real-time processing system.

Key tasks include:

- Data collection and preprocessing from various TV channels.

- Development and training of a machine learning model for classification.

- Validation and testing of the model's accuracy and performance.

- Integration of the algorithm into a real-time processing framework.

The project deliverables will include a fully functional algorithm capable of real-time classification of TV content and commercial breaks. The team will provide a detailed report documenting the development process, data sources, model architecture, and performance metrics. Additionally, a demonstration of the algorithm's real-time classification capabilities will be presented.

Deliverables include:

- A trained and validated machine learning model.

- A comprehensive project report.

- A real-time processing system integrated with the algorithm.

- A live demonstration of the algorithm in action.