FindGrant is seeking to enhance its platform by integrating a Smart Recommendation Engine that can suggest relevant grants to users based on their profiles and past grants success data. The goal is to improve user experience by providing personalized grant suggestions, thereby increasing user engagement and satisfaction. This project involves developing an algorithm that analyzes user data, such as interests, previous grant applications, and success rates, to generate tailored recommendations. The engine should be capable of learning and adapting over time to improve its accuracy. Learners will apply their knowledge of data analysis, machine learning, and software development to create a prototype of this recommendation system. The project will focus on creating a scalable and efficient solution that can be integrated into the existing FindGrant platform.

- Analyze user data to identify key factors for grant recommendations.

- Develop a machine learning model to predict relevant grants for users.

- Ensure the recommendation engine is scalable and efficient.

- Test and validate the engine's accuracy and adaptability.

The project deliverables will include a functional prototype of the Smart Recommendation Engine, complete with documentation and a demonstration of its capabilities. The team will provide a detailed report outlining the algorithm's design, data sources used, and the testing process. Additionally, a presentation will be prepared to showcase the engine's features and potential impact on user engagement.

- Functional prototype of the recommendation engine.

- Comprehensive documentation and user guide.

- Detailed report on algorithm design and testing.

- Presentation demonstrating the engine's capabilities.