Kavodax seeks to enhance its investment strategy by developing a machine learning-based recommendation system for stocks and bonds. The project aims to integrate a factor model to optimize portfolio allocation, allowing for more informed investment decisions. Learners will apply their knowledge of machine learning algorithms and financial modeling to create a system that analyzes historical market data and identifies key factors influencing asset performance. The project will involve data preprocessing, model training, and validation to ensure accuracy and reliability. By the end of the project, learners will have gained practical experience in applying machine learning techniques to real-world financial problems, enhancing their understanding of both data science and finance.

Key tasks include:

- Collecting and preprocessing historical stock and bond data.

- Developing a machine learning model to generate investment recommendations.

- Implementing a factor model for portfolio optimization.

- Testing and validating the system's performance against historical data.

The project deliverables will include a fully functional machine learning-based recommendation system and a comprehensive report detailing the methodology, model performance, and investment insights. Additionally, learners will provide a presentation summarizing their findings and recommendations for potential improvements.

Deliverables include:

- A working prototype of the recommendation system.

- A detailed report on the model's development and performance.

- A presentation of key findings and future recommendations.