Teal Climate is seeking to enhance its database update systems to achieve global coverage. The current databases contain over a hundred thousand rows compiled from various online sources, focusing on limited geographic areas. The goal of this project is to develop a system that can extrapolate these limited geographic databases to achieve comprehensive global coverage. This will involve utilizing relevant data and machine learning models to predict and fill in gaps in the existing data. The project will provide learners with the opportunity to apply their knowledge of database management, data analysis, and machine learning. The tasks will include analyzing the current database structure, identifying gaps, and developing algorithms to extrapolate data for global coverage.

- Analyze the current database structure and identify geographic data gaps.

- Develop machine learning models to predict and fill in missing data.

- Test and validate the accuracy of the extrapolated data.

- Document the process and results for future reference.

The project deliverables will include a comprehensive report and a functional system for database extrapolation. The report should detail the analysis of the current database, the methodology used for data extrapolation, and the results of the testing phase. Additionally, the team will deliver a working prototype of the system that can be integrated into Teal Climate's existing database infrastructure.

- A detailed report on the database analysis and extrapolation methodology.

- A functional prototype of the database extrapolation system.

- Documentation of the testing and validation process.

- Recommendations for future improvements and scalability.