Maryline-ui/maji_ndogo_data_validation

Maji Ndogo Data Validation This project validates and integrates agricultural, weather, and soil data from Maji Ndogo Farm Survey using SQL and Python. The analysis ensures data accuracy and consistency across multiple features, supporting insights into crop yield, farm management, and environmental conditions. Project Overview The goal is to validate, clean, and verify data relationships within the Maji Ndogo dataset to prepare it for accurate analysis. The project connects to a SQLite database and explores: Geographic and environmental features Soil and crop characteristics Weather patterns Farm management practices Tools & Technologies Python (Pandas, SQLAlchemy) SQLite for relational data management Jupyter Notebook for data exploration and validation SQL Queries for data integration and consistency checks Key Tasks Connected and queried multiple database tables using SQLAlchemy Validated data integrity and relationships between key entities Renamed, merged, and standardized features for clarity Ensured clean, analysis-ready datasets Outcome The final validated dataset provides a reliable foundation for further agricultural productivity analysis, climate impact studies, and data-driven decision-making in farm management. Author Maryline Abongo Portfolio | LinkedIn | GitHub