Nickson-Data-Science/Maji-ndogo-agriculture

# 🌾 Maji Ndogo Agriculture Analytics & Data Validation Pipeline An end-to-end data analytics and validation pipeline for agricultural production in Maji Ndogo, integrating field survey data with environmental measurements to ensure data quality and generate actionable insights. This project focuses on data ingestion, cleaning, validation, exploratory analysis, and statistical testing to support reliable downstream machine learning and yield modeling. --- ## 📌 Project Objectives The goal of this project is to: - ✅ Build reliable data pipelines from relational databases and CSV sources - ✅ Clean and standardize agricultural and weather datasets - 🧪 Validate data quality using automated testing - 📊 Compare environmental measurements across sources using hypothesis testing and Prepare trusted datasets for analytics and machine learning --- 📊 Key Features ✔ Modular data processing scripts ✔ Automated validation with pytest ✔ Statistical testing of environmental consistency ✔ Reproducible Python workflow ✔ Clean project structure for scalability --- ## 🛠️ Tech Stack - **Language:** Python 3.10+ - **Database:** SQLite - **Libraries:** - `pandas` — data processing - `numpy` — numerical computation - `pytest` — data validation tests - `scipy` — hypothesis testing - matplotlib / seaborn (for EDA where applicable) - **Version Control:** Git + GitHub --- ## 📂 Project Structure Maji-ndogo-agriculture/ │ ├── field_data_processor.py # Field survey cleaning & transformation ├── weather_data_processor.py # Weather station data processing ├── validate_data.py # Automated validation tests ├── Integrated_project.ipynb ├── requirements.txt └── README.md --- ## ⚡ Getting Started 1. **Clone the repository** ```bash git clone github.com cd Maji-ndogo-agriculture --- Create a virtual environment python -m venv venv source venv/bin/activate # macOS/Linux venv\Scripts\activate # …