reregin/water-quality-prediction

A machine learning project to predict water quality parameters across river locations in South Africa, with emphasis on identifying key factors influencing water quality variation. # 🌊 Water Quality Prediction A machine learning project to predict water quality parameters across river locations in South Africa, with emphasis on identifying key factors influencing water quality variation. **Project Objective:** Develop a robust ML model capable of predicting water quality parameters (total alkalinity, electrical conductance, dissolved reactive phosphorus) and identify the key environmental and geographic factors that significantly influence these measurements. --- ## 📂 Project Structure This project follows a strict separation of concerns. ``` ├── data/ │ ├── raw/ # Original water quality dataset (2011-2015, ~200 locations) │ ├── processed/ # Cleaned & feature-engineered data │ └── external/ # Geographic/environmental reference data │ ├── notebooks/ # Experimental Laboratory │ ├── 00_data_collection.ipynb # Data loading & exploration │ ├── 01_eda_and_discovery.ipynb # Discovery & Analysis (Split-First: Train Only) │ ├── 02_preprocessing.ipynb # Feature Engineering & Transformation │ ├── 03_model_training.ipynb # Model Training with MLflow Tracking │ └── 04_inference_test.ipynb # Validation & Feature Importance Analysis │ ├── src/ # Production Codebase │ ├── config.py # Global Control Center (Paths, Params) │ ├── data_loader.py # Robust Data Ingestion & Splitting │ ├── preprocessing.py # Reusable Cleaning & Feature Engineering Logic │ ├── train.py # Model Training Pipeline │ ├── inference.py # Prediction Engine │ └── utils.py # Helper Functions │ ├── models/ # Serialized Models (.pkl, .pth) ├── app/ │ └── main.py # User Interface (Streamlit/FastAPI) ├── mlflow.db # MLflow Experiment Tracking Database └── requirements.txt # Dependencies ``` --- …