A robust, modular, and production-ready platform for solar power system data analysis, machine learning, and prediction. Built with ZenML, Streamlit, MLflow, and a rich Python data science stack, this project enables end-to-end workflows from data ingestion and EDA to model training, deployment, inference and experiment tracking

A robust, modular machine learning pipeline for predicting solar panel efficiency, featuring domain-specific preprocessing, advanced feature engineering, model training, evaluation, selection, and easy batch prediction from CSV files.