🌊 RippleWorks – AquaPredict Pro

An interactive Water Quality Intelligence System built with machine learning and Streamlit.
This project predicts Water Quality Index (WQI) (numeric score) and Water Quality Classification (WQC) (Excellent / Good / Medium / Poor / Very Poor) based on water parameters.

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Features

• End-to-end pipeline: cleaning → feature engineering → modeling → deployment
• Handles missing values (KNN/median strategies) and outliers (IQR capping + domain thresholds)
• Computes WQI dynamically and categorizes into classes (WQC)
• Supports both regression (WQI prediction) and classification (WQC prediction)
• Deployed as a sleek Streamlit web app

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Tech Stack

• Python (Pandas, NumPy, Scikit-learn, XGBoost)
• Streamlit for web UI
• Joblib for model persistence
• GitHub + Streamlit Cloud for deployment

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Dataset

Source: Kaggle – Indian River Water Quality dataset

• 1991 rows, 8 key water quality parameters
• Target variables engineered: WQI and WQC

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