This project addresses the critical public safety need for rapid water toxicity detection by developing a Machine Learning classifier to predict "Hazardous" water samples based on chemical profiles. The primary goal was to build a binary classification model that predicts hazards instantly using real-time chemical markers, preventing public exposure during the standard 24-48 hour biological testing window.
Directly addressing requirements to minimize False Negatives, our team transitioned from a linear Logistic Regression baseline to a non-linear XGBoost architecture. We overcame significant data challenges by transforming an OpenML dataset from a sparse "Long Format" of 1.26M rows into a structured wide-format of 51k samples. While the baseline model failed to detect 59% of hazards (Recall: 0.41), our final Optimized XGBoost model—tuned via Precision-Recall thresholding—increased Recall to 0.70. This improvement successfully balances hazard detection with operational viability, identifying Total Phosphorus, Enterococcus, and Total Suspended Solids as the primary scientific drivers of water toxicity.
- Architecture Design: Led the development of an XGBoost classifier to replace traditional 48-hour biological toxicity tests.
- Metric Optimization: Improved hazard detection recall from 0.41 to 0.70, directly increasing the system's ability to identify safety-critical water hazards.
- Data Engineering: Engineered a pipeline to transform a 1.26 million-row environmental dataset into 51,000 high-fidelity samples for predictive analysis.
- Feature Importance: Identified Total Phosphorus and Enterococcus as the primary scientific drivers of water toxicity through feature importance analysis.
We successfully raised the primary safety metric (Recall) by 70% relative to the baseline. The transition to XGBoost drastically reduced the "False Negative" count, directly addressing the core safety objective of minimizing missed hazards.
| Metric | Baseline (LogReg) | Weighted XGBoost | Final Optimized XGBoost |
|---|---|---|---|
| Recall (Safety) | 0.41 | 0.84 | 0.70 |
| Precision | 0.81 | 0.57 | 0.70 |
| F1-Score | 0.55 | 0.68 | 0.70 |
| False Negatives | 1,376 (High Risk) | 385 (Best Safety) | 700 (Balanced) |
Using XGBoost Feature Importance, we identified the top chemical drivers for hazard predictions, validating the model’s scientific accuracy:
- Total Phosphorus (0.2757): The strongest predictor, likely due to agricultural runoff fueling bacterial growth.
- Enterococcus (0.1300): A direct biological indicator strongly correlated with Fecal Coliform.
- Total Suspended Solids (0.1013): Indicates water clarity and particulate matter where bacteria attach.
- E. coli (0.0793): A direct measure of fecal contamination.
- Hour_cos (0.0729): Captured via cyclical time feature engineering to identify temporal toxicity patterns.