This project focuses on predicting product quality based on physicochemical properties using supervised machine learning. Utilizing a Random Forest Classifier, the model analyzes chemical balances to classify whether a product meets high-quality industry standards.
- Language: Python
- Algorithm: Random Forest Classifier
- Libraries: Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn
- Feature Importance: Isolated the key physicochemical variables that exert the highest statistical impact on the final quality rating.
- Ensemble Learning: Leveraged Random Forest to handle non-linear relationships and avoid overfitting, resulting in highly stable predictions.
- Data Preprocessing: Handled features with wide variations using proper scaling techniques to optimize classifier boundaries.
💡 Domain Transferability (Automotive Application): This precise quality classification framework is highly applicable to Automotive Manufacturing & Fluid Analytics. The same Random Forest logic is widely used to analyze the chemical properties of engine oils, coolant degradation, and evaluating the chemical health/lifespan of Lithium-Ion Electric Vehicle (EV) battery cells.