Project Description

As part of my final project for a Machine Learning course, I analyzed a dataset of water quality indicators to predict potability. I explored various models, including Random Forest Classifier (RFC), Support Vector Machine (SVM), Decision Tree, and LightGBM, to determine the best-performing algorithm. The project involved handling missing values, feature engineering, and model evaluation using key metrics such as accuracy, precision, and recall.