This project focuses on detecting Chronic Kidney Disease (CKD) using supervised machine learning techniques. The dataset, obtained from Kaggle, contains clinical and laboratory features of patients. Data preprocessing steps such as handling missing values, encoding categorical variables, and feature scaling were performed to prepare the dataset for modelling.
Two classification algorithms, Logistic Regression and Random Forest, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed that Random Forest achieved superior performance, making it the preferred model for CKD prediction. This study demonstrates the effectiveness of machine learning in supporting early disease detection and improving healthcare decision-making.