Diabetes Prediction Model

Machine Learning project to diagnose patients based on clinical metrics. Performed extensive Exploratory Data Analysis (EDA) and cleaning using Pandas/NumPy. Benchmarked algorithms including KNN and Logistic Regression. Achieved 98% accuracy using a Random Forest Classifier to distinguish between Non-Diabetic, Pre-Diabetic, and Diabetic patients.

Tech Stack

Key Features

• 98% accuracy with Random Forest Classifier
• Extensive EDA and data cleaning
• Benchmarking multiple algorithms (KNN, Logistic Regression)