Alzheimer Detection

Developed a machine learning model to predict Alzheimer’s disease progression using tabular clinical data from the OASIS dataset. This project demonstrates strong capabilities in data preprocessing, exploratory data analysis, and predictive modeling for healthcare applications. Performed comprehensive EDA to explore demographic, cognitive, and MRI-derived features, handling missing values and outliers to ensure data quality. Implemented a Random Forest classifier for robust prediction of Alzheimer’s presence and severity, leveraging its interpretability and ability to handle complex, heterogeneous features. Key Highlights: Data Analysis: Conducted deep analysis on patient demographics, MRI measures, and cognitive scores to uncover meaningful patterns. Model Implementation: Trained and validated a Random Forest model for disease classification and feature importance ranking. Complex Data Handling: Managed and processed multi-type tabular data (categorical, numerical, and clinical variables) effectively. Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn. Impact: This project reinforces expertise in biomedical data analysis, machine learning for health diagnostics, and the ability to extract actionable insights from complex real-world datasets, reflecting strong analytical and implementation skills.