Built a Logistic Regression model in R to predict housing loan approvals using financial and demographic data. Performed data preprocessing, feature engineering, and model evaluation using ROC curve and confusion matrix. Achieved ~74% accuracy, showcasing the effectiveness of ML in loan decision-making.

Machine Learning projects from undergrad.

Applied Machine Learning projects includes: a supervised machine learning model to classify emails from the given dataset as spam and not-spam. 2.

An AI-powered system using CNN and Grad-CAM to detect and visualize brain tumors from MRI scans, offering accurate predictions and interpretable medical imaging insights.

Implementation of LDA and QDA classifiers using the Iris dataset. Includes data normalization, training, testing, and accuracy measurement.