Featured Projects
“Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard.
Fetal Health ML Analysis
Machine learning-based fetal health classification system using cardiotocography data. Compares multiple algorithms (XGBoost, Random Forest, MLP, Logistic Regression) for predicting fetal health status with 95.9% accuracy.
Tech: Python, Scikit-learn, Pandas, etc.
Brain Tumor VGG16 Transfer Learning
Deep learning brain tumor classification using fine-tuned VGG16 with transfer learning. Achieves 89% accuracy in classifying MRI scans into 4 categories: glioma, meningioma, pituitary tumor, and no tumor.
Tech: TensorFlow, PIL, NumPy, etc.
Python Refresher with Simulated Healthcare Data
A Python fundamentals refresher using simulated healthcare datasets to demonstrate core programming concepts, data analysis, and visualization techniques.
Tech: Pandas, Matplotlib, Seaborn, etc.
CardioPredict Deep Learning SHAP
A comprehensive machine learning pipeline for cardiovascular disease prediction using deep neural networks with explainable AI capabilities.
Tech: TensorFlow, Scikit-learn, SHAP, etc.
Drowsiness Detection System
A computer vision-based system that detects drowsiness in real-time using eye aspect ratio (EAR) analysis. Features live webcam monitoring, audio alerts, data logging, and comprehensive analytics for safety applications.
Tech: OpenCV, Dlib, SciPy, etc.
Premier-League-Predictor
Machine learning models to predict Premier League match outcomes using historical data and team form analysis. Features Random Forest, XGBoost, Gradient Boosting, and Logistic Regression classifiers with interactive visualizations.
Tech: Scikit-learn, Bokeh, SciPy, etc.