Regression-based ML project forecasting daily bike rentals using weather, calendar, and temporal features. Implements data cleaning, feature engineering, and model tuning.
Predictive modeling on the classic Titanic dataset, focusing on feature extraction, imputation, and comparative analysis between classic ML algorithms (Logistic Regression, Random Forest).
View ProjectUtilizes Recurrent Neural Networks (RNNs) to generate creative TV script text based on show transcripts, demonstrating sequence modeling skills.
Applies Generative Adversarial Networks to synthesize realistic human faces from noise, showcasing knowledge of adversarial model architectures and training tricks.
Full-stack ML pipeline for training, deploying, and serving a sentiment analysis model as a web API. Includes REST endpoint creation and live model inference.