A professional portfolio of seven completed projects across exploratory data analysis, predictive modeling, deep learning, computer vision, generative AI, RAG, and model delivery.
Each case study explains the business problem, project scope, workflow, analysis focus, conclusions, repository assets, and a direct path to discuss similar work.
Built a retrieval-augmented assistant that grounds LLM responses in trusted medical reference materials using document processing, embeddings, semantic search, vector storage, and controlled prompting.
Developed an end-to-end retail sales forecasting workflow spanning feature preparation, regression modeling, tuning, API packaging, containerization, and interactive delivery.
Created a safety-helmet image classifier using CNNs, transfer learning, fine-tuning, and data augmentation to support automated workplace compliance monitoring.
Designed neural-network classifiers for wind-turbine generator failure prediction with class-imbalance handling and recall-focused evaluation for proactive maintenance.
Built an ensemble-learning solution to predict visa application outcomes using bagging, boosting, stacking, hyperparameter tuning, and feature-importance analysis.
Developed a customer-propensity model for personal-loan targeting using exploratory analysis, feature preparation, classification models, and business-oriented evaluation.
Performed end-to-end exploratory analysis of food-delivery orders to uncover customer behavior, restaurant performance, cuisine demand, ratings, delivery patterns, and revenue opportunities.
Data quality assessment, descriptive analysis, visualization, segmentation, operational patterns, and business recommendations.
Classification, regression, ensembles, model tuning, feature engineering, evaluation, and decision support.
Neural networks, CNNs, transfer learning, image augmentation, fine-tuning, and risk-aware evaluation.
Document preparation, embeddings, vector retrieval, grounded prompting, LLM response generation, and evaluation.
Reproducible notebooks, HTML reports, APIs, Streamlit, Docker, and portfolio-ready technical documentation.
Clear problem framing, metric selection, conclusion writing, visual storytelling, and actionable recommendations.
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