A showcase of my work spanning full-stack development, machine learning, and enterprise solutions. Each project demonstrates my commitment to building scalable, impactful applications.
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Production-grade financial analytics platform providing ticker search, price performance tracking, fundamental analysis, and news aggregation. Demonstrates proficiency in financial data processing, API integration, and building decision-support tools—valuable for data science roles in fintech and quantitative analysis.
End-to-end deep learning solution leveraging MobileNetV2 transfer learning to classify 120 dog breeds with optimized accuracy. Demonstrates proficiency in computer vision, data preprocessing, and model architecture selection—key skills for ML production environments.
Enterprise-level collaborative analytics application enabling teams to share datasets, generate insights, and visualize findings collectively. Showcases understanding of multi-user systems, data governance, and collaborative workflows—critical for data science teams in production environments.
Comprehensive supervised learning pipeline for predicting heart disease risk using patient medical records. Features rigorous data exploration, model selection across multiple algorithms, hyperparameter tuning, and cross-validation—demonstrating systematic ML workflow and healthcare domain understanding.
Full-stack data science project predicting bulldozer auction prices using real-world commodity data. Covers complete pipeline: feature engineering, handling temporal data, model ensembling with XGBoost, and RMSLE optimization. Demonstrates end-to-end problem-solving from raw data to production predictions.
AI-driven recommendation system applying NLP and collaborative filtering to anime datasets. Demonstrates understanding of recommendation systems, text processing, and user preference modeling—critical for personalization in production ML systems.
Interactive Streamlit application generating daily meal plans based on nutritional goals and dietary constraints. Showcases full-stack ML application development: requirement gathering, algorithm design, and user-centric UI/UX implementation.
Smart workout recommendation engine tailored to user fitness levels, goals, and equipment availability. Demonstrates machine learning application in domain-specific personalization and real-time system adaptability.
Production-grade conversational AI application leveraging LLMs for natural dialogue. Demonstrates prompt engineering, context management, and real-time inference optimization—essential for modern ML applications in NLP.
Comprehensive business analytics dashboard analyzing HR metrics, employee performance, and organizational trends. Demonstrates data visualization expertise, business intelligence fundamentals, and ability to translate data insights into actionable business metrics.
LLM-powered content generation system producing well-structured essays with customizable prompts and export functionality. Demonstrates prompt tuning, output formatting, and practical application of language models for content generation.
Comprehensive NLP project benchmarking multiple models for disaster-related tweet classification. Tests diverse architectures (LSTM, BERT, TF-IDF + ML) and demonstrates strong experimental rigor, model evaluation methodology, and understanding of NLP fundamentals.
End-to-end ML solution deployed on AWS SageMaker for automated inventory bin counting from images. Demonstrates cloud ML platforms expertise, model deployment at scale, and MLOps fundamentals—critical for enterprise data science roles.
Complete Udacity nanodegree covering AWS ML services, MLOps, and production ML pipelines. Includes projects on SageMaker, model deployment, monitoring, and hyperparameter optimization—essential competencies for ML engineering roles.
Enterprise-grade machine learning project implementing complete MLOps lifecycle: data pipelines, model versioning, monitoring, and continuous retraining. Predicts customer churn with focus on production reliability, scalability, and reproducibility—directly aligning with data science role requirements.