I’m a Data Scientist and Machine Learning Engineer with an M.S. in Computer Science specialized in Data Science. I have successfully led cross-functional teams and completed projects within 2 months, demonstrating expertise in performing ETL, generating Tableau visualizations, and reducing campaign costs by 5%. I have contributed to open-source machine learning repositories, implemented B-I-O tagging for 141,000 tokens, and developed a 5-feature set with a CRF sequence tagging model achieving 91% document-level accuracy. My experience also includes identifying fake news using SVM models with 88.92% accuracy.

My expertise spans designing and implementing end-to-end machine learning pipelines, including data ingestion, exploratory data analysis (EDA), data preprocessing, model training, and deployment on platforms like Flask. I am passionate about solving complex problems using advanced machine learning techniques, hyperparameter tuning, and creating production-ready systems with custom logging and exception handling.

• Goal: Predict income levels using the Adult Income Census dataset.
• Highlights: End-to-end ML pipeline with data ingestion, preprocessing, model training (Random Forest, Decision Tree, Logistic Regression), and deployment on Flask.
• Technologies: Anaconda, Python, Scikit-learn, Jupyter, Pandas, Numpy, Flask.
• Results: Achieved 85% accuracy using hyperparameter-tuned models. Deployed the model on Flask with real-time predictions under 200ms.