About
Highly motivated and results-driven B.Tech student specializing in Artificial Intelligence and Machine Learning, with practical experience in NLP, deep learning, and full-stack web development. Proven ability to apply theoretical knowledge to real-world projects, including sentiment analysis, medical data classification, and web application development. Eager to contribute to innovative solutions in AI/ML and software engineering.
Work
Summary
Currently developing a comprehensive hospital website and dashboard solution.
Highlights
Building a demo hospital website and dashboard, gaining hands-on experience in end-to-end web application development.
Utilizing ASP.NET for backend logic, MySQL and MongoDB for robust data management, and JavaScript, HTML, and CSS for dynamic and responsive user interfaces.
Summary
Focused on Natural Language Processing (NLP) and advanced ensemble machine learning methods.
Highlights
Developed an intent and sentiment analysis system for over 50,000 YouTube comments, employing text classification techniques.
Gained practical experience in various ensemble methods, including bagging, boosting, stacking, and random forests, to enhance model performance.
Summary
Received intensive training and practical exposure to foundational Python libraries for data science and machine learning.
Highlights
Trained extensively on key Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn for data manipulation, visualization, and machine learning.
Applied web scraping and Exploratory Data Analysis (EDA) techniques to acquire and prepare data for analytical models.
Education
Publications
Languages
English
Fluent
Bengali
Native
Hindi
Proficient
Skills
Programming Languages
Python, JavaScript, SQL, HTML, CSS.
Databases
MongoDB, MySQL.
Machine Learning Libraries
Scikit-learn, NumPy, Pandas, Matplotlib, Seaborn.
Data Visualization & BI
Power BI, Matplotlib, Seaborn.
Domains & Methodologies
Machine Learning, Natural Language Processing (NLP), Exploratory Data Analysis (EDA), Web Scraping, Full Stack Development (.NET), Text Classification, Ensemble Methods (Bagging, Boosting, Stacking, Random Forests).