An eager and dedicated Computer Science engineering student specializing in Machine Learning, data-driven solutions, and Python-based web technologies. I focus on developing robust and efficient applications, from scalable ML models to intuitive user interfaces. I'm constantly seeking new challenges and expanding my knowledge in the evolving fields of AI and software engineering.
Developed using Hybrid techniques, implementing user-based and item-based filtering approaches. Processed datasets with over 1,000,000 book entries.
Python Machine Learning Collaborative FilteringIntegrated LLM API for concise text summaries and built a Flask-based web interface for text input and display. Reduced average reading time by 70% for lengthy documents and implemented caching to improve response times.
Python Flask LLM API NLP CachingDeveloped using Django with user authentication. Designed a responsive layout with blog functionality and implemented security measures including HTTPS.
Django Python HTML/CSS JavaScript User Authentication HTTPSDesigned and implemented a comprehensive database system for library operations, including a normalized relational database schema with 10+ tables (Books, Members, Loans, etc.).
SQL Database Systems SQLiteDesigned and implemented a comprehensive language detection system using NLTK and scikit-learn. Processed datasets with over 1,000,000 text entries.
Python NLTK scikit-learnDeveloped an AI-powered Text-to-SQL solution that translates natural language questions into accurate SQL queries. Fine-tuned a T5-based LLM on the comprehensive Spider dataset, enabling seamless database interaction for non-technical users and significantly enhancing data accessibility.
Python Hugging Face Transformers PyTorch Streamlit SQL NLPDeveloped a question-answering bot using LLMs, capable of answering questions based on a provided context. Implemented a Flask-based web interface for user interaction.
Python streamlit BERT Natural Language ProcessingDeveloped a news classification system using NLP techniques to categorize news articles into predefined topics. Achieved an accuracy of 85% on the test dataset.
Python NLTK scikit-learn BERT Natural Language Processing