Hi! I'm a student with interests in Machine Learning, AI, and Databases. Currently working on my M.Tech. in CSE at IISc Bangalore, trying to learn something new every day. 🎓
I'm part of the Database Systems Lab (DSL) where I contribute to a project called UNMASQUE. It's an interesting challenge where we're developing ways to extract hidden SQL queries from database applications. UNMASQUE helps identify queries that are tucked away in stored procedures or encrypted functions - something like puzzle-solving for databases.
I've been exploring Machine Learning, Deep Learning, NLP, and Data Analysis along with database systems. Still learning the ropes, but enjoying the process of working with data and finding patterns.
When I need a break from coding, I watch football and cricket (probably more than I should!). Movies are another escape - I enjoy different genres and occasionally overthink plot details. This mix of technical work and casual hobbies helps me stay balanced.
Designed and implemented a robust recommendation system for e-commerce platforms, incorporating rating-based, content-based, and collaborative filtering methods. Developed a hybrid approach by blending techniques to deliver personalized product recommendations.
Built a sentiment analysis model using Naive Bayes, Bag of Words, and Word2Vec, achieving 85% accuracy. Preprocessed text data by tokenizing, removing stopwords, and encoding categorical variables. Explored semantic relationships using Word2Vec embeddings.
Improved SQL query performance by replacing REGEX with equivalent SQL LIKE patterns. Developed Rule-Based and LLM-Assisted Conversion System to map REGEX constructs to LIKE syntax. Implemented various prompting techniques to enhance conversion accuracy.
Developed a machine learning model to predict IPL match outcomes with 81% accuracy. Preprocessed and analyzed IPL datasets, including runs scored, balls left, wickets remaining, and run rates. Visualized match progression using Matplotlib.
Developed a deep learning model using Convolutional Neural Networks (CNN) to classify potato leaf diseases: Early Blight, Late Blight, and Healthy. Achieved 92% accuracy in disease classification, enabling farmers to take timely action to prevent crop loss.
Indian Institute of Science, Bangalore
CGPA: 7.8
LNCT, Bhopal
CGPA: 8.26
CBSE
Percentage: 90.6%
CBSE
Percentage: 90.8%
pabhishek@iisc.ac.in
+91 6262442513
Bangalore, India