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Decoding the Indian IPO Market

Every IPO cycle in India comes with the same familiar excitement. A company opens for subscription, WhatsApp groups start discussing GMP, retail investors rush to apply through every Demat account available, and the biggest question becomes: will this list at a premium?

This project began with that very practical question, but I wanted to answer it with data rather than anecdotes:

Is investing in Indian IPOs actually profitable, and which pre-listing signals best predict IPO success?

The result is a quantitative study of Indian IPOs across mainboard, SME, and GMP-era datasets, covering listing-day gains, oversubscription, grey market premium, market phase, holding periods, long-run performance, and machine learning based screening. The analysis uses web-scraped IPO data, API-collected GMP data, Yahoo Finance enrichment, and statistical tests designed for heavily skewed financial data.

Enhanced Research Assistant: AI-Powered Tool for Efficient Research

This blog post explores my 2025Q1 Kaggle Gen AI Intensive Course Capstone project, where I built an AI-powered research assistant to automate web searches, content extraction, summarization, and report generation. The assistant uses Retrieval Augmented Generation, embeddings for semantic search, and document understanding to deliver polished, relevant reports in minutes, tackling information overload for students and professionals. Future enhancements may include source validation and multi-modal support.