Overview: A powerful interactive dashboard for analyzing stock market trends, sector performance, and forecasting future stock movements using historical data and time series analysis.

Description: This dashboard provides a comprehensive view of stock performance, allowing users to explore market trends, evaluate sector dynamics, and make data-driven decisions. Key features include detailed visualizations of stock prices, sector performance, and time series forecasting to predict future trends. Built with Power BI for interactive visuals and Python for advanced data analysis, this tool empowers investors and professionals to track and analyze market data efficiently.

Technologies Used: Power BI, Python

Overview: A modular machine learning framework designed to predict stock prices and evaluate trading strategies using realistic backtesting and live simulation environments.

Description: This project provides an end-to-end solution for financial forecasting and trading strategy evaluation. Built with advanced ML algorithms, robust experiment tracking, and Streamlit integration, users can perform data-driven analysis, backtests, and model deployments efficiently.

Technologies Used: Python, Scikit-learn, MLflow, Streamlit

Overview: Scout is an AI-powered competitive intelligence platform that automates market monitoring for product teams and strategy leaders. It scrapes competitor sources, uses LLM-based classification to extract strategic events, and delivers actionable intelligence briefings.

Description: Scout transforms hours of manual competitor research into 60-second insights. The platform monitors SaaS competitors across Analytics, Design Tools, and Project Management verticals, automatically detecting feature launches, pricing changes, and strategic partnerships. Key features include:

• Automated Data Collection: Multi-source scraping from blogs, changelogs, and RSS feeds
• AI-Powered Classification: GPT-4-mini with structured outputs for event extraction
• Interactive Dashboard: Real-time metrics, category breakdowns, and confidence scoring
• Intelligence Briefings: One-click HTML export with visualizations and source links
• Competitive Sets: Pre-configured monitoring for Mixpanel, Figma, Asana, and more

Impact: Reduces competitive intelligence gathering from 3+ hours per week to under 60 seconds, achieving 80%+ classification precision on real-world competitor announcements.

Technologies Used: Python, Streamlit, OpenAI GPT-4-mini, SQLite, Plotly, Newspaper3k, Feedparser