I engineered a robust Lane Detection and Speed Breaker Warning System designed to elevate the safety and precision of autonomous vehicles. Leveraging convolutional neural networks (CNNs), the system accurately identifies lane boundaries from real-time camera feeds, while a dedicated machine learning model detects speed breakers under varying conditions. Through rigorous simulations and real-world testing, the system demonstrated marked improvements in lane-keeping performance and timely responses to road irregularities—paving the way for smarter, more adaptive autonomous navigation. 

As part of an advanced data science initiative, I developed an interactive dashboard that delivers real-time insights into the job market using live data from the Adzuna API. This tool empowers users to explore current job listings, filter by title and location, analyze salary trends, identify in-demand skills, and discover top hiring companies—all through a clean, intuitive interface built with Streamlit and enhanced by dynamic visualizations using Plotly and Matplotlib. Designed for professionals and analysts alike, the dashboard also offers downloadable reports for deeper exploration, making it a powerful asset for career planning and market research. 

I created an interactive data analytics dashboard to track and visualize the impact of COVID-19 across India, using real-time data to make public health insights more accessible and actionable. Built with Python, Pandas, NumPy, and Streamlit, the dashboard features dynamic charts, state-wise and national views, and trend analysis through moving averages. It offers a clean, intuitive interface powered by Plotly visualizations, enabling users to explore daily case metrics and historical patterns with ease. This project highlights the power of data science in addressing real-world challenges through effective data pipelines, visualization, and user-centric design.

This project explores the intricate relationship between child nutrition and economic conditions across global regions, emphasizing how poverty impacts adolescent health outcomes. Leveraging datasets from UNICEF, WHO, and the World Bank, I conducted a comprehensive analysis using statistical modeling and data analysis techniques. A key finding revealed a strong positive correlation (r = 0.85) between undernourishment and the proportion of the population living below the $1.90/day poverty line. These insights underscore the critical role of economic development in improving child nutrition and advocate for targeted policy interventions to address disparities. This work reflects my ability to translate complex data into actionable public health insights.