SHAURYA
VERMA

An AI-powered career guidance platform that helps users build professional resumes, analyze career profiles, and get personalized job recommendations using intelligent insights. The platform leverages OpenAI APIs to provide smart resume feedback, skill gap analysis, and tailored career path suggestions. Built with a responsive React frontend and a robust backend, it offers a seamless experience for job seekers looking to stand out in a competitive market.

A fully automated Python tool designed to help developers maintain and visualize their GitHub contribution streaks effortlessly. It leverages GitHub Actions to make daily commits at a scheduled time, ensuring your streak never breaks even when you are away. The tool supports customizable commit schedules, automatic timezone handling, and provides a lightweight, cloud-based solution that requires no local setup.

A full-stack food ordering application built with the MERN stack, enabling users to browse restaurant menus, add items to cart, and place orders with a smooth checkout experience. The backend features RESTful APIs built with Express.js and MongoDB for persistent data storage. It includes user authentication, order history tracking, and an admin dashboard for managing menu items and monitoring incoming orders in real time.

An interactive real-time platform that combines audio streaming, live chat, and collaborative content creation into a single unified experience. Powered by WebRTC for peer-to-peer audio communication and Socket.io for instant messaging, users can join rooms, stream audio, and chat simultaneously. The React-based UI provides an intuitive interface with room management, user presence indicators, and a rich content creation editor.

A comprehensive machine learning project that analyzes text reviews and classifies sentiment as positive, negative, or neutral using NLP techniques. The pipeline includes text preprocessing with NLTK, feature extraction using TF-IDF vectorization, and model training with scikit-learn classifiers. Extensive exploratory data analysis and model comparison are performed in Jupyter Notebooks, achieving high accuracy across multiple evaluation metrics.

An in-depth exploratory data analysis project on a large car sales dataset, uncovering key trends, pricing patterns, and market insights through powerful visualizations. Built with Pandas for data manipulation, Matplotlib and Seaborn for detailed charting, the project covers data cleaning, statistical analysis, correlation mapping, and feature engineering. The findings provide actionable insights for understanding consumer preferences and market dynamics.