I’m Preety Sriwastava — a highly motivated and growth-oriented Computer Science Engineering student with a strong foundation in programming, data analytics, and full-stack development. With hands-on internship experience in Salesforce Development and Data Analytics, I’ve gained practical exposure to real-world problem-solving, process automation, and deriving insights from large datasets. I have a deep interest in AI/ML, full-stack web development, and impactful project building. From developing a Disease Prediction Model using Machine Learning to building a real-time GitHub Webhook Tracker using Flask and MongoDB, I love turning technical knowledge into tangible, scalable solutions. I’m proficient in Python, C++, and Java, and have worked with modern web technologies like React.js, Node.js, Flask, MongoDB, and cloud tools like Ngrok and GitHub Actions. I enjoy working on innovative ideas, collaborating with others, and constantly expanding my skill set. When I’m not coding, I love exploring design trends, participating in virtual job simulations, and staying updated with the latest advancements in tech. I'm looking forward to opportunities where I can contribute to meaningful tech-driven solutions and grow alongside like-minded professionals.
Bakhtiyarpur College Of Engineering Patna, CGPA: 8.12
Government Women's Polytechnic Patna, CGPA: 8.24
Aacharya Sudarshan Vidyapeeth Mubarakpur Sitamarhi, CGPA: 9.2
A full-stack web application that captures real-time GitHub events (push, pull request, merge) using GitHub Webhooks. Flask backend logs data into MongoDB, and the frontend displays events in a live-updating dashboard using HTML, CSS, and JavaScript.
Integrated with Ngrok for public HTTPS access and includes a dummy repo to trigger GitHub Actions.
Tech Stack: Python (Flask), MongoDB, HTML, CSS, JavaScript, Ngrok
🔗 Repositories:
•
action-repo
– Dummy repo to trigger actions
•
webhook-repo
– Main Flask + MongoDB project
Explored Diwali sales data using Python in a Jupyter Notebook—cleaned and transformed data from CSV, visualized insights using Pandas, NumPy, Matplotlib, and Seaborn.
Key insights included identifying top customer demographics, high-performing product categories and states, and uncovering trends like highest sales among 26–35‑year‑old females in UP, Maharashtra, and Karnataka.
Tech Stack: Python, Jupyter Notebook, Pandas, NumPy, Matplotlib, Seaborn
View Notebook →