Pranchal Katiyar
Final Year IT Student | Cybersecurity & Data Enthusiast | Data Analytics Learner | Aspiring Full Stack Developer
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About Me
Get to know me better
Hello! I'm Pranchal Katiyar, a passionate final-year IT student at Ajay Kumar Garg Engineering College, specializing in cybersecurity, data analytics, and full-stack development. With a strong foundation in programming and a keen interest in emerging technologies, I'm dedicated to solving complex problems through innovative solutions.
My journey in technology has been driven by curiosity and a desire to make meaningful contributions to the digital world. I've successfully led teams of 50+ members, managed projects with budgets exceeding ₹5 lakhs, and built applications that serve thousands of users.
Education & Experience
Bachelor of Technology - IT
Specializing in Information Technology with focus on Data Structures, Algorithms, Web Development, and Machine Learning.
Coordinator - Renaissance AKGEC
Leading cross-functional teams of 50+ students, organizing events with 2000+ participants, and managing project budgets exceeding ₹5 lakhs.
Volunteer - NayePankh Foundation
Contributing to data-driven social initiatives supporting 500+ underprivileged individuals through technology solutions.
Certifications
Technical Skills
Technologies I work with
Programming Languages
Frontend Development
Backend & Databases
Data Analytics & Tools
Featured Projects
Some of my recent work
Full-Stack E-Commerce Platform
Responsive e-commerce application with user authentication, payment integration, and inventory management. Implemented RESTful APIs reducing load time by 40%.
Student Performance Prediction System
Machine learning model using logistic regression achieving 85% prediction accuracy. Designed interactive web interface using Flask for real-time predictions.
Real-Time Sales Analytics Dashboard
Comprehensive sales dashboard displaying 15+ KPIs for retail business analysis. Implemented advanced SQL queries and real-time data pipelines.
Movie Recommendation Web App
Full-stack recommendation system using collaborative filtering algorithms. Performed EDA on 10,000+ IMDB movie records and deployed on AWS.
Get In Touch
Let's discuss opportunities and collaborations