Educational Background
The candidate is pursuing a B.Tech in Computer Science and Engineering at the Indian Institute of Information Technology, Sonepat, with a CGPA of 8.58 (2021–2025). They completed their Class 12 with 97.4% at Kendriya Vidyalaya, AFS, Thiruvananthapuram (2020–2021).
Hackathon Experience
Participated in the GSTN Analysis Hackathon (Sep–Oct 2024), where the candidate collaborated on developing a predictive model for GST analytics using AI/ML techniques. They worked with a dataset of 900,000 records and achieved an accuracy of 96% and an F1-Score of 0.955.
Project Work: Women Safety Analytics
Developed a real-time monitoring system (WoSaFT) to enhance women’s safety using advanced analytics. The system can monitor, analyze, and report gender distribution and detect suspicious scenarios, contributing to strategic safety planning.
Project Work: Coronary Heart Disease Prediction
Engineered a predictive model for estimating the 10-year risk of Coronary Heart Disease. By combining Random Forest and Artificial Neural Networks, the model improved its accuracy from 80% to 90%, aiding early risk detection.
Project Work: Attendance System using Face Recognition
Designed and implemented an automated attendance system using facial recognition. The system reduced manual tracking time by 80%, achieved over 95% accuracy, and minimized errors compared to traditional attendance methods.
Technical Skills and Tools
The candidate is proficient in Python, C, Java, SQL, and has hands-on experience with tools and libraries such as MySQL, TensorFlow, Keras, Scikit-learn, OpenCV, Selenium, and Jupyter Notebook, with coursework covering Machine Learning, AI, Data Structures, and Computer Vision.