A passionate Computer Engineering student with a solid foundation in software development principles and a keen interest in data analysis, machine learning, and cloud computing. Eager to apply my technical skills to build innovative software solutions and contribute to a collaborative engineering team.
With a solid foundation in Data Engineering and Devops Engineering, I'm on a mission to bridge the gap between infrastructure automation and business insights. Whether it's analyzing user behavior through dashboards or deploying scalable apps using Devops pipelines, I strive to create impact through efficient and intelligent solutions.
A CNN-based system that classifies blood groups from fingerprint images, delivering fast, non-invasive, and cost-effective predictions of ABO and Rh types.
This project predicts stock prices for Tata Motors, BHEL, Zomato, TCS, and OIL using Linear Regression, SVM, Random Forest, and XGBoost. We trained models on historical data, evaluated using MSE and R-squared. Future enhancements include deep learning techniques like LSTM for better predictions.
Machine Learning project replicating and extending the research paper "An Optimal House Price Prediction Algorithm: XGBoost" using Ames Iowa (USA) and Indian housing datasets with detailed model comparison and analysis.
Gained hands-on experience in ML and Data Science using RapidMiner for data preprocessing, modeling, and algorithm application.
Completed AI/ML internship using TensorFlow, focusing on data preprocessing, model training, and deep learning.
Built core cybersecurity skills in threat detection, network security, incident response, and ethical hacking.
Specialized in Cloud Computing, Data Analytics, and AI/ML. Completed multiple projects and virtual internships.
Completed 12th grade with a focus on PCM subjects and computer fundamentals.
Focused on holistic development through academics, sports, and community involvement during high school years.
