I’m a passionate student with a strong background in data analytics, aiming to use my problem-solving and research skills. Currently focused on machine learning and statistical analysis, I enjoy turning data into insights and working with teams to solve real-world challenges. I'm always eager to learn and contribute to projects that make a difference.
View my CVBuilt a real-time optimized ETL pipeline for distributed systems reducing data loading time by 15 minutes and an adaptive patient forecasting system.
Implemented YOLO V8 and OCR technology to automate invoice data entry, reducing manual processing time by 60 hours/week and improving accuracy by 85%.
Developed a CNN-based model to classify MRI brain scans into glioma, meningioma, and pituitary tumors. Achieved 96.5% accuracy with explainability via LIME & Grad-CAM.
Designed an interactive Power BI dashboard to monitor sales trends and analyze customer behavior for a metal manufacturing business with dynamic filtering.
Designed a data-driven procurement system using GARCH, Bollinger Bands, LOF, and K-Means to analyze gold price trends and customer behavior.
Built an interactive Streamlit dashboard to visualize global earthquake data (1995–2023) with geospatial heatmaps, anomaly detection, and trend analysis.
Built a Flask app that recommends crops and fertilizers from soil data and detects crop diseases using ML and deep learning models.
Built a personalized recommendation system using Twitter sentiment analysis and Multinomial Naive Bayes to suggest movies and books based on user tweets.
A system for efficiently managing supermarket operations using computerized tools to streamline processes, store data, and enhance staff productivity.
Won 2nd prize in 'Data Vista' contest held at TSM Yukthi'25, 2025.
Conducted non-technical event 'Treasure Hunt' at TCE, 2025.
Organised 'The Data Gambid' competition in TECHUTSAV 2025 at TCE.
Certified for Smart Vehicles Innovation during Hackathon 2023.
Won 1st Prize for Paper Presentation at TCE, 2023.
Participated in Machine Learning and Data Science workshop at NIT, June 2023.
Completed Python basics workshop conducted by BlueBuzz, 2023.
Won 1st Prize for Robotics poster presentation at TCE, 2022.
Participated in Explainable AI (XAI) for Cyber Security and Resilience, DEC 2024.
Developed a CNN-based deep learning model to classify MRI brain scans into tumor types with 96.5% accuracy.
Implemented time-series models to predict pharmaceutical sales trends, enabling better inventory and supply chain decisions.
Used regression analysis to identify key engagement factors affecting student academic performance across institutions.
Created a PyTorch CNN ensemble model for malaria cell image classification, achieving over 96% accuracy with data augmentation.
Designed an AI model that recommends optimal crops based on soil data and seasonal patterns to enhance yield and efficiency.
Feel free to reach out for collaboration, project discussions, or just a friendly chat!
Based in India | Open to work opportunities