About Me
Data Science student at DBATU specialising in ML, deep learning, and recommendation systems. Engineered CareerSense — a job recommendation platform across 10K+ records with 70% faster queries — and trained a CME Detection System at 95% accuracy on live satellite data.
Skills & Technologies
Work Experience
Data Analytics & Data Science Intern
Engineered a Sales Analysis Dashboard in Python, Pandas, Power BI, and Excel, surfacing 3 key revenue KPIs and replacing a manual spreadsheet reporting process. Trained a Customer Churn Prediction model using Scikit-learn and SQL, flagging at-risk segments to inform retention campaign targeting.
Data Analytics Intern
Queried and restructured relational databases using SQL, resolving inconsistencies across 3 software performance assessment reports. Scripted data processing pipelines in Python, cutting manual steps by ~30% and produced visual reports that reduced QA review time across 2 cycles.
Projects
CareerSense AI — Job Recommendation System
Engineered a full-stack recommendation platform handling 10K+ job records; automated CSV ingestion and admin workflows with Django and SQLite. Implemented cosine similarity over precomputed skill vectors, cutting query latency by 70% via Pickle-based matrices and SQL pagination.
Aditya-L1 CME Detection System
Architected a solar-event detection system with a FastAPI backend and live Streamlit alert dashboard; containerised the full pipeline using Docker. Trained ensemble ML models on real-time Aditya-L1 satellite data achieving 95% accuracy in CME identification.
Power BI Financial Dashboard — Burn Rate & Runway
Coded DAX measures simulating 60% revenue growth and 25% cost reduction scenarios; pinpointed salary costs as the 68% primary burn driver via waterfall analysis.
Education
B.Tech
Computer Science