Engineering practical AI solutions. I develop end-to-end machine learning applications from robust data pipelines to production-ready REST APIs.
I am an Artificial Intelligence & Data Science engineering student interested in building machine learning systems that solve practical problems. My work spans predictive modeling, anomaly detection, time-series forecasting, and end-to-end AI application development from raw data to deployed APIs.
Currently learning, building, testing, and shipping.
Engineering workflow
Collect & understand
EDA & feature analysis
Train & tune
Metrics & validation
API & integration
Alerts & retraining
Novus Neuron Club, DYPIEMR
Guided junior members in data preprocessing, machine learning model development, evaluation, and project troubleshooting. Provided structured technical debugging and implementation support for AI/ML projects.
Dr. D. Y. Patil Institute of Engineering, Management & Research (DYPIEMR), Pune
Pursuing a comprehensive engineering degree with a specialized focus on machine learning algorithms, deep learning architectures, and data science workflows.
Dnyansamwardhini Junior College, Shirwal, Satara
Graduated in the Bifocal Stream focusing on Computer Science fundamentals.
Dnyansamwardhini Vidyalaya, Shirwal, Satara
Completed foundational secondary education.
Applied ML systems, not template demos
AI-Powered CO₂ Emission Monitoring & Compliance Platform. Track emissions, predict trends with Prophet, and stay compliant using automated alerts and anomaly detection.
Industrial plants struggle with fragmented carbon accounting and regulatory compliance, leading to unexpected penalties.
Chose Prophet over ARIMA for its superior handling of missing emission logs and weekly seasonality; used Isolation Forest for robust, high-dimensional anomaly detection without needing labeled outliers.
End-to-end Air Quality Index forecasting system. Uses XGBoost and Random Forest to predict AQI from 11 distinct pollutant parameters with high precision.
Citizens and authorities lack granular, localized air quality forecasts based on high-dimensional environmental sensor data.
Deployed XGBoost over Random Forest for gradient-based optimization on non-linear pollutant correlations, ensuring maximum accuracy on messy real-world data.
Complete ML pipeline: regression, classification, clustering, deep learning, and reinforcement learning.
10-course professional track covering the full data science lifecycle with hands-on projects.
B.E.
AI & Data Science
DYPIEMR, PUNE
8.53
CGPA / 10
ONGOING
Python
Primary language
+ SQL
AI/ML
Primary focus
DATA SCIENCE
I approach machine learning as a complete engineering system — not just model training. It starts with understanding the real problem and building clean data pipelines, then training models validated for performance and real-world reliability. Production-ready APIs connect AI to applications, and dashboards translate model outputs into actionable insights. The pipeline includes monitoring, threshold alerts, and retraining schedules where appropriate.
Open to AI/ML internships, research opportunities, and real-world problem solving.
