Machine Learning Engineer • Data Analytics • MLOps • Python • SQL • Cloud

I am a Machine Learning and Data Analytics professional with a strong interest in building production-ready machine learning systems, scalable analytics pipelines, and data-driven applications.

I recently completed my B.Tech in Electronics and Telecommunication from Vishwakarma Institute of Technology, Pune. My work combines machine learning, analytics, data engineering, and MLOps to create solutions that are practical, measurable, and deployment-ready.

• Machine Learning Intern with hands-on experience in computer vision
• Strong in Python, SQL, machine learning, analytics, and cloud tools
• Interested in Machine Learning Engineering, Data Analytics, Data Engineering, and MLOps
• Focused on transforming data into meaningful business and technical outcomes

An end-to-end machine learning pipeline built to predict hotel reservation cancellations using large-scale booking data.

Tech Stack: Python, Scikit-learn, MLflow, Jenkins, Docker, Kubernetes, FastAPI

Highlights

• Built predictive models using 30,000+ booking records
• Performed preprocessing, feature engineering, and model evaluation
• Implemented CI/CD automation for deployment workflows
• Deployed a scalable REST API for real-time prediction use cases

A scalable SQL-based data warehouse and analytics solution designed for business reporting and KPI analysis.

Tech Stack: SQL Server, T-SQL, ETL, Power BI, Data Modeling

Highlights

• Designed Bronze–Silver–Gold medallion architecture
• Built optimized star schema for analytical queries
• Developed ETL pipelines for ingestion and transformation
• Created business dashboards for KPI reporting and decision support

A machine learning regression system built to predict housing prices with structured preprocessing and model optimization.

Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib

Highlights

• Performed EDA, correlation analysis, and feature engineering
• Trained and optimized multiple regression models
• Evaluated performance using RMSE, MAE, and R²
• Improved model quality through scaling and outlier handling

• Machine Learning Engineering
• Data Analytics and Business Intelligence
• Data Warehousing and SQL Analytics
• MLOps and Model Deployment
• Cloud-Based Data Solutions
• End-to-End Predictive Systems

Building intelligent machine learning systems, scalable analytics workflows, and production-ready data solutions.