Professional Summary
Pre-final year B.Tech Computer Science & Engineering student specializing in Machine Learning and Cloud Computing. Experienced in architecting, containerizing, and deploying end-to-end data pipelines and web applications. Proficient in Python, SQL, Scikit-learn, and modern deployment toolkits (Docker, FastAPI, Streamlit), demonstrated by designing and launching 10+ live-deployed project applications. Passionate about bridging theoretical algorithms with production-ready, scalable software solutions.
Education
B.Tech in Computer Science & Engineering (Specialization: Cloud Computing & Machine Learning)
- Academic Standings: CGPA: 8.0+ / 10. Relevant Coursework: Data Structures & Algorithms, Machine Learning, Database Management Systems, Cloud Infrastructure, Systems Design, Python.
Technical Skills
Languages & Core:
Python, SQL, C
Libraries & Modeling:
Scikit-learn, Pandas, NumPy, Feature Engineering, Model Diagnostics, Hyperparameter Tuning
MLOps & Hosting:
Docker, FastAPI, Flask, Streamlit, Git, GitHub Actions, Vercel, Render
System Design:
RESTful APIs, Containerization, Relational Databases, Scalable Infrastructure, Reproducible Pipelines
Projects
Python, Scikit-learn, Pandas, Streamlit, Vercel, GitHub Actions
- Architected an end-to-end regression application that structures data collection, automated preprocessing, and model scoring.
- Compared model architectures (Linear vs. Ridge Regression) using RMSE and R² diagnostics to select the best predictor for deployment.
- Deployed the interactive inference app to Vercel, integrating automated CI/CD checks via GitHub Actions for staging quality control.
Python, Pandas, Seaborn, Matplotlib, Streamlit, Predictive Modeling
- Ingested and structured 10,000+ credit records; handled null indices, categorical formatting, and data cleansing routines.
- Designed robust exploratory data analysis pipelines using Seaborn/Matplotlib to extract primary churn drivers and segment customer behaviors.
- Built and launched an interactive analytics dashboard allowing marketing stakeholders to identify high-risk accounts instantly.
Experience & Open Source Contributions
- Created and maintained 10+ public GitHub repositories showcasing clean code structure, comprehensive README documents, and automated scripts.
- Published verified analytics datasets and model evaluation logs on Kaggle, emphasizing notebook clarity and pipeline repeatability.
- Competed in the Coderush 2.0 Hackathon (2025), writing algorithmic logic to solve optimization problems under strict deadlines.