Data Analyst
Computer Science and Engineering student specializing in AI and Machine Learning, with experience in data analysis, dashboards, machine learning APIs, model deployment, and exploratory data analysis.
Excelerate
Sep 2025
Analyzed complex datasets to extract actionable insights and support data-driven decision making; created interactive dashboards and visualizations for stakeholders; performed exploratory data analysis to identify trends, patterns, and anomalies; collaborated with cross-functional teams to deliver analytical solutions aligned with business objectives.
Bachelor of Technology, Computer Science & Engineering (AIML)
2023 – 2027
Class 12th (CBSE), Physics, Chemistry, Mathematics, Biology
2023
87%
Class 10th (CBSE)
2021
86%
Built a B2B diabetes risk prediction API using Python, FastAPI, XGBoost, MLflow, Docker, and Streamlit, trained on 253K CDC BRFSS records with an AUC of 0.825. Deployed it on Render with Docker and GitHub Actions CI/CD; implemented SHAP explainability, API key authentication, automated data drift detection with Evidently AI, SQLite prediction logging, MLflow experiment tracking, automated best-model selection, and a live Streamlit dashboard.
Built an end-to-end sports analytics platform using Python, XGBoost, TensorFlow, Scikit-learn, FastAPI, Docker, and Streamlit on 180K+ football records. Achieved 50.77% match outcome prediction accuracy, deployed four REST API endpoints, used GitHub Actions CI/CD, identified six player archetypes through K-Means clustering on 10,582 players, and created PCA-based interactive visualizations.