MS Data Science @ Montclair State University '26 | AI/ML · NLP · Python · SQL · Power BI

Building AI-powered pipelines, machine learning models, and analytics dashboards that turn complex data into real decisions.

• 🎓 MS Data Science, Montclair State University (GPA 3.8/4.0, May 2026)
• 🤖 Passionate about AI/ML, NLP, and applied data science
• 💼 Former Data Analytics Intern @ Main Flow Services & Technologies
• 📍 New Jersey — open to NYC metro & remote roles
• 🛂 Available on OPT from June 2026
• 📫 Reach me: vinayakvemula09@gmail.com
• 🔗 LinkedIn

Languages & ML

AI & NLP

Data & BI

Tools

Python · Flask · Pandas · SciPy · Chart.js · EPA Data

Built a full-stack analytics dashboard analyzing U.S. recycling rates (1960–2022) using real EPA government data.

• Engineered complete ETL pipeline from raw Excel → structured CSVs → 9 Flask REST API endpoints
• Applied linear regression & Pearson correlation (Paper R²=0.97, Metal R²=0.91)
• Key insight: deposit-law states recycle 2.4× more glass than non-deposit states
• Interactive dashboard with choropleth maps, KPI cards, and trend visualizations

Python · Scikit-Learn · SMOTE · PCA · XGBoost · Matplotlib

End-to-end ML pipeline on 284,000+ financial transactions for anomaly and fraud detection.

• Handled severe class imbalance with SMOTE oversampling
• Applied PCA for dimensionality reduction + StandardScaler normalization
• Benchmarked Logistic Regression, Random Forest, and XGBoost optimizing for fraud recall

Python · Pandas · SQL · Plotly · Seaborn · Mapbox

Analyzed 39,000+ FARS crash records to identify temporal, geographic, and environmental risk patterns.

• Optimized SQL queries reduced report generation time by 40%
• Interactive Plotly dashboard with bubble maps, choropleth maps, and drill-down filters
• Key finding: evening hours (5–8 PM) + adverse lighting = top contributing factors

PyTorch · GCN · Cora Dataset · NMI · Modularity Metrics

Unsupervised graph clustering pipeline using Graph Convolutional Networks for community detection.

• Preprocessed Cora citation dataset to generate graph embeddings
• Evaluated with Normalized Mutual Information (NMI) and modularity metrics
• Applied deep learning for representation learning on graph-structured data

Python · Scikit-Learn · XGBoost · AdaBoost · SVM · NumPy

Comprehensive benchmarking of 8 classification algorithms on structured datasets.

• Algorithms: Decision Tree, Naive Bayes (Gaussian & Multinomial), SVM (Linear & RBF), k-NN, Random Forest, AdaBoost, XGBoost
• XGBoost: 97.8% accuracy · SVM (RBF): 97.6% accuracy
• Full performance report with trade-off analysis across accuracy, interpretability, and compute cost

Python · Scikit-Learn · Random Forest · K-Means · Linear Regression

Multi-algorithm ML on Spotify audio features to predict song popularity.

• Compared classification, regression, and clustering approaches with cross-validation
• Feature importance: energy, danceability, and loudness are top predictors

Open to AI/ML Analyst, Junior Data Scientist, and NLP Analyst roles. Available on OPT June 2026.