Data-Driven Insights, Analytics Solutions.

I've worked on a variety of data analytics projects, focusing on extracting meaningful insights from complex datasets and building analytical solutions that drive business decisions. Here are some highlights I'm proud of, showcasing my process from data exploration to actionable insights.
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Spring 2026
Amazon Review Sentiment at Scale (group capstone)
Group capstone on the Amazon Reviews fastText corpus (4M labeled reviews)—EDA, TF–IDF linear baselines, unsupervised clustering, MLP and DistilBERT comparisons, and a single locked evaluation on held-out test data with strict leakage controls.
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Amazon Review Sentiment at Scale (group capstone)
Spring 2026
Automotive Supply Chain Risk with Neo4j Graph Analytics (group capstone)
Group capstone modeling a multi-tier automotive supply network in Neo4j (87k+ BOM links, facilities, products), then using graph algorithms—PageRank for critical chokepoints and Leiden community detection for modular risk views—to prioritize disruption triage over expensive recursive SQL patterns.
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Automotive Supply Chain Risk with Neo4j Graph Analytics (group capstone)
Dec 2025
AWS Serverless ETL Pipeline
Built a complete serverless ETL pipeline on AWS that processes e-commerce sales data from ingestion to visualization. Implemented automated data processing with AWS Glue, SQL analytics with Athena, and interactive dashboards with Nuxt 4 and QuickSight—all for less than $2/month.
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AWS Serverless ETL Pipeline
Sept 2025 - Oct 2025
AWS Sentiment Analysis System
Built a production-ready sentiment analysis system for customer feedback analysis using AWS Free Tier services. Implemented serverless architecture with Lambda, DynamoDB, S3, and API Gateway, creating a cost-effective solution that processes thousands of feedback items with 75-80% accuracy.
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AWS Sentiment Analysis System
Aug 2025 - Sept 2025
Fraud Detection System
A comprehensive machine learning system for detecting fraudulent transactions in mobile money services using unsupervised anomaly detection. Implements Isolation Forest algorithm to identify potentially fraudulent transactions with ~2% accuracy rate.
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Fraud Detection System
July 2025 - Sept 2025
GymTracker Streamlit Dashboard
Developed an interactive fitness tracking application using Streamlit and Python. Features include workout analytics, progress visualization, and personalized recommendations based on user data patterns.
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GymTracker Streamlit Dashboard
Aug 2025 - December 2025
Hospital Readmission Prediction Model
Built three machine learning models (Logistic Regression, CART, Random Forest) to predict 30-day hospital readmissions using data from 130 US hospitals. Achieved 60-62% accuracy with high interpretability, enabling healthcare providers to identify high-risk patients and improve care outcomes.
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Hospital Readmission Prediction Model
Jan 2025 - June 2025
Traffic Violation Data Analysis
Conducted comprehensive analysis of traffic violation data using Jupyter Notebooks and Python. Identified patterns in violation types, locations, and time periods to help improve traffic safety and enforcement strategies.
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Traffic Violation Data Analysis

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