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ahartshorn416/README.md

Hi! 👋 I'm Alison Hartshorn

💫 About Me:

I'm a Data Scientist with a Master's in Data Science from Merrimack College specializing in end-to-end ML pipelines, agentic AI systems, RAG applications, and large-scale data analysis. My projects work with real-world datasets — 16M+ row U.S. Census data, 4.25M HMDA mortgage applications, and 941K NYC 311 service requests. I build in Python, SQL, and R, orchestrate pipelines with Apache Airflow, deploy LLM-powered applications with the Anthropic Claude API, and hold an AWS Cloud Practitioner certification. Background in legal operations and data governance. Currently seeking entry-level Data Scientist roles.

🔭 Featured Projects:

  • 🛒 ReviewPulse — Agentic AI Consumer Insight System — Autonomous LLM agent (Claude tool use) that investigates product review data, detects statistically significant defect trends via spike detection, and generates evidence-backed action reports; includes a Streamlit dashboard with an interactive Q&A assistant grounded in the same agent tools
  • ⚖️ CFPB Legal Q&A RAG Agent — Retrieval-Augmented Generation pipeline built with LangChain, ChromaDB, and the Anthropic Claude API; ingests the CFPB Supervision and Examination Manual, retrieves semantically relevant passages via cosine similarity search, and generates cited, grounded answers to mortgage compliance questions via a Streamlit chat interface
  • 🏦 Home Loan Approval Prediction — ML pipeline on 4.25M real HMDA 2023 mortgage applications; XGBoost ROC-AUC 0.9932, 96.3% accuracy across 121 features; SHAP-based feature importance and fairness analysis across demographic groups
  • 🏠 Rent Burden Prediction — Fairness & ML analysis on 16M+ ACS PUMS household records (Logistic Regression, Random Forest, Gradient Boosting); equity analysis across race, sex, and geography for HUD policy context
  • 🔄 HMDA Mortgage Data Pipeline — Apache Airflow — Orchestrated weekly ETL pipeline using Apache Airflow (Astro CLI) on 279K NC HMDA 2023 mortgage records; automated ingestion, cleaning, quality validation (4 data checks), and model performance monitoring as a DAG; AUC 0.7273 on held-out test set

🌐 Socials:

LinkedIn Tableau

💻 Tech Stack:

Python R MySQL PostgreSQL NumPy Pandas Matplotlib scikit-learn XGBoost PyTorch Apache Spark Apache Airflow MLflow LangChain Anthropic Streamlit Tableau Power BI SciPy

Pinned Loading

  1. nyc311-weather-dashboard nyc311-weather-dashboard Public

    Does rain make New Yorkers angrier? ETL pipeline + Power BI dashboard correlating 941K NYC 311 complaints with daily weather data. Python · PostgreSQL · Automated daily refresh.

    Python

  2. predicting-rent-burden predicting-rent-burden Public

    ML models predicting U.S. household rent burden using 16M+ ACS survey records — includes fairness analysis across race, sex & geography to inform housing policy.

    Python 1

  3. home_loan_approval_prediction home_loan_approval_prediction Public

    Predicts U.S. home loan approvals using 4.25M real HMDA 2023 applications — XGBoost, Random Forest, Logistic Regression, ROC-AUC 0.9932

    Python

  4. heart-disease-sparkml heart-disease-sparkml Public

    End-to-end heart disease classification pipeline built with Apache SparkML, MLflow experiment tracking, and hyperparameter tuning via CrossValidator.

    Python

  5. hmda-airflow-pipeline hmda-airflow-pipeline Public

    Orchestrated HMDA mortgage data pipeline using Apache Airflow — automated ingestion, cleaning, quality validation, and weekly model performance monitoring on 279K NC loan applications.

    Python

  6. reviewpulse-agent reviewpulse-agent Public

    Agentic AI system that investigates customer product reviews, autonomously detects emerging defect trends via LLM tool use, and generates evidence-backed action reports — with a live dashboard and …

    Python