🎓 Data Science & Finance Student at Northeastern University

💼 Incoming Data Scientist | Teaching Assistant | Project Enthusiast

I’m a passionate Data Scientist focused on leveraging advanced analytics and machine learning to solve real-world problems. With a strong foundation in data science and finance, I’m excited about the opportunity to make a meaningful impact through data-driven solutions across various domains, including sports analytics, personal finance, and predictive modeling.

• Python, R, SQL

• Pandas, NumPy, scikit-learn, XGBoost, TensorFlow, Keras
• NLTK, spaCy (NLP)
• Matplotlib, Seaborn (Visualization)

• Jupyter Notebook, Streamlit, Docker, Git, Tableau

• MySQL, MongoDB, Redis

• Machine Learning (Supervised & Unsupervised)
• NLP, Model Evaluation, Imbalanced Data Handling

A comprehensive project focused on predicting residential housing prices and forecasting REIT performance in the U.S. housing market. This project integrates data from multiple sources, including Redfin, Zillow, and Alpha Vantage, to create robust predictive models.

• Languages & Libraries: Python, scikit-learn, XGBoost, Pandas, NumPy, Matplotlib, Seaborn
• Tools & Frameworks: Jupyter Notebook, Streamlit
• Key Features:
  • Aggregated data from Redfin and Zillow to include metrics like average sales price and total listings by county.
  • Leveraged Alpha Vantage to obtain REIT closing prices and market indicators.
  • Trained tree-based models (Random Forest, Adaboost, XGBoost) to predict REIT prices into the future.
  • Developed a backtesting framework to evaluate custom trading strategies against market benchmarks.
  • Built an interactive Streamlit application to present forecasts, trading strategy performance, and visualizations for stakeholders.
  • Deployed Streamlit App: Explore the interactive tool to visualize predictions and backtesting results.

A project leveraging NLP and regression models to predict commodities price movements. The project uses large language models (LLMs) for feature engineering, alongside sentiment analysis and model evaluation to assess price trends.

• Tech Used: Python, NLP, Regression Models, Jupyter Notebook
• Key Features:
  • Feature engineering using LLMs
  • Sentiment analysis for market prediction
  • Model evaluation using regression metrics

A personalized fantasy football assistant that provides data-driven insights based on player statistics, game trends, and community sentiment analysis from Reddit. This project uses Natural Language Processing (NLP) to analyze discussions and sentiment around players, offering real-time recommendations and advice to fantasy football enthusiasts.

• Tech Used: Python, NLP, APIs, Reddit Scraping, Jupyter Notebook
• Key Features:
  • Uses Retrieval Augmented Generation (RAG) for personalized advice based on community input.
  • API integration with NFL data to fetch real-time player statistics.
  • Offers personalized recommendations for fantasy football strategies based on user preferences and real-time data.

🌐 LinkedIn
📧 Email

• Fitness Enthusiast & Amateur Nutritionist 🏋️‍♂️🍌
• Dragon Boat Paddler 🐉🚣
• Professional Shower Singer 🎤🎵
• Avid 49ers and Manchester United Fan 🏈⚽

Feel free to fork any projects or reach out for collaborations! 🌟