I'm a UC Berkeley-certified Machine Learning & AI professional and a full-stack web developer trained at AppAcademy. I’m passionate about applying machine learning, NLP, and modern development tools to solve real-world problems and create meaningful user experiences.
I transitioned into tech after years of hands-on work as an automotive technician which was an experience that sharpened my ability to work under pressure, think systematically, and solve complex problems. I now bring that same mindset to building and debugging intelligent systems and full-stack applications.
- Languages: Python, JavaScript, SQL
- Frameworks & Libraries: React, Node.js, Express, pandas, scikit-learn, TensorFlow
- Databases: PostgreSQL, SQLite
- Tools: Git, GitHub, Jupyter, VS Code
Stock Sentiment Analysis Using Twitter Data
Analyzed stock price behavior in relation to public sentiment using an existing dataset of tweets and market data. Built an NLP pipeline, engineered features, and trained ML models to uncover predictive relationships.
Tech: Python, pandas, scikit-learn, Matplotlib
Predictive Modeling of Car Prices
Explored how different features impact car prices using regression models. Cleaned and processed structured data, ran exploratory analysis, and built predictive models.
Tech: Python, pandas, scikit-learn, seaborn
Classifier Benchmarking for Coupon Acceptance
Compared the performance of multiple machine learning classifiers on a dataset involving consumer coupon acceptance behavior. Focused on accuracy, interpretability, and evaluation metrics.
Tech: Python, pandas, scikit-learn
- 💼 LinkedIn: https://www.linkedin.com/in/david-shamis/
- ✉️ Email: [email protected]
Thanks for visiting my GitHub!