AI Pipeline
Text prediction application with machine learning
Built an end-to-end NLP application that turned a large text corpus into a predictive language model and deployed it as an interactive Shiny app.
Stack
R, Shiny, NLP, Machine Learning
Impact
Shows early end-to-end ML product thinking: data preparation, model design, evaluation, and a user-facing interface for real-time interaction.
Text prediction application with machine learning
This project focused on building a usable NLP application, not just training a model. I developed a text-prediction workflow that cleaned a large corpus, engineered n-gram features, and turned the result into an interactive Shiny app for real-time next-word suggestions.
What I built
- Text cleaning and preparation for a large language corpus
- N-gram feature engineering for next-word prediction
- A predictive language model
- A live Shiny interface for real-time interaction
Why it matters
This project shows an early version of how I like to work: taking a machine-learning problem all the way from data preparation to a user-facing application. It highlights practical NLP foundations, model development, and a willingness to ship something interactive instead of leaving the work in notebooks.
Takeaway
More than a standalone NLP demo, this project reflects a pattern that still runs through my work: building complete systems that move from data to model to interface in a way a real user can actually use.