This project presents a next-word prediction application developed as part of the Johns Hopkins Data Science Capstone (Coursera). The objective was to build a lightweight and efficient predictive text model similar to those used in smart mobile keyboards. Using the HC Corpora English datasets (blogs, news, and Twitter), I performed exploratory data analysis, constructed n-gram frequency tables (2-gram, 3-gram, and 4-gram models), and implemented a backoff strategy to handle unseen word combinations. The final product is a deployed Shiny application that predicts the top three most likely next words given an input phrase. The model is optimized for performance and memory efficiency to ensure responsiveness in a web environment. Key Features: Frequency-based n-gram language model Backoff prediction logic (4-gram → 3-gram → 2-gram → fallback) Efficient storage using serialized RDS tables Deployed via shinyapps.io Live application: https://chidemannie.shinyapps.io/Swiftkey_Next_Word_Predictor/ Source code: https://github.com/chidemannie/swiftkey-capstone This slide deck summarizes the modeling approach, performance considerations, and demonstrates how the application works.

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Desarrollar habilidades básicas para manejo de información y comandos en R.