Evolución de consumos de electricidad y agua así como tarificación.

Beautiful Queen's Neckless & Sports facilities around Marine Drive, Mumbai

Thalanga VMS Deposit | Pre-Estimation Workflow & Domain Modeling

first day using rpubs

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.

Map of Major healthcare institutions and schools in Boston, along with their national ranking

Massachusetts Trade model with foreign nations, Ireland highlighted to show the impact of historical ties on modern trade trends.