This report presents an exploratory analysis of the SwiftKey natural language dataset consisting of blogs, news articles, and Twitter text. The analysis includes data cleaning, summary statistics, visualization of word frequencies, word clouds, and n-gram analysis. The report also outlines the planned approach for building a next-word prediction model and deploying it through a Shiny web application as part of the Coursera Data Science Capstone project.

This project presents a simple Shiny application that predicts the next word based on user input. The model uses a lightweight rule-based algorithm to generate fast and consistent predictions. The app demonstrates how predictive text can enhance typing efficiency and user experience. Potential applications include mobile keyboards, search engines, and writing assistants.

This report presents an exploratory data analysis of three English text datasets (blogs, news, and Twitter). It includes summary statistics, observations of word frequency and sentence length, and outlines a plan for building a predictive text model and a Shiny application.