This report presents an exploratory data analysis (EDA) for the Data Science Capstone Project, based on text data from three sources: blogs, news, and Twitter. The primary objective is to understand the structure and content of the datasets in preparation for building a predictive text model.

ITs a plot from SEM

This project presents a Shiny web application that predicts the next word in a given English phrase using an N-gram language model. The app takes user input and suggests the most likely next word based on patterns learned from a large corpus of text (e.g., Twitter, blogs, and news data). The underlying algorithm leverages quadgrams, trigrams, and bigrams with a backoff strategy to ensure accurate and fast predictions. This tool demonstrates a simple yet effective approach to predictive text modeling, similar to autocomplete features used in modern keyboards.

Malaria vaccination Dropout rate in ten health zones in Kongo