This project demonstrates the end-to-end development of a data-driven application using R and Shiny. The goal is to build an interactive Next Word Predictor powered by an N-gram language model with a backoff strategy. The application processes text data from the Reuters crude oil dataset, cleans and tokenizes it, and constructs unigrams, bigrams, and trigrams to predict the most likely next word in a user-provided phrase. The model prioritizes trigram matches for context, falls back to bigrams when necessary, and defaults to unigrams for general predictions. To quantify uncertainty, the app calculates entropy, providing users with a measure of prediction confidence. The Shiny interface allows users to input text, view top predictions, and explore visualizations such as word frequency charts, bigram and trigram plots, and word clouds.

This project demonstrates the end-to-end development of a data-driven application using R and Shiny. The goal is to build an interactive Next Word Predictor powered by an N-gram language model with a backoff strategy. The application processes text data from the Reuters crude oil dataset, cleans and tokenizes it, and constructs unigrams, bigrams, and trigrams to predict the most likely next word in a user-provided phrase. The model prioritizes trigram matches for context, falls back to bigrams when necessary, and defaults to unigrams for general predictions. To quantify uncertainty, the app calculates entropy, providing users with a measure of prediction confidence. The Shiny interface allows users to input text, view top predictions, and explore visualizations such as word frequency charts, bigram and trigram plots, and word clouds.

This document presents a complete workflow for text mining and exploratory analysis using the crude dataset from the tm package in R. It is structured into two main parts: Data Loading and Preprocessing Loads the text corpus and applies standard cleaning steps such as lowercasing, punctuation and number removal, stopword elimination, and whitespace stripping. Summarizes the dataset with key statistics like document count, average length, and vocabulary size. Exploratory Data Analysis (EDA) Constructs a Document-Term Matrix (DTM) and computes term frequencies. Performs Unigram, Bigram, and Trigram analysis to identify frequent words and phrases. Visualizes results using bar plots and a word cloud for better interpretability. Highlights key observations about word distribution and contextual patterns. The analysis provides insights into the corpus structure and prepares the foundation for predictive text modeling using n-gram language models.

This report presents an exploratory analysis of the Iris dataset, a classic dataset widely used for machine learning and statistical modeling. The goal of this project is to demonstrate familiarity with data handling, visualization, and planning for predictive modeling. Key highlights of this report: Data Overview: Summary of the Iris dataset, including structure and basic statistics. Visual Insights: Bar charts and boxplots illustrating species distribution and feature variability. Interesting Findings: Observations on how sepal and petal measurements differ across species. Future Plans: Outline for building a Random Forest-based prediction algorithm and developing an interactive Shiny app for real-time species prediction. This document serves as a progress checkpoint and invites feedback on the proposed modeling approach and app design.