This study aims to perform dimensionality reduction on a dataset containing physical properties of superconducting materials. Since the dataset includes 81 features, many of which are likely correlated, dimensionality reduction provides an effective way to simplify the dataset and serves as a strong starting point for further analysis. To achieve this, Principal Component Analysis will be employed. PCA is a linear technique that transforms the original variables into a smaller set of uncorrelated variables, known as principal components, while retaining as much variance as possible from the original dataset.

This slide deck presents a next-word prediction algorithm built in R using text mining techniques, integrated with a Shiny app for interactive demonstration. The presentation highlights the problem, approach, demo, and potential impact for improving typing efficiency and chatbots.

This project demonstrates a next-word prediction algorithm in a Shiny app. Users can type any phrase, and the app predicts the most likely next word. The app is accompanied by a 5-slide presentation on RPubs that explains the algorithm, usage instructions, and showcases the functionality.