An N-gram based Next Word Prediction app built in R and deployed with Shiny - trained on real-world English text from blogs, news, and Twitter using the Stupid Backoff algorithm.

This milestone report is part of the Johns Hopkins Data Science Capstone project in partnership with SwiftKey. The ultimate goal is to build a predictive text application - similar to the autocomplete feature on a smartphone keyboard - that suggests the next word a user is likely to type.

An interactive Shiny app to explore historical returns of DAX, SMI, CAC, and FTSE indices using the EuStockMarkets dataset (1991–1998). Select an index, adjust the date range, and view key metrics like cumulative return, volatility, and max drawdown.

This project analyzes the historical performance of Apple Inc. (AAPL) stock using data obtained from Yahoo Finance through the R programming language. The analysis focuses on two key aspects of stock performance: historical closing prices and daily returns. The historical stock price visualization helps identify long-term trends and overall growth in Apple’s market value, while the daily returns analysis highlights short-term fluctuations and volatility in stock performance. Interactive charts created using Plotly allow users to explore the data in greater detail through zooming and hovering features. The objective of this analysis is to gain insights into Apple’s stock behavior over time and understand both its long-term price movement and day-to-day return patterns.

This document features an interactive map built with the **Leaflet** package in R, showing 10 iconic world landmarks. Click any marker to learn more!