This presentation showcases a predictive text application developed using R, Shiny, and NLP techniques. The app uses a backoff n-gram language model to predict the next word in a user’s input phrase based on statistical patterns learned from a large corpus of blogs, news articles, and tweets

This report presents an exploratory analysis of the SwiftKey English text data sets (blogs, news, and Twitter) as part of the Coursera Data Science Capstone project. It includes key summary statistics, visualizations of the most frequent words, and outlines the plan for building a natural language processing model to predict the next word based on user input. The final product will be an interactive Shiny app that demonstrates the word prediction capabilities of the model. The report is written to be accessible to both technical and non-technical audiences.

This app helps users determine their Body Mass Index based on their height and weight inputs. App Features - Accepts user input: Height (cm) and Weight (kg) - Calculates BMI reactively - Displays a message indicating health status: - Underweight - Normal - Overweight - Obese

This presentation demonstrates the use of Plotly in R Markdown to build an interactive scatter plot. The plot visualizes the relationship between weight and miles per gallon (mpg) in the mtcars dataset, with points colored by the number of cylinders.

This R Markdown project demonstrates how to create an interactive map using the Leaflet package in R. The map highlights the location of Peshawar, Pakistan, and serves as a minimal example of embedding dynamic visual content in HTML documents generated via R Markdown.

This report explores the NOAA Storm Database from 1950 to 2011 to analyze the impact of various weather events across the United States. The analysis identifies which types of events are most harmful to population health and which ones have the greatest economic consequences. Using R, the raw data is cleaned, processed, and visualized to provide key insights that could help municipal managers and government agencies prioritize resources for disaster preparedness.