Guess My Next Word application developed in R and deployed using Shiny package is a web application that uses a Natural Language Processing model of N-grams to predict the next word in a sentence. This prediction is based on a single corpus constructed from three sources of text namely, Twitter, News, and Blogs.

Guess My Next Word application developed in R and deployed using Shiny package is a web application that uses a Natural Language Processing model of N-grams to predict the next word in a sentence. This prediction is based on a single corpus constructed from three sources of text namely, Twitter, News, and Blogs.

Milestone report for Coursera Data Science Capstone course.

In this study, I explore the U.S. National Oceanic and Atmospheric Administration’s storm database which tracks the characteristics of major weather events in the U.S. I perform an extensive data cleaning, collapse different categories and reduce the dimensionality of the analysis, and present my findings using dplyr, barplot, and knitr packages in R.

Using the database of tooth growth in R, I provide summary statistics of the variables and evaluate the differences between the effects of different predictor variables using t-test. The graphs are made using ggplot2 package in R and the report is produced using R Markdown and Latex.

In this study, I run a thousand simulation of exponential distribution in R and compared its mean, variance, confidence intervals, and distribution with the theoretical normal statistics and used the Q-Q plot to evaluate the CLT.

Analyzing a database of cars using correlation analysis, simple linear regression, multi-variate regression, AIC model selection algorithm, and residuals diagnostics, I addressed questions about the MPG differences in different types of cars. R base graphics was used for making the graphs and final report was produced using R Markdown and Latex.