What it does - Predicts next words as you type - Offer a real-time predictions - Context-aware suggestions - Confidence visualization - One-click word insertion

This project leverages a diverse text dataset (blogs, news, and Twitter) to build a predictive text application. The project has the following steps: Step 1. First it explores and summarizes text data. (Milestone Report focus)

This project leverages a diverse text dataset (blogs, news, and Twitter) to build a predictive text application. Read the milestone report as it explores and summarizes, and clean the text data.

This SQL project explores a rich dataset of songs that includes a wide range of acoustic characteristics such as danceability, energy, valence, tempo, loudness, and instrumentalness, among others.

Sales Data Filter Shiny App is a user-friendly web application built with R and Shiny. - It enables interactive filtering and exploration of sales data. - Users can upload their own CSV files and apply various filters to analyze sales performance.

This document demonstrates several different graphics visualizations using Plotly.

Summary leaflet() to initialize the map. addTiles() to add the background tiles. addMarkers() to add points of interest. addCircles() to add visual elements.

This report describes the development of a machine learning model to classify weightlifting exercises into five categories (A, B, C, D, E) based on sensor data from accelerometers. Data Sources Training Data: pml-training.csv (19,622 observations) Test Data: pml-testing.csv (20 observations)

This report explores whether manual or automatic transmissions are associated with better fuel efficiency (measured as miles per gallon, MPG) using the mtcars dataset. We first compare MPG by transmission type using summary statistics and visualizations, followed by regression modeling to quantify the effect while adjusting for confounding variables. Results show that manual transmissions are associated with significantly higher MPG than automatic transmissions, even after adjusting for weight and horsepower. This suggests that, on average, manual cars in this dataset offer better fuel economy.

This analysis investigates the impact of severe weather events in the U.S. using the NOAA Storm Data dataset. The study identifies the event types (`EVTYPE`) most harmful to population health by measuring combined fatalities and injuries, with tornadoes and excessive heat ranking highest. Economic consequences were also evaluated by calculating property and crop damages, adjusted using exponent codes (`PROPDMGEXP`, `CROPDMGEXP`) to reflect true financial loss. Hurricanes, floods, and tornadoes were found to cause the greatest economic damage.