This is the first part of the Data Visualization in Analytics series using rCharts. It explores various plots the rCharts package has to offer along with the JavaScript libraries. We will also look into the syntax and the semantics of the plots.

This is the final presentation for the INFO 650 Data mining project. The model is run in SQL Server for 3 algorithms namely the Logistic Regression, Naive Bayes and Decision Trees. Team members are : Ramaranjan Ruj, Ruchir Rege and Sunny Shetty

Predicting which iPad on eBay are sold - Kaggle Competition (Rank: 12th/1884).

Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement, a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. Machine learning algorithms and techniques will be used to make a model that is able to predict the way exercises are being done. The success of this model also asserts the argument that trainers could be replaced by machines which can “correct” the exercising technique with more accuracy.

The analysis provides an insight to the effect of natural calamities on US public health and economy. The data is collected from various sources all accross US from 1950’s to 2011. 985 types of natural causes are incorporated in the dataset. The analysis shows that Texas state is the most impacted. Tornadoes are the main cause for deaths and injuries while hail cause the maximum damage to economy.