The goal of this project/challenge is to predict the results of Cancer Mortality Rates. The website hosting the data is located at <https://data.world/nrippner/ols-regression-challenge>. These data were aggregated from a number of sources including the American Community Survey (<https://www.census.gov>), <https://www.clinicaltrials.gov>, and <https://www.cancer.gov>. Using only OLS to build the model, the final model chosen uses 6 predictors, out of 36 total, with an adjusted R-squared of 0.9624.

The goal of this analysis project is to elucidate trends in US adult's activity levels using pedometer data taken from [Coursera/Johns Hopkins]("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip","project1.zip") website compared against data published at the [National Institute of Health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2927728/). The data presented in this analysis depicts the number of steps taken in five minute intervals daily between October 1,2012 and November 30,2012.

The goal of this project is to use machine learning to predict the results of Development Therapeutics Program (DTP) AIDS Antiviral Screening. Therefore, this report documents both the predictive power of the model and all corresponding data used for the analysis in an effort to identify effective therapeutic agents for treating patients with AIDS, and/or cancer.