Overview and Rationale
In order to consolidate your theoretical knowledge into technique and skills with practical and applicational value, you will use the glmnet() package in R to implement LASSO function to build linear and logistic models through LASSO over values of regularization parameter lambda.
Use one of the real world example data sets from R (not previously used in the R practice assignment) or a dataset you have found, to build regularization models by using Lasso (least absolute shrinkage and selection operator) and extend Lasso model fitting to big data that cannot be loaded into memory. You will fit solution paths for linear or logistic regression models penalized by Lasso over a grid of values for the regularization parameter lambda. Use the resources in this module to guide your R code development.
Your assignment/project should have a good cover/title page, introduction of what the goals of the project and the methods you use. It also should follow APA format with at least 1000 words (excluding title page and references page) and references page. In the body of your project you should incorporate the R codes and R outputs with interpretation of your results. Be sure to show all the elements in the official hypothesis, including the null and alternative hypothesis, the critical values, calculation of the test statistics and p-values. Finally, you need to make sense of your results to make good points with proper conclusions, to show your understanding of the course material and its application to the dataset.
Graphs, figures, charts, tables are very useful to increase visual effects to impress your readers. You also should do your best to give insight and understanding to the project with a good conclusion. Please use subtitles to make your assignment more reader friendly as well.