Machine Learning Project - R

Hello, I am looking for someone with experience in machine learning techniques and R whom can do an analysis on a dataset; this will be a classification problem and will need supervised learning techniques applied. The dataset will include around 2 million rows of data and 60 independent variables. Within the most current version of R, walk through your process: 1 - Loading the data into R 2 - Applying linear and/or logistic regression against the data with residual plot/analysis, and some other techniques that you are familiar with that will help determine statistically significant variables; provide your reasoning behind which variables are significant and on the residual plot analysis 3 - Building 4 to 5 models using various techniques, i.e. decision trees, naive bayes, random forest, gradient boosted trees and another technique or two that may be of value and apply 10 fold cross validation 4 - Build a summary chart that shows each model explored along with is accuracy %, AUC, Class Recall % (True N and True Y), Class Precision (Pred N and Pred Y); this chart should then allow one to compare the different models performance and choose the appropriate model. 5 - Provide your thoughts and some next steps to improve any of the above models and perform those steps in R and repeat step 4 Since I am trying to learn how to do this on my own, I am looking for good R programming (nothing extremely advanced) along with good comments. The final deliverable is an R script and a word document providing all of the above. I am looking for a fairly quick turnaround within a few days (at most). If you have any questions along the way, please don't hesitate to ask. Thank you