<commit_before># Stat159 Fall2016 Proj2

## Author
* <NAME>
* <NAME>

## Introduction
In this project, we will utilize the model selection method used in Chapter 6, Linear Model Selection and Regularization, from the book "An Introduction to Statistical Learning" by <NAME>, <NAME>, <NAME> and <NAME>.

The following five models will be applied to our dataset: Ordinary Least Squares, Ridge regression, Lasso regression, Principal Components regression and Partial Least Squares regression.

## Structure
The structure of this project is listed as following:

	stat159-fall2016-project2/
    	.gitignore
    	README.md
    	LICENSE
    	Makefile
    	session-info.txt
    	session.sh
    	code/
      		...
    	data/
      		...
      	images/
      		...
      	slides/
      		slides.html
      		slides.Rmd
    	report/
      		report.Rmd
      		report.pdf
      		sections/
        		...
        		
## Reproduce
To reproduce this project:

1. `git clone` this repo
2. `cd` into the folder `Stat159-fall2016-project2`.
3. Run `make` in the terminal to regenerate all output files, graphs and report.
4. We can also reproduce any part as you want by run `make SPECIFIC PART`. More detail about parts you can reproduce are included in `Makefile` as targets.

## License
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.       <commit_msg>readme: add code license and acknowledgement<commit_after># Stat159 Fall2016 Proj2

## Author
* <NAME>
* <NAME>

## Introduction
In this project, we will utilize the model selection method used in Chapter 6, Linear Model Selection and Regularization, from the book "An Introduction to Statistical Learning" by <NAME>, <NAME>, <NAME> and <NAME>.

The following five models will be applied to our dataset: Ordinary Least Squares, Ridge regression, Lasso regression, Principal Components regression and Partial Least Squares regression.

## Structure
The structure of this project is listed as following:

	stat159-fall2016-project2/
    	.gitignore
    	README.md
    	LICENSE
    	Makefile
    	session-info.txt
    	session.sh
    	code/
      		...
    	data/
      		...
      	images/
      		...
      	slides/
      		slides.html
      		slides.Rmd
    	report/
      		report.Rmd
      		report.pdf
      		sections/
        		...
        		
## Reproduce
To reproduce this project:

1. `git clone` this repo
2. `cd` into the folder `Stat159-fall2016-project2`.
3. Run `make` in the terminal to regenerate all output files, graphs and report.
4. We can also reproduce any part as you want by run `make SPECIFIC PART`. More detail about parts you can reproduce are included in `Makefile` as targets.

## Acknowledgement
Thanks for the intructions and examples in <a rel="book" href="http://www-bcf.usc.edu/%7Egareth/ISL/">An Introduction to Statistical Learning</a>.

## License
### Media content
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.       

### Code
All code is licensed under <a rel="license" href="https://opensource.org/licenses/MIT">The MIT License</a>.   
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