<commit_before>#Stat 159 Final Project- Virtual Statistical Consulting 
##Description
This is the public repository for Stat 159 consulting project for a virtual client. Our targeted client is a group of 4-year art institution administrators in California trying to compare themselves to similar schools demographically, in terms of diversity and graduation rate.
<br>

##Author
<NAME>, <NAME>, <NAME>, <NAME>
<br>

##Team Member Responsibility
<NAME>: Model Building and "model building" section of report

Timothy Park:  Shiny App and "shiny app" section; compile and generated report

<NAME>: Data cleaning and slides preparation

Yuyu Zhang: EDA analysis, all Readme files and first four sections of report 
<br>
## Steps
1. git clone the repository
2. `cd` into directory
3. run `make all` to execute all scripts and get all output

<br>
##File Structure
```
stat159_final_proj/
	README.md
	Makefile	
	LICENSE
	session-info.txt
 session-info.sh
	.gitignore
data/
		README.md
  Lasso.RData
  Lasso.txt
  OLs.RData
  OLS.txt
  PCR.RData
  PCR.txt
  PLSR.RData
  PLSR.txt
  Ridge.RData
  Ridge.txt
  client-data.csv
  correlation-matrix.RData
  eda-output-enroll.txt
  eda-output-grad.txt
  orig_data.csv
  scaled-client.csv
  train_test.RData
  
		
code/
  README.md
	 /scripts
    Lasso_regression.R
    PC_regression.R
    PLS_regression.R
    Ridge_regression.R
    data-cleaning.R
    eda-script-enroll.R
    eda-script-grad.R
    ols_regression.R
    pre_model_script.R
    session-info.R
    train_test_script.R

shiny/
		README.md
		app.R
		
report/
		README.md
		report.pdf
		report.Rnw
			sections/
				00_abstract.Rnw
				01_introduction.Rnw
				02_data.Rnw
				03_methods.Rnw
				04_results.Rnw
				05_analysis.Rnw
				06_conclusion.Rnw

slides/
		README.md
		presentation.Rmd 
		presentation.html

images/
		README.md
		eda/
			#images generated by running eda_script.R
	 Regression/
   #images generated by running different model scripts
```


<br>

Code: This directory contains all the code for data cleaning, EDA analysis and model building

Data: This directory contains the raw data, clean data, EDA statistics model building output

Images: This directory contains images generated from EDA (barplots, histogram) and model building

Shiny: This directory contains code for building the interactive shiny App

Report: This directory contains different sections of the report and the finalized report 

Slides: This directory contains the presentation slide 

<br>
##Makefile Commands
 The Makefile in this directory contains a number of phony targets which run different pieces of the data acquisition, cleaning, model fitting, analysis, app building and report. To run certain targets, simply type "make target_name" into the terminal. Here is a list of the phony target titles:
• all: runs the main aspects of our project: Exploratory data analysis, Model creation, and Report compiling/rendering  
• data: downloads the file Credit.csv to the folder data/    
• eda: performs the exploratory data analysis  
• ols: OLS regression  
• ridge: Rdige Regression  
• lasso: Lasso Regression  
• pcr: Principal Components Regression  
• plsr: Partial Least Squares Regression  
• regressions: all five regressions    <commit_msg>readme: updated file structure<commit_after>#Stat 159 Final Project- Virtual Statistical Consulting 
##Description
This is the public repository for Stat 159 consulting project for a virtual client. Our targeted client is a group of 4-year art institution administrators in California trying to compare themselves to similar schools demographically, in terms of diversity and graduation rate.
<br>

##Author
<NAME>, <NAME>, <NAME>, <NAME>
<br>

##Team Member Responsibility
<NAME>: Model Building and "model building" section of report

Timothy Park:  Shiny App and "shiny app" section. 

<NAME>: Data cleaning and slides preparation,compile report 

Yuyu Zhang: EDA analysis, all Readme files and first four sections of report 
<br>
## Steps
1. git clone the repository
2. `cd` into directory
3. run `make all` to execute all scripts and get all output

<br>
##File Structure
```
stat159_final_proj/
	README.md
	Makefile	
	LICENSE
	session-info.txt
        session-info.sh
	.gitignore
data/
  README.md
  Lasso.RData
  Lasso.txt
  OLs.RData
  OLS.txt
  PCR.RData
  PCR.txt
  PLSR.RData
  PLSR.txt
  Ridge.RData
  Ridge.txt
  client-data.csv
  correlation-matrix.RData
  eda-output-enroll.txt
  eda-output-grad.txt
  orig_data.csv
  scaled-client.csv
  train_test.RData
  
		
code/
  README.md
	 /scripts
    Lasso_regression.R
    PC_regression.R
    PLS_regression.R
    Ridge_regression.R
    data-cleaning.R
    eda-script-enroll.R
    eda-script-grad.R
    ols_regression.R
    pre_model_script.R
    session-info.R
    train_test_script.R

shiny/
    README.md
    app.R
		
report/
    README.md
    report.pdf
    report.Rnw


slides/
    README.md
    presentation.Rmd 
    presentation.html

images/
    README.md
    eda/
    #images generated by running eda_script.R
    Regression/
   #images generated by running different model scripts
```


<br>

Code: This directory contains all the code for data cleaning, EDA analysis and model building

Data: This directory contains the raw data, clean data, EDA statistics model building output

Images: This directory contains images generated from EDA (barplots, histogram) and model building

Shiny: This directory contains code for building the interactive shiny App

Report: This directory contains different sections of the report and the finalized report 

Slides: This directory contains the presentation slide 

<br>
##Makefile Commands
 The Makefile in this directory contains a number of phony targets which run different pieces of the data acquisition, cleaning, model fitting, analysis, app building and report. To run certain targets, simply type "make target_name" into the terminal. Here is a list of the phony target titles:
• all: runs the main aspects of our project: Exploratory data analysis, Model creation, and Report compiling/rendering  
• data: downloads the file Credit.csv to the folder data/    
• eda: performs the exploratory data analysis  
• ols: OLS regression  
• ridge: Rdige Regression  
• lasso: Lasso Regression  
• pcr: Principal Components Regression  
• plsr: Partial Least Squares Regression  
• regressions: all five regressions    
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