mdancho/workshop_2018_dsgo repository overview

⁠DSGO 2018 - Machine Learning With R + H2O Workshop

Get ready to learn how to predict credit defaults with R + H2O!

⁠Program

⁠

• Data is Credit Loan Applications to a Bank.

• Objective is to assess Risk Of Default, prevent bad loans, save bank lots of $$$

• Best Kagglers got 0.80 AUC with more 100's of manhours, feature engineering, combining more data sets

• We'll get 0.74 AUC in 30 minutes of coding (+1.5 hour of explaining)

⁠Data

• Kaggle Competition: Home Credit Default Risk⁠

• Data is large (166MB unzipped, 308K rows, 122 columns)

• Will work with sampled data 20% to keep manageable

⁠Machine Learning With H2O

The goal of Machine Learning with H2O is to get you experience with:

1. The R programming language

2. h2o for machine learning

3. lime for feature explanation

4. recipes for preprocessing

⁠Becoming A Data Science Rockstar

⁠

• This 3 hour workshop will teach you some of the latest tools & techniques for Machine Learning in business

• With this said, you will spend 5% of your time on modeling (machine learning) & 95% of your time:

  • Managing projects
  • Collecting & working with data (manipulating, combining, cleaning)
  • Visualizing information - showing the size of problems and what is likely contributing
  • Communicating results in terms the business cares about
  • Recommending actions that improve the business

• Further, your organization will be keenly aware of what you contribute financially. You need to show them Return on Investment (ROI). They are making an investment in having a data science team. They expect tangible results.

• Important Actions:

  • Attend my talk on the Business Science Problem Framework⁠ tomorrow. The BSPF is the essential system that enables driving ROI with data science.

  • Take my DS4B 201-R course⁠. This teaches you a 10-Week Program that has cut data science projects in half for consultants and has progressed data scientists more than any other course they've take. You will get 20% OFF (expires after DSGO conference).

⁠Installation Instructions

⁠Option 1: RStudio IDE Desktop + Install R Packages

⁠Step 1: Install R and RStudio IDE

• Download and Install R⁠

• Download RStudio IDE Desktop⁠

⁠Step 2: Open Rstudio and run the following scripts

pkgs <- c("h2o", "tidyverse", "rsample", "recipes", "lime")
install.packages(pkgs)

Test H2O - You may need the Java Developer Kit⁠

library(h2o)
h2o.init()

If H2O cannot connect, you probably need to install Java.

⁠Step 3: Load the Project From GitHub

Wait for instructions from Matt.

The URL for the GitHub project is:

https://github.com/business-science/workshop_2018_dsgo⁠

⁠Option 2: If You Have Docker Installed

⁠Step 0: Docker Installation (Takes Time)

Skip this step if you already have Docker Community Edition installed

Docker Community Edition Installation Instructions⁠

⁠Step 1: Run the DSGO Workshop Docker Image

In a terminal / command line, run the following command to download and install the workshop container. This will take a few minutes to load.

docker run -d -p 8787:8787 -v "`pwd`":/home/rstudio/working -e PASSWORD=rstudio -e ROOT=TRUE mdancho/workshop_2018_dsgo

⁠Step 3: Fire Up RStudio IDE in your Browser

Go into you favorite browser (I'll be using Chrome), and enter the following in the web address field.

localhost:8787

⁠Step 4: Log into RStudio Server

⁠

Use the following credentials.

• User Name: rstudio
• Password: rstudio

⁠Step 5: Load the Project From GitHub

Wait for instructions from Matt.

The URL for the GitHub project is:

https://github.com/business-science/workshop_2018_dsgo⁠

⁠Further Resources

• tidyverse: A meta-package for data wrangling and visualization. Loads dplyr, ggplot2, and a number of essential packages for working with data. Documentation: https://www.tidyverse.org/⁠

• recipes: A preprocessing package that includes many standard preprocessing steps. Documentation: https://tidymodels.github.io/recipes/⁠

• h2o: A high-performance machine learning library that is scalable and is optimized for perfromance. Documentation: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html⁠

  • GLM: Elastic Net (Generalized Linear Regression with L1 + L2 Regularization)

  • GBM: Gradient Boosted Machines (Tree-Based + Boosting)

  • Random Forest: Tree Based + Bagging

  • Deep Learning: Neural Network

  • Automated Machine Learning: Stacked Ensemble, All Models and Best of Family

• lime: A package for explaining black-box models. LIME Tutorial: https://www.business-science.io/business/2018/06/25/lime-local-feature-interpretation.html⁠