Milestone 4: Dockerfile and Final wrap-up

Milestone4's release consist of the following key updates:

1. Create a dependency diagram of the Makefile and display on README.md
2. Create a Dockerfile that includes all the software and libraries/packages needed to run your analysis pipeline
3. Set up workflow on GitHub Actions to automate the building of your Docker image from your Dockerfile and publish the image on DockerHub. Adopting GitHub Action workflow from Tiffiany
4. Include proper instructions on the project README.md on how to use the project with and without Docker
5. Reply follow up and incorporate suggestions per TA's feedback and peer review to improve the overall project and reports

v0.5.0

Changes in this Release

• Rebuilt Docker image and published to DockerHub with new tag

v0.4.0

Changes in this Release

• Rebuilt Docker image and published to DockerHub with new tag

v0.3.0

Changes in this Release

• Rebuilt Docker image and published to DockerHub with new tag

Third Milestone: Makefile

Milestone3's release:

• Making a driver script named Makefile to automate the analysis pipeline

Second Milestone: Project Analysis and Results

Milestone2's release consist of the following key updates:

1. a script that reads the downloaded data and
2. a script that performs and data cleaning/pre-processing, and splitting of train and test set
3. a script which creates exploratory data visualizations for reader/consumer to understand that data set
4. a script that performs some statistical or machine learning analysis
5. a script that summarizes the results as a figure(s) and a table(s)
6. a literate code document which presents your analysis and findings in both Rmarkdown and Markdown form (ie. reports.Rmd and reports.md)

First Milestone: Project set-up and EDA

• Set up startup files and a directory structure
• Identify data set and research question
• Write flexible script to download data set
• Conduct exploratory data analysis in literate code document