- Project Purpose: The goal of this project was to replicate a project I previously did in SAS for a previous class in Python. I wanted to demonstrate the ability to use many software to show adaptibility. The data I used was a previous dataset from the project. It is LA Lakers basketball data; I am their fan for sports, so I used their data. This is a project for my own prefrence.
- Tools used: I used Python with the packages of pandas, nupmy, and plotnine. The main script is used for the project is ball.py.
- Results: The conclusion of this project is simple. I was not running any analysis, just creating the graphs. The process of creating them was a bit easy after learning R. I love how Python has the plotnine feature that is like ggplot of R. In this way, as I have not made these graphs in R yet, it was learning the process for both of the coding softwares. Next steps would learn to do a regression in Python, as I have done it in SAS and R already. Special note: favorite graph would be Figure 16: Stacked Bar Chart. This one was the 'most' difficult to do as finding the code for the percentage part was looking for online.
- readme.md
- scripts
---- readme.md (short description of each script)
---- data_munge.R
---- data_munge.py
---- eda.R
---- model.py
- data (less than 100 Mb)
---- readme.md (links to data larger than 100 Mb and data details.)
---- crimes.csv
---- visits.json
- documents
---- readme.md (notes while doing your project)
---- mlmethod.pdf
---- api_guide.pdf