reference links:

• linear regression tutorial: https://www.youtube.com/watch?v=O2Cw82YR5Bo&list=WL&index=22
• webscraping tutorial: https://www.youtube.com/watch?v=8dTpNajxaH0&list=WL&index=25
• UFC stats: http://ufcstats.com/statistics/events/completed

Description: Followed a tutorial on python's scikit-learn's regression machine learning model and used the knowledge to build this program. Building this simple machine learning model taught me various skills and deepened my knowledge in machine learning as well as taught me more about python's extensive libraries.

Libraries:

• Pandas
  • utilized panda's dataframes and unique built-in functions to clean and sort data
  • added additional columns and divided columns for cleaner data structure
• Beautiful Soup
  • Used beautiful soup to scrape static data off of the UFC stats site
• SciKit-Learn
  • fed the machine learning model data following a 70:30 ratio for unkowns and knowns
  • tested the resulting data for accuracy
  • constructed a simple regression machine learning model
• matplotlib/seaborn
  • constructed several graphs to visualize data outputs clearly
  • used a legend, multiple colors and different graphs(scatter plots, double bar graphs)
  • In some cases, used seaborn as an alternative for graph creation when matplotlib was insufficient