- Python versions 3.*.
- Python Libraries:
- Pandas.
- Scikit-learn.
- numpy.
- time.
- matplotlib.
- seaborn.
For this project, I was interested in using a dataset I collected before. This dataset is for the Saudi Professional League for he past 9 years. I wanted to make a predictor model and analyze the data to try to come with pattern and find interesting results. I wanted to:
- See how many teams played in the league in the past 9 years?
- Who was the most valuable player?
- Does having clean sheet or low goals against matter on winning the title?
- How many teams won the title?
- Does home team have bigger chance to win at home than away team?
There is one notebook file that have all the work related to the above questions. The data is not available but I showed the data frames and it have some of the data in it. Markdown cells were used to assist in walking through the thought process for individual steps.
The main findings of the code can be found here.
Credit goes to Slstat.com for the data. The data is no longer shared with public so I can't re-post it without permission (waiting on that.). Feel free to ask me anything about the code @alioh