Practice project on Logistic regression, Lasso models, Decision Trees and Polynomial regression
This week we learnt about Machine Learning. specifically: Polynomial regression, Linear regression, prediction I will be putting what I learnt to test with this dataset about football predictions
I intend to find out whether football results can be predicted based on the team ranking, whether the team is playing at home or away and their previous performance
I acheived a pretty good prediction with an RSME value of over 85%. Its crazy to think that footbal results can be predicted ethically. Betting companies and people who gamble could find this useful.
- There's more information where that came from follow me for more
Here are the major tools that we used for the data analysis
- Google Colab
- Tableu Public
- Python
- Git
- Special thanks to google
- Special thanks to grepper tool
- Special thanks to Moringa School
I did this analysis with the intention of improving my skills in machine learning. However, the models used in this analysis could be used by betting companies to calculate odds and by gamblers to maximize returns on their bets placed.
Following my analysis I identified some gaps in the data and would like to continue improving the dashboard and analysis in order to come up with a more accurate prediction on footbal game results . Some of the data that would have been nice to have are:
- Better structured datasets. I had a particularly hard time merging the datasets
Similar datasets for athletes in Kenya would be interesting to work on
We would love to continue improving this analysis. Please contribute.. 😃 😃
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch
- Commit your Changes
- Push to the Branch
- Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Ian Tirok - Ian - ian.tirok@gmail.com