Soccer Fixture Predictor

Machine Learning program to predict soccer game results using the football-data.co.uk dataset for training.

Below shows the final error rates for the different learning methods used after optimization:

SVM Linear	SVM RBF	Polynomial Regression	Random Forests	SVM Poly	QDA	SVM Sigmoid
42.49%	43.14%	44.21%	44.31%	46.49%	48.97%	58.7%

Read full setup, results and analysis here.

Required Libraries

This program is dependent on the python libraries: xlrd for parsing the excel data files, scikit-learn and SciPy for implementing the machine learning algorithms.

How To Run

1. Install Python

To install python, download and install a python version from here.

2. Install pip

Next, in order to download the required libraries, install pip following the directions at pypa.io.

3. Install xlrd

Once pip is installed correctly, in order to install the xlrd library simply run the following command:

pip install xlrd

4. Install scikit-learn

Next, install scikit-learn using the following command:

pip install sklearn

5. Install SciPy

Lastly, install SciPy using the following command:

pip install scipy

6. Download Files

You can download this project by using the following command:

git clone https://github.com/kvelcich/Soccer_Fixture_Predictor

7. Edit Main, and Run

To run the program, in the main directory of the project, run the following command:

python main.py

You can also dig around, editing the hyperparameters and the range of the inputs given to the algorithms in order to achieve differing results.