talhaahussain/ufc-outcome-analysis

Continuous Assessment for ECM3420 - Learning From Data, set by Dr. Chico Camargo, Dr Diogo Pacheco and Dr Marcos Oliveira (Year 3, Semester 1). Involves the use of machine learning methods to explore the best predictors for outcomes of UFC fights, based on historical data.

UFC-outcome-analysis

Continuous Assessment for ECM3420 - Learning From Data, set by Dr. Chico Camargo, Dr. Diogo Pacheco and Dr. Marcos Oliveira (Year 3, Semester 1). Involves the use of machine learning methods, specifically a multi-layer perceptron (MLP), to explore which is the best predictor of the outcome of a UFC fight - the fighters' physical metrics or their historical data.

This work received a final mark of 75/100.

Please see specification.md for specification. (Unfortunately, original specification does not exist; this is a replica.)

Prerequisites

pandas, numpy and sklearn are required to run src/physical-fp.py and src/historical-fp.py. These can be installed with:

pip install -r requirements.txt

Usage

Please run Python source files with

python physical-fp.py

and

python historical-fp.py

Results are printed to stdout, and can be redirected to a file if you wish.

Results

Please see doc/report.pdf and doc/slides.pdf for results. A YouTube video discussing the results is also available; please click here for the link.

Footnote

This research makes use of a dataset that has not been included due to size limitations. The dataset can be accessed here. All credits go to their respective owners.