GitHub: https://github.com/RobertoFalconi/GameRatingsPredictor
Brief description and useful links:
Hi everyone!
This is a project originally made by Roberto Falconi and Federico Guidi for the course "Quantitative Methods for Computer Science" and its teacher Luigi Freda, based at Sapienza - University of Rome.
The code is open source and written in Python 3.x but it's also Python 2.x backward compatible.
This project goal is to classifie each video game in the dataset by ESRB rating, to do this we used Logistic Regression, Random Forest and k-NON.
GitHub repository with full code: https://github.com/RobertoFalconi/GameRatingsPredictor
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Game Ratings Predictor - machine learning software to predict video games content rating
1. GAME RATINGS
PREDICTOR
Video games ESRB (Entertainment Software
Rating Board) predictor
Control Systems and Computer Engineering – Sapienza University of Rome
Quantitative Methods for Computer Science
By Roberto Falconi and Federico Guidi
5. Super Mario Sunshine
2002 (E)
Grand Theft Auto V
2013 (M)
The Legend of Zelda
Breath of the Wild
2017 (E10+)
Uncharted 4
2016 (T)
Roberto Falconi
Federico Guidi
12. DATASET CONFIGURATION
INCOMPLETE ELEMENTS DELETION
Name Rating
Super
Mario
E
FIFA T
Pokémon E10
Tetris NaN
Name Rating
Super
Mario
E
FIFA T
Pokémon E10
Roberto Falconi
Federico Guidi
13. DATASET CONFIGURATION
APPLYING ONE-HOT ENCODING
Name Rating
Super
Mario
E
FIFA T
Pokémon E10
Name Rating_E Rating_E10 Rating_T
Super
Mario
1 0 0
FIFA 0 0 1
Pokémon 0 1 0
Roberto Falconi
Federico Guidi
14. DATASET CONFIGURATION
TRAINING SET AND TEST SET
Name Rating_E Rating_E10 Rating_T
Pokémon 0 1 0
Name Rating_E Rating_E10 Rating_T
Super
Mario
1 0 0
FIFA 0 0 1
Name Rating_E Rating_E10 Rating_T
Super
Mario
1 0 0
FIFA 0 0 1
Pokémon 0 1 0
Roberto Falconi
Federico Guidi
33. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Elemento 1 Elemento 2 Elemento 3 Elemento 4
Logistic Regression – normalized confidence
(probability that an element belongs to a class )
E E10 T M
Misclassification on element 1
RUNNING CLASSIFICATORS
Roberto Falconi
Federico Guidi
35. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Elemento 1 Elemento 2 Elemento 3 Elemento 4
k-NN – normalized confidence
(probability that an element belongs to a class )
E E10 T M
RUNNING CLASSIFICATORS
Roberto Falconi
Federico GuidiMisclassification on element 2 and element 3