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Game Ratings Predictor - machine learning software to predict video games content rating
The document discusses a game ratings predictor developed by Roberto Falconi and Federico Guidi, utilizing various machine learning techniques such as logistic regression, random forest, and k-nearest neighbors (k-NN) to classify video game ratings based on a dataset. It details the dataset's configuration, including classes and preprocessing steps, as well as the performance metrics of different classifiers. The conclusion presents the effectiveness of the models in predicting the ESRB (Entertainment Software Rating Board) ratings, showcasing the accuracy of each method.
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Game Ratings Predictor - machine learning software to predict video games content rating
- 1. GAME RATINGS PREDICTOR Video gamesESRB (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
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- 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
- 6. DATASET ANALYSIS DATASET SLICE RobertoFalconi Federico Guidi
- 7. DATASET CLASSES RIPARTITION Numberof elements per class Everyone Mature Everyone 10+ Teen Roberto Falconi Federico Guidi
- 8. DATASET ANALYSIS SALES RATING’SIMPORTANCE Roberto Falconi Federico Guidi
- 9. DATASET ANALYSIS SALES RATING’SIMPORTANCE Roberto Falconi Federico Guidi
- 10. DATASET ANALYSIS SALES RATING’SIMPORTANCE Roberto Falconi Federico Guidi
- 11. SETUP UBUNTU, DEBIAN EMACOS Roberto Falconi Federico Guidi
- 12. DATASET CONFIGURATION INCOMPLETE ELEMENTSDELETION 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-HOTENCODING 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 SETAND 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
- 15. DATASET CONFIGURATION TRAINING SETAND TEST SET ▶ ▶ ▶ Roberto Falconi Federico Guidi
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- 18. LOGISTIC REGRESSION PYTHON CODE RobertoFalconi Federico Guidi
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- 21. RANDOM FOREST ▶ B RobertoFalconi Federico Guidi
- 22. RANDOM FOREST PYTHON CODE RobertoFalconi Federico Guidi
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- 28. RUNNING CLASSIFICATORS 70% 75% 80% 85% 90% E E10T M Accuracy Score / Cross-validation Logistic Regression Random Forest k-NN Roberto Falconi Federico Guidi
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- 30. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Elemento 1 Elemento2 Elemento 3 Elemento 4 Random Forest - confidence (probability that an element belongs to a class) E E10 T M RUNNING CLASSIFICATORS Roberto Falconi Federico Guidi
- 31. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Elemento 1 Elemento2 Elemento 3 Elemento 4 Random Forest – normalized confidence (probability that an element belongs to a class ) E E10 T M RUNNING CLASSIFICATORS Roberto Falconi Federico Guidi
- 32. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Elemento 1 Elemento2 Elemento 3 Elemento 4 Logistic Regression - confidence (probability that an element belongs to a class ) E E10 T M RUNNING CLASSIFICATORS Roberto Falconi Federico Guidi
- 33. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Elemento 1 Elemento2 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
- 34. 0% 10% 20% 30% 40% 50% 60% 70% 80% Elemento 1 Elemento2 Elemento 3 Elemento 4 k-NN - confidence (probability that an element belongs to a class ) E E10 T M RUNNING CLASSIFICATORS Roberto Falconi Federico Guidi
- 35. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Elemento 1 Elemento2 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
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- 38. CONCLUSION 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Random Forest LogisticRegression k-NN Final scores Roberto Falconi Federico Guidi
- 39. CONCLUSION Name Rating Madden NFLE Mafia III M No Man’s Sky T NBA 2K17 E Slice of dataset Output Roberto Falconi Federico Guidi