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Game Ratings Predictor - machine learning software to predict video games content rating

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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This is a project originally made by Roberto Falconi and Federico Guidi for the course "Quantitative Methods for Computer Science" and its teacher Luigi Freda, based at Sapienza - University of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

Published in: Software
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Game Ratings Predictor - machine learning software to predict video games content rating

  1. 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
  2. 2. INTRODUCTION ► ► ► Roberto Falconi Federico Guidi
  3. 3. PROCEDURE ► ► ► ► ► ► ► ► Roberto Falconi Federico Guidi
  4. 4. Roberto Falconi Federico Guidi
  5. 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. 6. DATASET ANALYSIS DATASET SLICE Roberto Falconi Federico Guidi
  7. 7. DATASET CLASSES RIPARTITION Number of elements per class Everyone Mature Everyone 10+ Teen Roberto Falconi Federico Guidi
  8. 8. DATASET ANALYSIS SALES RATING’S IMPORTANCE Roberto Falconi Federico Guidi
  9. 9. DATASET ANALYSIS SALES RATING’S IMPORTANCE Roberto Falconi Federico Guidi
  10. 10. DATASET ANALYSIS SALES RATING’S IMPORTANCE Roberto Falconi Federico Guidi
  11. 11. SETUP UBUNTU, DEBIAN E MACOS Roberto Falconi Federico Guidi
  12. 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. 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. 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
  15. 15. DATASET CONFIGURATION TRAINING SET AND TEST SET ▶ ▶ ▶ Roberto Falconi Federico Guidi
  16. 16. LOGISTIC REGRESSION Roberto Falconi Federico Guidi
  17. 17. LOGISTIC REGRESSION ▶ Roberto Falconi Federico Guidi
  18. 18. LOGISTIC REGRESSION PYTHON CODE Roberto Falconi Federico Guidi
  19. 19. LOGISTIC REGRESSION ▶ ▶ ▶ ▶ Pros ▶ ▶ ▶ ▶ Cons Roberto Falconi Federico Guidi
  20. 20. RANDOM FOREST Roberto Falconi Federico Guidi
  21. 21. RANDOM FOREST ▶ B Roberto Falconi Federico Guidi
  22. 22. RANDOM FOREST PYTHON CODE Roberto Falconi Federico Guidi
  23. 23. RANDOM FOREST ▶ ▶ ▶ ▶ ▶ ▶ ▶ Pro ▶ Contro Roberto Falconi Federico Guidi
  24. 24. K-NN Roberto Falconi Federico Guidi
  25. 25. K-NN ▶ Roberto Falconi Federico Guidi
  26. 26. K-NN PYTHON CODE Roberto Falconi Federico Guidi
  27. 27. K-NN ▶ ▶ ▶ Pro Contro ▶ ▶ ▶ ▶ ▶ Roberto Falconi Federico Guidi
  28. 28. RUNNING CLASSIFICATORS 70% 75% 80% 85% 90% E E10 T M Accuracy Score / Cross-validation Logistic Regression Random Forest k-NN Roberto Falconi Federico Guidi
  29. 29. ▶ ▶ ▶ RUNNING CLASSIFICATORS Roberto Falconi Federico Guidi
  30. 30. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Elemento 1 Elemento 2 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. 31. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Elemento 1 Elemento 2 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. 32. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Elemento 1 Elemento 2 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. 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
  34. 34. 0% 10% 20% 30% 40% 50% 60% 70% 80% Elemento 1 Elemento 2 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. 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
  36. 36. BIAS-VARIANCE TRADEOFF OBSERVATIONS Roberto Falconi Federico Guidi
  37. 37. BIAS-VARIANCE TRADEOFF OBSERVATIONS ▶ ▶ ▶ Roberto Falconi Federico Guidi
  38. 38. CONCLUSION 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Random Forest Logistic Regression k-NN Final scores Roberto Falconi Federico Guidi
  39. 39. CONCLUSION Name Rating Madden NFL E Mafia III M No Man’s Sky T NBA 2K17 E Slice of dataset Output Roberto Falconi Federico Guidi

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