Predicting the Oscars with Machine Learning

I used Megafauna Software parsed data to quickly create a model using BigML: http://bml.io/W6lKqe It uses information gain mix to create a decision tree. It also concluded that winning the 'best director' is the most important field. However, 'make up' didn't top the most important fields. See the results below.

    Data distribution:
    n: 16.94% (82 instances)
    y: 83.06% (402 instances)

    Predicted distribution:
    n: 16.74% (81 instances)
    y: 83.26% (403 instances)

    Field importance:
    1. Directing_1: 84.47%
    2. Music (Scoring)_0: 3.59%
    3. Music (Scoring)_1: 2.68%
    4. Art Direction_0: 2.39%
    5. Sound_0: 1.75%
    6. Film Editing_0: 1.55%
    7. Costume Design_1: 1.40%
    8. Film Editing_1: 0.82%
    9. Costume Design_0: 0.54%
    10. Cinematography_1: 0.52%
    11. Art Direction_1: 0.29%
    12. Special Achievement Award_0: 0.00%
    13. Scientific and Technical (Bonner Medal)_1: 0.00%
    14. Foreign Language Film_0: 0.00%
    15. Sound Editing_1: 0.00%
    16. Scientific and Technical (Bonner Medal)_0: 0.00%
    17. Sound Editing_0: 0.00%
    18. Scientific and Technical (Academy Award of Merit)_1: 0.00%
    19. Sound_1: 0.00%
    20. Animated Feature Film_1: 0.00%
    21. Scientific and Technical (Academy Award of Merit)_0: 0.00%
    22. Documentary (other)_1: 0.00%
    23. Animated Feature Film_0: 0.00%
    24. Music (Song)_1: 0.00%
    25. Documentary (other)_0: 0.00%
    26. Actress -- Supporting Role_1: 0.00%
    27. Short Film (Live Action)_1: 0.00%
    28. Music (Song)_0: 0.00%
    29. Documentary (Short Subject)_1: 0.00%
    30. Actress -- Supporting Role_0: 0.00%
    31. Short Film (Live Action)_0: 0.00%
    32. Documentary (Short Subject)_0: 0.00%
    33. Actress -- Leading Role_1: 0.00%
    34. Short Film (Animated)_1: 0.00%
    35. Documentary (Feature)_1: 0.00%
    36. Short Film (Animated)_0: 0.00%
    37. Makeup_1: 0.00%
    38. Scientific and Technical (Technical Achievement Award)_1: 0.00%
    39. Actress -- Leading Role_0: 0.00%
    40. Documentary (Feature)_0: 0.00%
    41. Actor -- Supporting Role_1: 0.00%
    42. Makeup_0: 0.00%
    43. Actor -- Supporting Role_0: 0.00%
    44. Scientific and Technical (Technical Achievement Award)_0: 0.00%
    45. Jean Hersholt Humanitarian Award_1: 0.00%
    46. Directing_0: 0.00%
    47. Actor -- Leading Role_1: 0.00%
    48. Scientific and Technical (Special Awards)_1: 0.00%
    49. Jean Hersholt Humanitarian Award_0: 0.00%
    50. Actor -- Leading Role_0: 0.00%
    51. Writing_1: 0.00%
    52. Scientific and Technical (Special Awards)_0: 0.00%
    53. Irving G. Thalberg Memorial Award_1: 0.00%
    54. Acting (other)_1: 0.00%
    55. Writing_0: 0.00%
    56. Scientific and Technical (Scientific and Engineering Award)_1: 0.00%
    57. Irving G. Thalberg Memorial Award_0: 0.00%
    58. Acting (other)_0: 0.00%
    59. Visual Effects_1: 0.00%
    60. Scientific and Technical (Scientific and Engineering Award)_0: 0.00%
    61. Honorary Award_1: 0.00%
    62. Cinematography_0: 0.00%
    63. Visual Effects_0: 0.00%
    64. Scientific and Technical (Gordon E. Sawyer Award)_1: 0.00%
    65. Honorary Award_0: 0.00%
    66. Category_1: 0.00%
    67. Special Achievement Award_1: 0.00%
    68. Scientific and Technical (Gordon E. Sawyer Award)_0: 0.00%
    69. Foreign Language Film_1: 0.00%
    70. Category_0: 0.00%

It would be interesting to use some other data to predict the Oscars -- data that comes out before the Oscars begins, e.g., Golden Globe and other film festival awards, date the movie was released, box office revenue, availability on DVD, history of actors/directors/writers (e.g., have they won or been nominated for Oscars in the past).

Perhaps this illustrates how much of an effect a film's director can have on it's perception.

As Stanley Kubrick once said, "The screen is a magic medium. It has such power that it can retain interest as it conveys emotions and moods that no other art form can hope to tackle."