MemphisMeng

# movie profile
movie_profile = movies[['id', 'title', 'genres']]
movie_profile.rename(columns={'id': 'movieId'}, inplace=True)
genres = [item.strip() for l in all_genres for item in l ]
unique_genres = set(genres)
for genre in unique_genres:
  movie_profile[genre] = 0
  
for i in range(len(movie_profile)):
  if type(movie_profile['genres'].iloc[i]) != None.__class__:

MemphisMeng

algo = SVD()
trainset = data.build_full_trainset()
algo.fit(trainset)
# Than predict ratings for all pairs (u, i) that are NOT in the training set.
testset = trainset.build_anti_testset()
predictions = algo.test(testset)

MemphisMeng

proba = {'Manchester City': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Bayern Munich': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Paris Saint-Germain': {'qtr': 300, 'semi': 0, 'final': 0, 'champion': 0},
        'Real Madrid': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Juventus': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Lyon': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Barcelona': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Napoli': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Chelsea': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Atalanta': {'qtr': 300, 'semi': 0, 'final': 0, 'champion': 0},

MemphisMeng

from sklearn import preprocessing
from imblearn.over_sampling import RandomOverSampler

le = preprocessing.LabelEncoder()
fixtures['Score'] = le.fit_transform(fixtures['Score'].astype(str))
ros = RandomOverSampler(random_state=42)
fixtures, Score_ = ros.fit_resample(fixtures.drop(columns=['Score']), fixtures['Score'])
fixtures['Score'] = le.inverse_transform(Score_)

MemphisMeng

# "fixtures" is the fixture table containing all the matches that have taken place
# "aggregate" is the set of selected features
fixtures = fixtures.merge(aggregate, left_on='Home', right_on='Squad')
fixtures = fixtures.merge(aggregate, left_on='Away', right_on='Squad', suffixes=('_Home', '_Away'))