meta = pd.read_csv('/home/bigpandas/nba_meta.csv')
series = pd.read_csv('/home/bigpandas/nba_series.csv')
nba_df = ingames.merge(lastmeeting,
on='GAME_ID').merge(meta,
on='GAME_ID').merge(series,
on='GAME_ID')
nbadict = process_data(nba_df)
model_training_input = np.array(nbadict['model_input'])[:30076]
model_testing_input = np.array(nbadict['model_input'])[30076:]
nbadict['model_input']
model_training_output = np.array(nbadict['labels'])[:30076]
model_testing_output = np.array(nbadict['labels'])[30076:]
grant_model_test = Builder.BuildNeuralNet(model_training_input,
model_training_output)
# In[3]:
nba_scrape = NBAScraper
nbadict = process_data(nba_df)
model_training_input = np.array(nbadict['model_input'])[:30076]
model_testing_input = np.array(nbadict['model_input'])[30076:]
model_training_output = np.array(nbadict['labels'])[:30076]
model_testing_output = np.array(nbadict['labels'])[30076:]
grant_model_test.evaluate(model_testing_input, model_testing_output)
# In[4]:
Builder.Predict(model_testing_input[4], grant_model_test)
