Kaggle competition: NFL Big Data Bowl
<SOS> metric: CRPS Kaggle data source: nfl-big-data-bowl<install_modules>
<import_modules><EOS> 
env = nflrush.make_env()
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df = pd.read_csv('/kaggle/input/nfl-big-data-bowl-2020/train.csv', low_memory=False)
 n = len(df)
 prior = 199 * [0.]
counts = df.Yards.value_counts()
for i, ni in zip(counts.index, counts.values):
prior[i+99] = ni/n
pred = np.cumsum(prior)
 names = dict(zip(range(199), ['Yards%d' % i for i in range(-99, 100)]))
for(test_df, sample_prediction_df)in env.iter_test() :
play = test_df["PlayId"].values[0]
if play in labels:
preds = [0 if i < labels[play] else 1 for i in range(-99, 100)]
else:
preds = pred
env.predict(pd.DataFrame(preds ).transpose().rename(names, axis=1))
env.write_submission_file()