Kaggle competition: Santander Customer Transaction Prediction
filename = 'test_data.sav'
joblib.dump(X_test, filename )<install_modules>
s1=pd.read_csv('.. /input/s1v8-nanashi-90-lines-solution-0901-fast/s1-v8.csv')['target']
s2=pd.read_csv('.. /input/santander-magic-lgb-0-901/submission.csv')['target']
s3=pd.read_csv('.. /input/s3v32-ashish-gupta-eda-pca-scaler-lgbm/s3-v32.csv')['target']
s4=pd.read_csv('.. /input/eda-pca-simple-lgbm-on-kfold-technique/submission26.csv')['target']
s5=pd.read_csv('.. /input/lgb-2-leaves-augment/lgb_submission.csv')['target']
s6=pd.read_csv('.. /input/lightgbm-with-data-augmentation/2019-04-01_07_56_sub.csv')['target']
s7=pd.read_csv('.. /input/s7v19-subham-sharma-what-is-next-in-santander/s7-v19.csv')['target']
s8=pd.read_csv('.. /input/s8v5-joshua-reed-santander-customer-transaction/s8-v5.csv')['target']
s9=pd.read_csv('.. /input/s9v16-gauravtambi-lgbm-augmentation/s9-v16.csv')['target']
s10=pd.read_csv('.. /input/best-parameters-lb-0-900/submission.csv')['target']
submission = pd.read_csv('.. /input/santander-customer-transaction-prediction/sample_submission.csv')
 solutions_set = pd.DataFrame({'s1': s1, 's2': s2, 's3': s3, 's4': s4, 's5': s5, 's6': s6,
's7': s7, 's8': s8, 's9': s9, 's10': s10} )
!pip install lofo-importance<install_modules>
scaler = MinMaxScaler()
solutions_set_scaled = scaler.fit_transform(solutions_set)
 solutions_set_scaled = pd.DataFrame(solutions_set_scaled, columns = solutions_set.columns )
!pip install pandarallel<import_modules>
for s in solutions_set_scaled.columns:
s = solutions_set_scaled[s]
cluster1 = s7
cluster2 = 1/2*(s1 + s5 + s8 + s10)
 cluster3 = 1/4*(s2 + s3 + s4 + s6 + s9)
 submission['target'] = 0.1*cluster1 + 0.8*cluster2 + 0.1*cluster3
mean_absolute_error)
<train_model>
filename="blended_submission_{:%Y-%m-%d_%H_%M}.csv".format(datetime.now())
 submission.to_csv(filename, index=False )