Kaggle competition: Santander Customer Transaction Prediction
X['answered_correctly_user'].fillna(0.65, inplace=True)
 X['explanation_mean_user'].fillna(prior_mean_user, inplace=True)
 X['quest_pct'].fillna(content_mean, inplace=True)
 X['part'].fillna(4, inplace = True)
 X['avg_questions_seen'].fillna(1, inplace = True)
 X['prior_question_elapsed_time'].fillna(elapsed_mean, inplace = True)
 X['prior_question_had_explanation_enc'].fillna(0, inplace = True)
 X['part_1'].fillna(0, inplace = True)
 X['part_2'].fillna(0, inplace = True)
 X['part_3'].fillna(0, inplace = True)
 X['part_4'].fillna(0, inplace = True)
 X['part_5'].fillna(0, inplace = True)
 X['part_6'].fillna(0, inplace = True)
 X['part_7'].fillna(0, inplace = True)
 X['type_of_concept'].fillna(0, inplace = True)
 X['type_of_intention'].fillna(0, inplace = True)
 X['type_of_solving_question'].fillna(0, inplace = True)
 X['type_of_starter'].fillna(0, inplace = True)
 X['part_1_boolean'].fillna(0, inplace = True)
 X['part_2_boolean'].fillna(0, inplace = True)
 X['part_3_boolean'].fillna(0, inplace = True)
 X['part_4_boolean'].fillna(0, inplace = True)
 X['part_5_boolean'].fillna(0, inplace = True)
 X['part_6_boolean'].fillna(0, inplace = True)
 X['part_7_boolean'].fillna(0, inplace = True)
 X['type_of_concept_boolean'].fillna(0, inplace = True)
 X['type_of_intention_boolean'].fillna(0, inplace = True)
 X['type_of_solving_question_boolean'].fillna(0, inplace = True)
 X['type_of_starter_boolean'].fillna(0, inplace = True )<data_type_conversions>
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/s6v1-ole-morten-light-gbm-with-data-augment/s6-v1.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} )
X_val['answered_correctly_user'].fillna(0.65, inplace=True)
 X_val['explanation_mean_user'].fillna(prior_mean_user, inplace=True)
 X_val['quest_pct'].fillna(content_mean, inplace=True)
 X_val['part'].fillna(4, inplace = True)
 X_val['avg_questions_seen'].fillna(1, inplace = True)
 X_val['prior_question_elapsed_time'].fillna(elapsed_mean, inplace = True)
 X_val['prior_question_had_explanation_enc'].fillna(0, inplace = True)
 X_val['part_1'].fillna(0, inplace = True)
 X_val['part_2'].fillna(0, inplace = True)
 X_val['part_3'].fillna(0, inplace = True)
 X_val['part_4'].fillna(0, inplace = True)
 X_val['part_5'].fillna(0, inplace = True)
 X_val['part_6'].fillna(0, inplace = True)
 X_val['part_7'].fillna(0, inplace = True)
 X_val['type_of_concept'].fillna(0, inplace = True)
 X_val['type_of_intention'].fillna(0, inplace = True)
 X_val['type_of_solving_question'].fillna(0, inplace = True)
 X_val['type_of_starter'].fillna(0, inplace = True)
 X_val['part_1_boolean'].fillna(0, inplace = True)
 X_val['part_2_boolean'].fillna(0, inplace = True)
 X_val['part_3_boolean'].fillna(0, inplace = True)
 X_val['part_4_boolean'].fillna(0, inplace = True)
 X_val['part_5_boolean'].fillna(0, inplace = True)
 X_val['part_6_boolean'].fillna(0, inplace = True)
 X_val['part_7_boolean'].fillna(0, inplace = True)
 X_val['type_of_concept_boolean'].fillna(0, inplace = True)
 X_val['type_of_intention_boolean'].fillna(0, inplace = True)
 X_val['type_of_solving_question_boolean'].fillna(0, inplace = True)
 X_val['type_of_starter_boolean'].fillna(0, inplace = True )<normalization>
scaler = StandardScaler()
solutions_set_scaled = pd.DataFrame(scaler.fit_transform(solutions_set),
columns = ['s1', 's2', 's3', 's4', 's5', 's6',
's7', 's8', 's9', 's10'] )
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)
 X_val = scaler.transform(X_val)
 X_train = X.reshape(X.shape[0], X.shape[1], 1)
 X_test = X_val.reshape(X_val.shape[0], X_val.shape[1], 1 )<import_modules>
pca = PCA(n_components = 1)
 factor = pca.fit_transform(solutions_set_scaled)
 print(pca.explained_variance_ratio_ )
<choose_model_class><EOS> 
submission['target'] = solutions_set_scaled['factor']
filename="blended_submission_{:%Y-%m-%d_%H_%M}.csv".format(datetime.now())
 submission.to_csv(filename, index=False )