Kaggle competition: NBA Rookies
test_df = pd.read_csv('.. /input/test.csv')
 print(test_df.isnull().sum())
 print('test.csv Old size: %d' % len(test_df))
test_df = test_df.dropna(how = 'any', axis = 'rows')
 print('test.csv New size: %d' % len(test_df)) 
<feature_engineering>
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Imputer
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
from statsmodels.compat import pandas as pd
import pandas as pd
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn import svm, preprocessing
from sklearn.decomposition import PCA
add_travel_vector_features(test_df)
 parse_and_extract_date_time_components(test_df)
<feature_engineering>
test = pd.read_csv(".. /input/test.csv")
 test_x = test.iloc[:, 2:].values
imp = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
 imp=imp.fit(test_x)
 test_x = imp.transform(test_x)
 dataset = pd.read_csv(".. /input/train.csv")
 print(dataset.info())
 X = dataset.iloc[:, 2:-1].values
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
 input_dim = X.shape[1]
imputer = imputer.fit(X)
 X = imputer.transform(X)
 y = dataset.iloc[:, 21].values
sc = StandardScaler()
X_train = sc.fit_transform(X)
 x_test = sc.transform(test_x)
 Xtrain = preprocessing.normalize(X_train, norm='l2')
 Xtest = preprocessing.normalize(x_test, norm='l2')
 print(X)
 print(y)
 print(test_x )
<define_variables><EOS> 
cols = { 'PlayerID': [i+901 for i in range(440)] , 'TARGET_5Yrs': [eclf1.predict([test_x[i]])[0] for i in range(440)] }
submission = pd.DataFrame(cols)
 print(submission)
 submission.to_csv("submission1.csv", index=False )