Machine learning in coding (Python): stitching raw data; generating high-level features

Stitching raw DATA:

Train_data = pd.read_csv (' train.csv ') Test_data = pd.read_csv (' test.csv ') All_data = Np.vstack ((train_data.ix[:,1:-1], TEST_DATA.IX[:,1:-1]))

Merge array Vstack and Hstack functions under NumPy:

>>> a = Np.ones ((2,2)) >>> B = Np.eye (2) >>> print Np.vstack ((A, b)) [[1.  1.] [1.  1.] [1.  0.] [0.  1.]]>>> Print Np.hstack ((A, b)) [[1.  1.  1.  0.] [1.  1.  0.  1.]

Generate a high (2) secondary feature:

def group_data (data, degree=2, Hash=hash):    new_data = []    m,n = Data.shape for    indicies in combinations (range (n), degree):            new_data.append ([Hash (tuple (v)) for V in Data[:,indicies]])    return Array (new_data). T

Before generating a high-level feature, do a "labelencoder" Operation ....

From Kaggle

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Machine learning in coding (Python): stitching raw data; generating high-level features