Data Preprocessing and Normalization code example
Example 1: feature scaling in python
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
from sklearn.linear_model import Ridge
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data,
random_state = 0)
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
Example 2: The function scale provides a quick and easy way to perform
from sklearn import preprocessing
import numpy as np
X_train = np.array([[1., -1., 2.],
[2., 0., 0.],
[0., 1., -1.]])
X_scaled = preprocessing.scale(X_train)
X_scaled
X_scaled.mea(axis=0)
X_scaled.std(axis=0)
scaler = preprocessing.StandardScaler().fit(X_train)
scaler
scaler.mean_
scaler.scale_
scaler.transform(X_train)
array([[ 0. ..., -1.22..., 1.33...],
[ 1.22..., 0. ..., -0.26...],
[-1.22..., 1.22..., -1.06...]])
X_test = [[-1., 1., 0.]]
scaler.transform(X_test)