Model Training Using Unscaled Data

from sklearn.model_selection import train_test_split
# Separating features and the target 
X = df.drop('Result', axis = 1) # features in X
y = df['Result'] # targets/labels in y
print("features are in X and the target is in y now! ")
# Splitting data 
test_size=0.30; random_state=42
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=test_size,random_state=random_state)
print("train and test data sets are ready with test_size = {} and random_state = {}".format(
    test_size,random_state))