@@ -0,0 +1,617 @@
+import pandas as pd%0A%0AX_train = pd.read_csv('%5Byour file path%5D/X_train.csv')%0AY_train = pd.read_csv('%5Byour file path%5D/Y_train.csv')%0A%0AX_test = pd.read_csv('%5Byour file path%5D/X_test.csv')%0AY_test = pd.read_csv('%5Byour file path%5D/Y_test.csv')%0A%0A%0Aprint (X_train.head())%0Aprint%0Aprint (X_test.head())%0A%0A%0A# check data types%0AX_train.dtypes%0A%0A%0A%25matplotlib inline%0A%0A# Apply Feature Scaling on continuous variables so that they can be compared on the same ground%0Aimport matplotlib.pyplot as plt%0A%0Ap = X_train%5BX_train.dtypes%5B(X_train.dtypes==%22float64%22)%7C(X_train.dtypes==%22int64%22)%5D%0A                        .index.values%5D.hist(figsize=%5B11,11%5D)%0A

create pandas_scikit_learn_preprocessing.py