diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt
index e5e26c239..6394aab14 100644
--- a/doc/src/DimRed/DimRed.do.txt
+++ b/doc/src/DimRed/DimRed.do.txt
@@ -24,18 +24,68 @@ reduction techniques: the principal component analysis PCA, Kernel PCA, and Loca
 !bblock
 
 Before we proceed however, we will discuss how to preprocess our
-data. Till now and in connection with project 1 not met so many cases
+data. Till now and in connection with our previous examples we have not met so many cases
 where we are too sensitive to the scaling of our data. Normally the
 data may need a rescaling and/or may be sensitive to extreme
 values. Scaling the data renders our inputs much more suitable for the
-algorithms we want to emply.
+algorithms we want to employ.
 
 _Scikit-Learn_ has several functions which allow us to rescale the data, normally resulting in much better results in terms of various accuracy scores.  The _StandardScaler_ function in _Scikit-Learn_ ensures that for each feature/predictor we study the mean value is zero and the variance is zero (every column in the design/feature matrix).
-This scaling has the drawback that it does not ensure that we have a particular maximum or minumum in our data set. Another function included in _Scikit-Learn_ is the _MinMaxScaler_ which ensures that all features are exactly between $0$ and $1$. The _Normalizer_ function scale each column of the design matrix so that 
+This scaling has the drawback that it does not ensure that we have a particular maximum or minumum in our data set. Another function included in _Scikit-Learn_ is the _MinMaxScaler_ which ensures that all features are exactly between $0$ and $1$. The _Normalizer_ function scales each column of the design matrix by its Euclidean norm. 
 
 !eblock
 
 
+!split
+===== Simple preprocessing examples =====
+
+We show here how we can use a simple regression case (our nuclear binding energies discussed earlier).
+Rescaling our data with different
+
+!bc pycod
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.svm import SVC
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+
+svm = SVC(C=100)
+svm.fit(X_train, y_train)
+print("Test set accuracy: {:.2f}".format(svm.score(X_test,y_test)))
+
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+
+scaler = MinMaxScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+print("Feature min values before scaling:\n {}".format(X_train.min(axis=0)))
+print("Feature max values before scaling:\n {}".format(X_train.max(axis=0)))
+
+print("Feature min values before scaling:\n {}".format(X_train_scaled.min(axis=0)))
+print("Feature max values before scaling:\n {}".format(X_train_scaled.max(axis=0)))
+
+
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy scaled data: {:.2f}".format(svm.score(X_test_scaled,y_test)))
+
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy scaled data: {:.2f}".format(svm.score(X_test_scaled,y_test)))
+
+!ec
+
+
 
 !split
 ===== Principal Component Analysis =====