KNN_3.py

import numpy as np 
from sklearn.cross_validation import train_test_split

## Retrieve features
X = df.values.tolist()
Y = []
## Convert classes in Strings to Integers 
for val in target:
    if(val == 'Iris-setosa'):
        Y.append(0)
    elif(val == 'Iris-virginica'):
        Y.append(2)
    else:
        Y.append(1)
## Make them as numpy array         
X = np.array(X)
Y = np.array(Y)
## Shuffle and split the data into training and test set
x_train, x_test, y_train, y_test = train_test_split(X,Y,train_size=0.9)
## Make them as numpy arrays 
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)

after the scatter plot the whole window disappears and the code exits with 1

The code did run successfully. The accuracy is printed in the output.

Okay I thought the graph that you showed in the article should also be printed.??