ML - Train Test Split

from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier

iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4 )

logreg = LogisticRegression()
logreg.fit(X_train,y_train)
y_pred = logreg.predict(X_test)

print('test comparison LR ', metrics.accuracy_score(y_test,y_pred))

knn = KNeighborsClassifier()
knn.fit(X_train,y_train)
k_pred = knn.predict(X_test)
print('test comparison KNN ', metrics.accuracy_score(y_test,k_pred) )

k_range = range(1,26)
scores  = []
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors= k)
    knn.fit(X_train, y_train)
    k_pred =knn.predict(X_test)
    scores.append(metrics.accuracy_score(y_test, k_pred) )
    
print(scores)

import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(k_range, scores)
plt.xlabel('value for K')
plt.ylabel('metric scores')