training image using svm.py

import matplotlib.pyplot as plt
from sklearn import svm,metrics
import pandas as pd

df = pd.read_csv('training-test.csv')
df = df.iloc[:,1:]

train = df.sample(frac=0.9, random_state=255)
test = df.drop(train.index)

train_in = train.drop(['class'], axis=1).values
train_out = train['class'].values

test_in = test.drop(['class'], axis=1).values
test_out = test['class'].values

cls = svm.SVC(gamma=0.001)
cls.fit(train_in,train_out)


expected = test_out
predicted = cls.predict(test_in)

print("Classification report for classifier %s:\n%s\n"
      % (cls, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))

print("\nThe accuracy is : {:.2f}%".format(metrics.accuracy_score(expected,predicted)*100))