In [60]:

    

import pandas as pd
import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions
from sklearn.svm import LinearSVC

%matplotlib inline


    

In [61]:

    

dataset = pd.DataFrame([[1,1,1], [1,0,0], [0,1,0], [0,0,0]], columns=['feature1', 'feature2', 'target'])


    

In [62]:

    

dataset


    

    
Out[62]:







  

    

      

      
feature1
      
feature2
      
target
    
  
  

    

      
0
      
1
      
1
      
1
    
    

      
1
      
1
      
0
      
0
    
    

      
2
      
0
      
1
      
0
    
    

      
3
      
0
      
0
      
0
    
  

In [63]:

    

X = dataset.values[:,:2]
Y = dataset['target'].values


    

In [64]:

    

model = LinearSVC().fit(X,Y)
model


    

    
Out[64]:





LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss='squared_hinge', max_iter=1000,
     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
     verbose=0)

In [65]:

    

score = model.score(X,Y)
print('Score com dados de teste: ', score)


    

    




Score com dados de teste:  1.0

In [69]:

    

print('Valor previsto: ', model.predict([[1,1]])[0])


    

    




Valor previsto:  1

In [74]:

    

plot_decision_regions(X, Y, clf=model)
plt.xlabel('features')
plt.ylabel('target')
plt.title('AND')
plt.show()