svm.py

# from: https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/
# importing necessary libraries 
from sklearn import datasets 
from sklearn.metrics import confusion_matrix 
from sklearn.model_selection import train_test_split 

# loading the iris dataset 
iris = datasets.load_iris() 

# X -> features, y -> label 
X = iris.data 
y = iris.target 

# dividing X, y into train and test data 
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) 

# print(X_train.shape)
print(y_train.shape)
# print(X_test.shape)
print(y_test.shape)

# print(type(X_train), type(y_train))
# print(type(X_test), type(y_test))

# training a linear SVM classifier 
from sklearn.svm import SVC 
svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train) 
svm_predictions = svm_model_linear.predict(X_test) 


# model accuracy for X_test 
accuracy = svm_model_linear.score(X_test, y_test) 

# creating a confusion matrix 
cm = confusion_matrix(y_test, svm_predictions) 
print(accuracy)
print(cm)