Example of Supervised Machine Learning with Support Vector Classifier from sklearn.

linear_svc_sklearn.py

from sklearn.svm import SVC
from graph import points, labels

# Create SVC classifier (linear decision boundary)
classifier = SVC(kernel = 'linear')

# Training with points and labels
classifier.fit(points, labels)

# Print the prediction 
print(classifier.predict([[3,4],[6,7]]))

non_linear_svc_sklearn.py

import codecademylib3_seaborn
from sklearn.svm import SVC
from graph import points, labels
from sklearn.model_selection import train_test_split

training_data, validation_data, training_labels, validation_labels = train_test_split(points, labels, train_size = 0.8, test_size = 0.2, random_state = 100)

# Create SVC classifier (non linear decision boundary)
classifier = SVC(kernel = 'poly', degree = 2)
classifier.fit(training_data, training_labels)

# Always important to validate the model withh thhe .score() method
print(classifier.score(validation_data, validation_labels))

rbf_svc_sklearn.py

from data import points, labels
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

training_data, validation_data, training_labels, validation_labels = train_test_split(points, labels, train_size = 0.8, test_size = 0.2, random_state = 100)

# Create SVC classifier (Radial Bias Function)
# An rbf kernel has a gamma parameter. If gamma is large, the training data is more relevant, and as a result overfitting can occur.
classifier = SVC(kernel = "rbf", gamma = 0.1)
classifier.fit(training_data, training_labels)

# Test the score
print(classifier.score(validation_data, validation_labels))