You’ve now written your own K-Nearest Neighbor classifier from scratch! However, rather than writing your own classifier every time, you can use Python’s sklearn library. sklearn is a Python library specifically used for Machine Learning. It has an amazing number of features, but for now, we’re only going to investigate its K-Nearest Neighbor classifier.

There are a couple of steps we’ll need to go through in order to use the library. First, you need to create a KNeighborsClassifier object. This object takes one parameter - k. For example, the code below will create a classifier where k = 3

classifier = KNeighborsClassifier(n_neighbors = 3)

Next, we’ll need to train our classifier. The .fit() method takes two parameters. The first is a list of points, and the second is the labels associated with those points. So for our movie example, we might have something like this

training_points = [ [0.5, 0.2, 0.1], [0.9, 0.7, 0.3], [0.4, 0.5, 0.7] ] training_labels = [0, 1, 1] classifier.fit(training_points, training_labels)

Finally, after training the model, we can classify new points. The .predict() method takes a list of points that you want to classify. It returns a list of its guesses for those points.

unknown_points = [ [0.2, 0.1, 0.7], [0.4, 0.7, 0.6], [0.5, 0.8, 0.1] ] guesses = classifier.predict(unknown_points)



We’ve imported sklearn for you. Create a KNeighborsClassifier named classifier that uses k=5.


We’ve also imported some movie data. Train your classifier using movie_dataset as the training points and labels as the training labels.


Let’s classify some movies. Classify the following movies: [.45, .2, .5], [.25, .8, .9],[.1, .1, .9]. Print the classifications!

Which movies were classified as good movies and which were classified as bad movies?

Remember, those three numbers associated with a movie are the normalized budget, run time, and year of release.

Take this course for free

Mini Info Outline Icon
By signing up for Codecademy, you agree to Codecademy's Terms of Service & Privacy Policy.

Or sign up using:

Already have an account?