Implement the K-Nearest Neighbors Classifier

Tasks

Create a new branch in your data structures repository called knn. You’ll use this branch to do your work for this assignment. Add a new file to your data-structures repository called knn.py. In this file, implement a K-Nearest Neighbors Classifier object. Your classifier should have the following public method:

• clf.predict(self, data): returns labels for your test data.

This should be able to take in an iterable of data records (either a tuple of records, or a list of records, or Pandas DataFrame).

The classifier should take one parameter, k, with a default of 5. This will be the number of neighbors you’ll use to classify a new data point. Appropriately handle the case where k is provided with an invalid input (e.g. -1, or k > len(data))

Your implementation must include tests. The tests must run in both Python 2 and Python 3. Ensure that your tests are properly connected to Travis CI and that you have a Travis badge displayed on your README.md. Expand your README.md to include notes about your classifier. Include any external sources used in its creation.

Click here for a data file of classified flowers which you may use to test your classifier. You can either use the column named target or class_name as your class label. They each map directly to each other.

Include the time complexities for this classification algorithm’s fit and predict methods in your README.

Stretch Goal

Enable your classifier to use as many characteristics as you provide. For example, if your input data from flowers_data.csv looks like (petal_length, petal_width, sepal_length, sepal_width), your classifier should be using the total distance between points. If unclear, ask.

Submitting Your Work

When you’ve completed your work and all your tests are passing, submit a pull request from the knn branch back to master. Copy the URL for that pull request and submit it using the URL input on Canvas. Once submitted you may merge your branch back to master.

Use the comment function to submit quests, comments, and reflections on the work you’ve done.