Difficulty: beginner
Estimated Time: 15 minutes

In the previous scenario, you've seen the typical Machine Learning process, from loading the data, splitting it, training and evaluation. This scenario is the continuation of the introduced concepts. This time you will try to classify the data that are not easily separable with the straight line.

Congratulations!

You've completed Logistic Regression scenario.

You've practiced the basics concepts of machine learning process and used them when trying to classify non-linearly separable examples using Logistic Regression.

Don’t stop now! The next scenario will only take about 10 minutes to complete.

Logistic Regression

Step 1 of 4

Dataset

The primary purpose of this scenario is for you to practice running the Machine Learning process with some Python code. This time we will use the data which classes cannot be easily separated with the straight line.

The following picture is visualising the data. Similarly as in the previous scenario, the (x1, x2) coefficients are the examples features, while the colour indicates the labels (red and blue).

Dataset

The code you'll be working with is in the classification.py file. We have also provided some helper functions which are available in the helper.py.

##Task 1

The first thing you need to do is to read the data. Use read_data or read_and_visualise_data and load them into data DataFrame. We're using pandas in our code, so you may find it useful to learn how does the indexing work.