Plotting your data

From the previous exercise we know that the ratio of fraud to non-fraud observations is very low. You can do something about that, for example by re-sampling our data, which is explained in the next video.

In this exercise, you'll look at the data and visualize the fraud to non-fraud ratio. It is always a good starting point in your fraud analysis, to look at your data first, before you make any changes to it.

Moreover, when talking to your colleagues, a picture often makes it very clear that we're dealing with heavily imbalanced data. Let's create a plot to visualize the ratio fraud to non-fraud data points on the dataset df.

The function prep_data() is already loaded in your workspace, as well as matplotlib.pyplot as plt.

This exercise is part of the course

Fraud Detection in Python

View Course

Exercise instructions

Define the plot_data(X, y) function, that will nicely plot the given feature set X with labels y in a scatter plot. This has been done for you.
Use the function prep_data() on your dataset df to create feature set X and labels y.
Run the function plot_data() on your newly obtained X and y to visualize your results.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define a function to create a scatter plot of our data and labels
def plot_data(X, y):
	plt.scatter(X[y == 0, 0], X[y == 0, 1], label="Class #0", alpha=0.5, linewidth=0.15)
	plt.scatter(X[y == 1, 0], X[y == 1, 1], label="Class #1", alpha=0.5, linewidth=0.15, c='r')
	plt.legend()
	return plt.show()

# Create X and y from the prep_data function 
X, y = prep_data(____)

# Plot our data by running our plot data function on X and y
____(X, y)

Edit and Run Code