Summary

Completed

In this module, you learned how to:

• Create a notebook in Azure Notebooks
• Import data into a notebook using curl
• Use Pandas to clean and prepare data
• Use scikit-learn to build a machine-learning model
• Use Matplotlib to visualize the results

Pandas, scikit-learn, and Matplotlib are among the most popular Python libraries on the planet. With them, you can prepare data for use in machine learning, build sophisticated machine-learning models from the data, and chart the output. Jupyter notebooks provide a ready-made environment for using these libraries, and Azure Notebooks give you easy access to Jupyter notebooks without requiring you to install any software or set up a Jupyter environment on a server.

Check your knowledge

1.

What method is used in Pandas to import a CSV file? Assume pandas was loaded as import pandas as pd.

pd.load_file('file.csv')

pd.read_file('file.csv')

pd.read_csv('file.csv')

2.

What is the purpose of splitting data before training a machine learning model?

To not give the model too much data.

One part can be used for training and the other can be used to evaluate the model's performance.

The model doesn't need much data.

3.

Assuming you've imported Matplotlib as 'plt', what is the method on Matplotlib that you would use to plot a bar chart?

plt.bar(labels, values)

plt.chart(labels, values)

plt.barchart(labels, values)

You must answer all questions before checking your work.