Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.
Table of Contents (13 chapters)

11. Machine Learning

Activity 25: Using Machine Learning to Predict Customer Return Rate Accuracy

Solution:

  1. The first step asks you to download the dataset and display the first five rows.

    Import the necessary pandas and numpy libraries to begin with:

    import pandas as pd
    import numpy as np
  2. Next, load the CHURN.csv file:
    df = pd.read_csv('CHURN.csv')
  3. Now, display the headers using .head():
    df.head()

    You should get the following output:

    Figure 11.37: Dataset displaying the data as output

  4. The next step asks you to check for NaN values. The following code reveals that there are none:
    df.info()

    You should get the following output:

    Figure 11.38: Information on the dataset

  5. The next step is done for you. The following code converts 'No' and 'Yes' into 0 and 1:
    df['Churn'] = df['Churn'].replace(to_replace=['No', 'Yes'], value=[0, 1])
  6. The next step asks you to correctly define X and y. The correct solution...