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Applied Supervised Learning with Python

You're reading from Applied Supervised Learning with Python Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Product type Paperback

Published in Apr 2019

Publisher

ISBN-13 9781789954920

Length 404 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Benjamin Johnston

Ishita Mathur

View More author details

Table of Contents (9) Chapters

Applied Supervised Learning with Python

Preface

1. Python Machine Learning Toolkit FREE CHAPTER

2. Exploratory Data Analysis and Visualization

3. Regression Analysis

4. Classification

5. Ensemble Modeling

6. Model Evaluation

Appendix

Chapter 2: Exploratory Data Analysis and Visualization

Activity 2: Summary Statistics and Missing Values

Solution

The steps to complete this activity are as follows:

Read the data. Use pandas' .read_csv method to read the CSV file into a pandas DataFrame:
```
data = pd.read_csv('house_prices.csv')
```
Use pandas' .info() and .describe() methods to view the summary statistics of the dataset:
```
data.info()
data.describe().T
```
The output of info() will be:
Figure 2.39: The output of the info() method
The output of describe() will be:
Figure 2.40: The output of the describe() method
Find the total count and total percentage of missing values in each column of the DataFrame and display them for columns having at least one null value, in descending order of missing percentages.
As we did in Exercise 12: Visualizing Missing Values, we will use the .isnull() function on the DataFrame to get a mask, find the count of null values in each column by using the .sum() function over the mask DataFrame and the fraction of null...

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Authors (2)

Benjamin Johnston

Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in ML, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds a first-class honors bachelor's degree in both engineering and medical science from the University of Sydney, Australia.

See other products by Benjamin Johnston

Ishita Mathur

Ishita Mathur

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.

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