Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Data Science  with Python

You're reading from   Data Science with Python Combine Python with machine learning principles to discover hidden patterns in raw data

Arrow left icon
Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781838552862
Length 426 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (3):
Arrow left icon
Rohan Chopra Rohan Chopra
Author Profile Icon Rohan Chopra
Rohan Chopra
 England England
Author Profile Icon England
England
Mohamed Noordeen Alaudeen Mohamed Noordeen Alaudeen
Author Profile Icon Mohamed Noordeen Alaudeen
Mohamed Noordeen Alaudeen
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface 1. Chapter 1 FREE CHAPTER
2. Introduction to Data Science and Data Pre-Processing 3. Chapter 2
4. Data Visualization 5. Chapter 3
6. Introduction to Machine Learning via Scikit-Learn 7. Chapter 4
8. Dimensionality Reduction and Unsupervised Learning 9. Chapter 5
10. Mastering Structured Data 11. Chapter 6
12. Decoding Images 13. Chapter 7
14. Processing Human Language 15. Chapter 8
16. Tips and Tricks of the Trade Appendix

Data Cleaning

Data cleaning includes processes such as filling in missing values and handling inconsistencies. It detects corrupt data and replaces or modifies it.

Missing Values

The concept of missing values is important to understand if you want to master the skill of successful management and understanding of data. Let's take a look at the following figure:

Figure 1.14: Bank customer credit data
Figure 1.14: Bank customer credit data

As you can see, the data belongs to a bank; each row is a separate customer and each column contains their details, such as age and credit amount. There are some cells that have either NA or are just empty. This is missing data. Each piece of information about the customer is crucial for the bank. If any of the information is missing, then it will be difficult for the bank to predict the risk of providing a loan to the customer.

Handling Missing Data

Intelligent handling of missing data will result in building a robust model capable of handling...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £13.99/month. Cancel anytime
Visually different images