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## You're reading fromPython Machine Learning (Wiley)

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee

1. Cover
2. Introduction
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index

# Data Cleansing

In machine learning, one of the first tasks that you need to perform is data cleansing. Very seldom would you have a dataset that you can use straightaway to train your model. Instead, you have to examine the data carefully for any missing values and either remove them or replace them with some valid values, or you have to normalize them if there are columns with wildly different values. The following sections show some of the common tasks you need to perform when cleaning

## Cleaning Rows with NaNs

Consider a CSV file named `NaNDataset.csv` with the following content:

````A,B,C`
`1,2,3`
`4,,6`
`7,,9`
`10,11,12`
`13,14,15`
`16,17,18` ```

Visually, you can spot that there are a few rows with empty fields. Specifically, the second and third rows have missing values for the second columns. For small sets of data, this is easy to spot. But if you have a large dataset, it becomes almost impossible to detect. An effective way to detect for empty rows is to load the dataset into a Pandas dataframe and...

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