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Data Cleaning and Exploration with Machine Learning

Data Cleaning and Exploration with Machine Learning

By : Michael Walker
4.3 (9)
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Data Cleaning and Exploration with Machine Learning

Data Cleaning and Exploration with Machine Learning

4.3 (9)
By: Michael Walker

Overview of this book

Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results. As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book. By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.
Table of Contents (23 chapters)
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1
Section 1 – Data Cleaning and Machine Learning Algorithms
5
Section 2 – Preprocessing, Feature Selection, and Sampling
9
Section 3 – Modeling Continuous Targets with Supervised Learning
13
Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
19
Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

Summary

Logistic regression has been a go-to tool for me for many, many years when I have needed to predict a categorical target. It is an efficient algorithm with low bias. Some of its disadvantages, such as high variance and difficulty handling highly correlated predictors, can be addressed with regularization and feature selection. We went over examples of doing that in this chapter. We also examined how to handle imbalanced classes in terms of what such targets mean for modeling and interpretation of results.

In the next chapter, we will look at a very popular alternative to logistic regression for classification – decision trees. We will see that decision trees have many advantages that make them a particularly good option if we need to model complexity, without having to worry as much about how our features are specified as we do with logistic regression.

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