Book Image

Data Cleaning and Exploration with Machine Learning

By : Michael Walker
Book Image

Data Cleaning and Exploration with Machine Learning

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)
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

Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

The last two chapters of this book examines unsupervised learning models. These are models where there is no target to predict. Even without a target there are many insights that can be gleaned from our data. Dimension reduction with principal component analysis (PCA) allows us to capture the variance of our features with fewer components than the original number of features.

The components created with PCA can be used for visualizations, or to identify processes that are important but cannot really be captured well by each feature. PCA can also be used when we need to reduce the feature space in a supervised learning model. We will demonstrate how to create and evaluate a PCA in the next chapter.

Clustering helps us group instances by those which have more in common with each other than with those in any other group. This often reveals relationships that are not otherwise obvious. We look at...