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)
Section 1 – Data Cleaning and Machine Learning Algorithms
Section 2 – Preprocessing, Feature Selection, and Sampling
Section 3 – Modeling Continuous Targets with Supervised Learning
Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

Section 1 – Data Cleaning and Machine Learning Algorithms

I try to avoid thinking about different parts of the model building process sequentially, to see myself as cleaning data, then preprocessing, and so on until I have done model validation. I do not want to think about that process as involving phases that ever end. We start with data cleaning in this section, but I hope the chapters in this section convey that we are always looking ahead, anticipating modeling challenges as we clean data; and that we also typically reflect back on the data cleaning we have done when we evaluate our models.

To some extent, the clean and dirty metaphor hides the nuance in preparing data for subsequent analysis. The real concern is how representative our instances and attributes (observations and variables) are of phenomena of interest. This can always be improved, and easily made worse without care. One thing is for certain though. There is nothing we can do in any other part of the model...