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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

Linear SVC models

We can often get good results by using a linear SVC model. When we have more than two features, there is no easy way to visualize whether our data is linearly separable or not. We often decide on linear or nonlinear based on hyperparameter tuning. For this section, we will assume we can get good performance with a linear model and soft margins.

We will work with data on National Basketball Association (NBA) games in this section. The dataset has statistics from each NBA game from the 2017/2018 season through the 2020/2021 season. This includes the home team, whether the home team won, the visiting team, shooting percentages for visiting and home teams, turnovers, rebounds, and assists by both teams, and a number of other measures.

Note

NBA game data is available for download for the public at https://www.kaggle.com/datasets/wyattowalsh/basketball. This dataset has game data starting with the 1946/1947 NBA season. It uses nba_api to pull stats from nba.com...

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