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.

Preface

Who this book is for

What this book covers

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Conventions used

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Section 1 – Data Cleaning and Machine Learning Algorithms

Section 1 – Data Cleaning and Machine Learning Algorithms

Free Chapter

Chapter 1: Examining the Distribution of Features and Targets

Chapter 1: Examining the Distribution of Features and Targets

Technical requirements

Subsetting data

Generating frequencies for categorical features

Generating summary statistics for continuous and discrete features

Identifying extreme values and outliers in univariate analysis

Using histograms, boxplots, and violin plots to examine the distribution of features

Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets

Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets

Technical requirements

Identifying outliers and extreme values in bivariate relationships

Using scatter plots to view bivariate relationships between continuous features

Using grouped boxplots to view bivariate relationships between continuous and categorical features

Using linear regression to identify data points with significant influence

Using K-nearest neighbors to find outliers

Using Isolation Forest to find outliers

Chapter 3: Identifying and Fixing Missing Values

Chapter 3: Identifying and Fixing Missing Values

Technical requirements

Identifying missing values

Cleaning missing values

Imputing values with regression

Using KNN imputation

Using random forest for imputation

Section 2 – Preprocessing, Feature Selection, and Sampling

Section 2 – Preprocessing, Feature Selection, and Sampling

Chapter 4: Encoding, Transforming, and Scaling Features

Chapter 4: Encoding, Transforming, and Scaling Features

Technical requirements

Creating training datasets and avoiding data leakage

Removing redundant or unhelpful features

Encoding categorical features

Encoding categorical features with medium or high cardinality

Using mathematical transformations

Feature binning

Feature scaling

Chapter 5: Feature Selection

Chapter 5: Feature Selection

Technical requirements

Selecting features for classification models

Selecting features for regression models

Using forward and backward feature selection

Using exhaustive feature selection

Eliminating features recursively in a regression model

Eliminating features recursively in a classification model

Using Boruta for feature selection

Using regularization and other embedded methods

Using principal component analysis

Chapter 6: Preparing for Model Evaluation

Chapter 6: Preparing for Model Evaluation

Technical requirements

Measuring accuracy, sensitivity, specificity, and precision for binary classification

Examining CAP, ROC, and precision-sensitivity curves for binary classification

Evaluating multiclass models

Evaluating regression models

Using K-fold cross-validation

Preprocessing data with pipelines

Section 3 – Modeling Continuous Targets with Supervised Learning

Section 3 – Modeling Continuous Targets with Supervised Learning

Chapter 7: Linear Regression Models

Chapter 7: Linear Regression Models

Technical requirements

Linear regression and gradient descent

Using classical linear regression

Using lasso regression

Using non-linear regression

Regression with gradient descent

Chapter 8: Support Vector Regression

Chapter 8: Support Vector Regression

Technical requirements

Key concepts of SVR

SVR with a linear model

Using kernels for nonlinear SVR

Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression

Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression

Technical requirements

Key concepts for K-nearest neighbors regression

K-nearest neighbors regression

Key concepts for decision tree and random forest regression

Decision tree and random forest regression

Using gradient boosted regression

Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning

Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning

Chapter 10: Logistic Regression

Chapter 10: Logistic Regression

Technical requirements

Key concepts of logistic regression

Binary classification with logistic regression

Regularization with logistic regression

Multinomial logistic regression

Chapter 11: Decision Trees and Random Forest Classification

Chapter 11: Decision Trees and Random Forest Classification

Technical requirements

Decision tree models

Implementing random forest

Implementing gradient boosting

Chapter 12: K-Nearest Neighbors for Classification

Chapter 12: K-Nearest Neighbors for Classification

Technical requirements

Key concepts of KNN

KNN for binary classification

KNN for multiclass classification

Chapter 13: Support Vector Machine Classification

Chapter 13: Support Vector Machine Classification

Technical requirements

Key concepts for SVC

Linear SVC models

Nonlinear SVM classification models

SVMs for multiclass classification

Chapter 14: Naïve Bayes Classification

Chapter 14: Naïve Bayes Classification

Technical requirements

Naïve Bayes classification models

Naïve Bayes for text classification

Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

Chapter 15: Principal Component Analysis

Chapter 15: Principal Component Analysis

Technical requirements

Key concepts of PCA

Feature extraction with PCA

Using kernels with PCA

Chapter 16: K-Means and DBSCAN Clustering

Chapter 16: K-Means and DBSCAN Clustering

Technical requirements

The key concepts of k-means and DBSCAN clustering

Implementing k-means clustering

Implementing DBSCAN clustering

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Chapter 13: Support Vector Machine Classification

There are some similarities between support vector classification models and k-nearest neighbors models. They are both intuitive and flexible. However, support vector classification, due to the nature of the algorithm, scales better than k-nearest neighbor. Unlike logistic regression, it can handle nonlinear models rather easily. The strategies and issues with using support vector machines for classification are similar to those we discussed in Chapter 8, Support Vector Regression, when we used support vector machines for regression.

One of the key advantages of support vector classification (SVC) is the ability it gives us to reduce model complexity without increasing our feature space. But it also provides multiple levers we can adjust to limit the possibility of overfitting. We can choose a linear model or select from several nonlinear kernels. We can use a regularization parameter, much as we did for logistic regression. With...