book

Data Cleaning and Exploration with Machine Learning

Name: Data Cleaning and Exploration with Machine Learning
Author: Michael Walker
ISBN: 9781803241678

by Michael Walker

August 2022

Beginner to intermediate

542 pages

11h 3m

English

Packt Publishing

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Data Cleaning and Exploration with Machine Learning
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Section 1 – Data Cleaning and Machine Learning Algorithms
Chapter 1: Examining the Distribution of Features and Targets
Technical requirementsSubsetting dataGenerating frequencies for categorical featuresGenerating summary statistics for continuous and discrete featuresIdentifying extreme values and outliers in univariate analysisUsing histograms, boxplots, and violin plots to examine the distribution of featuresUsing histogramsUsing boxplotsUsing violin plotsSummary
Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets
Technical requirementsIdentifying outliers and extreme values in bivariate relationshipsUsing scatter plots to view bivariate relationships between continuous featuresUsing grouped boxplots to view bivariate relationships between continuous and categorical featuresUsing linear regression to identify data points with significant influenceUsing K-nearest neighbors to find outliersUsing Isolation Forest to find outliersSummary
Chapter 3: Identifying and Fixing Missing Values
Technical requirementsIdentifying missing valuesCleaning missing valuesImputing values with regressionUsing KNN imputationUsing random forest for imputationSummary
Section 2 – Preprocessing, Feature Selection, and Sampling
Chapter 4: Encoding, Transforming, and Scaling Features
Technical requirementsCreating training datasets and avoiding data leakageRemoving redundant or unhelpful featuresEncoding categorical featuresOne-hot encodingOrdinal encoding Encoding categorical features with medium or high cardinalityFeature hashingUsing mathematical transformationsFeature binningEqual-width and equal-frequency binningK-means binningFeature scalingSummary
Chapter 5: Feature Selection
Technical requirementsSelecting features for classification modelsMutual information classification for feature selection with a categorical targetANOVA F-value for feature selection with a categorical targetSelecting features for regression modelsF-tests for feature selection with a continuous targetMutual information for feature selection with a continuous targetUsing forward and backward feature selectionUsing forward feature selectionUsing backward feature selectionUsing exhaustive feature selectionEliminating features recursively in a regression modelEliminating features recursively in a classification modelUsing Boruta for feature selectionUsing regularization and other embedded methodsUsing L1 regularizationUsing a random forest classifierUsing principal component analysisSummary
Chapter 6: Preparing for Model Evaluation
Technical requirementsMeasuring accuracy, sensitivity, specificity, and precision for binary classificationExamining CAP, ROC, and precision-sensitivity curves for binary classificationConstructing CAP curvesPlotting a receiver operating characteristic (ROC) curvePlotting precision-sensitivity curvesEvaluating multiclass modelsEvaluating regression modelsUsing K-fold cross-validationPreprocessing data with pipelinesSummary

Section 3 – Modeling Continuous Targets with Supervised Learning
Chapter 7: Linear Regression Models
Technical requirementsKey conceptsKey assumptions of linear regression modelsLinear regression and ordinary least squaresLinear regression and gradient descentUsing classical linear regressionPre-processing the data for our regression modelRunning and evaluating our linear modelImproving our model evaluationUsing lasso regressionTuning hyperparameters with grid searchesUsing non-linear regressionRegression with gradient descentSummary
Chapter 8: Support Vector Regression
Technical requirementsKey concepts of SVRNonlinear SVR and the kernel trickSVR with a linear modelUsing kernels for nonlinear SVRSummary
Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression
Technical requirementsKey concepts for K-nearest neighbors regressionK-nearest neighbors regressionKey concepts for decision tree and random forest regressionUsing random forest regressionDecision tree and random forest regressionA decision tree example with interpretationBuilding and interpreting our actual modelRandom forest regressionUsing gradient boosted regressionSummary
Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
Chapter 10: Logistic Regression
Technical requirementsKey concepts of logistic regressionLogistic regression extensionsBinary classification with logistic regressionEvaluating a logistic regression modelRegularization with logistic regressionMultinomial logistic regressionSummary
Chapter 11: Decision Trees and Random Forest Classification
Technical requirementsKey conceptsUsing random forest for classificationUsing gradient-boosted decision treesDecision tree modelsImplementing random forestImplementing gradient boostingSummary
Chapter 12: K-Nearest Neighbors for Classification
Technical requirementsKey concepts of KNNKNN for binary classificationKNN for multiclass classificationKNN for letter recognitionSummary
Chapter 13: Support Vector Machine Classification
Technical requirementsKey concepts for SVCNonlinear SVM and the kernel trickMulticlass classification with SVCLinear SVC modelsNonlinear SVM classification modelsSVMs for multiclass classificationSummary
Chapter 14: Naïve Bayes Classification
Technical requirementsKey conceptsNaïve Bayes classification modelsNaïve Bayes for text classificationSummary
Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning
Chapter 15: Principal Component Analysis
Technical requirementsKey concepts of PCAFeature extraction with PCAUsing kernels with PCASummary
Chapter 16: K-Means and DBSCAN Clustering
Technical requirementsThe key concepts of k-means and DBSCAN clusteringImplementing k-means clusteringImplementing DBSCAN clusteringSummary
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Overview

Take your data preparation skills to the next level with "Data Cleaning and Exploration with Machine Learning". In this book, you'll learn essential techniques to prepare and clean data effectively for machine learning workflows, improving your ability to derive insights and build accurate models. Become proficient in handling real-world data challenges using practical ML approaches.

What this Book will help me do

Master essential data preparation and cleaning techniques for machine learning.
Understand interactions between variables through bivariate and multivariate analysis.
Develop regression and classification models for both supervised and unsupervised machine learning tasks.
Gain hands-on experience interpreting and tuning models for better predictions.
Leverage algorithms like PCA and k-means clustering for advanced data diagnostics.

Author(s)

Michael Walker is a seasoned data scientist with over 10 years of experience in machine learning, data analysis, and statistical modeling. Having worked extensively across various industries, Michael brings a wealth of practical knowledge to his books. He is known for his approachable and structured teaching style, focusing on making complex topics accessible and actionable.

Who is it for?

This book is ideal for data scientists in the early stages of their careers and analysts transitioning into machine learning. If you have foundational knowledge of statistics and some programming experience, this book will help you solidify your understanding while introducing you to advanced data cleaning and preparation techniques. It's a must-have for professionals aiming to enhance their data wrangling skills and improve analysis accuracy.

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