Data Cleaning and Exploration with Machine Learning
Get to grips with machine learning techniques to achieve sparkling-clean data quickly
Published on 23. September 2024
542 pages
978-1-80324-591-1 (ISBN)
Description
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Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
Product notice
Reflowable
File size
9,17 MB
ISBN-13
978-1-80324-591-1 (9781803245911)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Michael Walker
Data Cleaning and Exploration with Machine Learning
Get to grips with machine learning techniques to achieve sparkling-clean data quickly
Book
08/2022
Packt Publishing
€51.00
Shipment within 3-4 weeks
Person
Walker Michael :
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.
Content
Table of Contents
Examining the Distribution of Features and Targets
Examining Bivariate and Multivariate Relationships between Features and Targets
Identifying and Fixing Missing Values
Encoding, Transforming, and Scaling Features
Feature Selection
Preparing for Model Evaluation
Linear Regression Models
Support Vector Regression
K-Nearest Neighbor, Decision Tree, Random Forest and Gradient Boosted Regression
Logistic Regression
Decision Trees and Random Forest Classification
K-Nearest Neighbors for Classification
Support Vector Machine Classification
Naive Bayes Classification
Principal Component Analysis
K-Means and DBSCAN Clustering
Examining the Distribution of Features and Targets
Examining Bivariate and Multivariate Relationships between Features and Targets
Identifying and Fixing Missing Values
Encoding, Transforming, and Scaling Features
Feature Selection
Preparing for Model Evaluation
Linear Regression Models
Support Vector Regression
K-Nearest Neighbor, Decision Tree, Random Forest and Gradient Boosted Regression
Logistic Regression
Decision Trees and Random Forest Classification
K-Nearest Neighbors for Classification
Support Vector Machine Classification
Naive Bayes Classification
Principal Component Analysis
K-Means and DBSCAN Clustering
