Walker Data Cleaning and Exploration with Machine Learning

Get to grips with machine learning techniques to achieve sparkling-clean data quickly
1. Auflage 2022
ISBN: 978-1-80324-591-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 542 Seiten

No detailed description available for "Data Cleaning and Exploration with Machine Learning".

Autoren/Hrsg.

Walker, Michael

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

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

Über Autor(innen)

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.