Econometrics and Data Science Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems
-
- Taschenbuch ausgewählt
- eBook
-
Sprache:Englisch
32,99 €
UVP
37,44 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
27.10.2021
Abbildungen
XVIII, 228 p. 107 illus.
Verlag
ApressSeitenzahl
228
Maße (L/B/H)
25,4/17,8/1,4 cm
Gewicht
474 g
Auflage
1st ed.
Sprache
Englisch
ISBN
978-1-4842-7433-0
Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis.
After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems.
What You Will Learn
- Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states
- Be familiar with practical applications of machine learning and deep learning in econometrics
- Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models
- Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models
- Represent and interpret data and models
Who This Book Is For
Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives
Noch keine Bewertungen vorhanden
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.
Kurze Frage zu unserer Seite
Vielen Dank für dein Feedback
Wir nutzen dein Feedback, um unsere Produktseiten zu verbessern. Bitte habe Verständnis, dass wir dir keine Rückmeldung geben können. Falls du Kontakt mit uns aufnehmen möchtest, kannst du dich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice