9781484274330 - Econometrics and Data Science: Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems de Nokeri, Tshepo Chris (16 resultados)

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Taschenbuch. Condición: Neu. Neuware -Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 248 pp. Englisch.

Taschenbuch. Condición: Neu. Econometrics and Data Science | Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems | Tshepo Chris Nokeri | Taschenbuch | xviii | Englisch | 2021 | APRESS | EAN 9781484274330 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condición: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. 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 LearnExamine 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.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science.Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regressionanalysis, 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 andcluster analysis. Key deep learning concepts and ways ofstructuring 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 aneconomy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, andpath analysis.After reading this book, you should be able to recognize the connection between econometricsand data science. You will know how to apply a machine learning approach to modeling complexeconomic problems and others beyond this book. You will know how tocircumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to dealwith pressing economic problems. What You Will LearnExamine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden statesBe familiar with practical applications of machine learning and deep learning in econometricsUnderstand theoretical framework and hypothesis development, and techniques for selecting appropriate modelsDevelop, 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 modelsRepresent and interpret data and models Who This Book Is ForBeginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives 248 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate-Advanced user levelGet up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science.Author Tshepo Chris Nokeri begins by introducing you to covariance .

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science.Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regressionanalysis, 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 andcluster analysis. Key deep learning concepts and ways ofstructuring 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 aneconomy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, andpath analysis.After reading this book, you should be able to recognize the connection between econometricsand data science. You will know how to apply a machine learning approach to modeling complexeconomic problems and others beyond this book. You will know how tocircumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to dealwith pressing economic problems. What You Will LearnExamine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden statesBe familiar with practical applications of machine learning and deep learning in econometricsUnderstand theoretical framework and hypothesis development, and techniques for selecting appropriate modelsDevelop, 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 modelsRepresent and interpret data and models Who This Book Is ForBeginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives.