Cryptocurrency Market Forecasting With Catboost Models (eBook, ePUB)
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Cryptocurrency Market Forecasting With Catboost Models explores the intersection of Financial Technology (FinTech) and Big Data Analytics, specifically their impact on cryptocurrency market predictions. It also discusses advanced machine learning techniques, such as the Catboost model, and the evolving landscape of technologies like quantum computing and blockchain. The book outlines the role of digital transformation in finance by introducing FinTech and its evolution. It then covers cryptocurrency market forecasting, addressing its unique challenges and opportunities. Detailed chapters follo...
Cryptocurrency Market Forecasting With Catboost Models explores the intersection of Financial Technology (FinTech) and Big Data Analytics, specifically their impact on cryptocurrency market predictions. It also discusses advanced machine learning techniques, such as the Catboost model, and the evolving landscape of technologies like quantum computing and blockchain.
The book outlines the role of digital transformation in finance by introducing FinTech and its evolution. It then covers cryptocurrency market forecasting, addressing its unique challenges and opportunities. Detailed chapters follow the Catboost model, covering its features, practical applications, and advantages. Essential topics include data cleaning, feature engineering, and data preprocessing for robust predictive models. The book also explores big data analytics, distributed computing frameworks, and ethical considerations in AI for market predictions. Real-world case studies illustrate key concepts, concluding with a forward-looking analysis of emerging technologies.
Key Features:
- Comprehensive coverage of FinTech and its impact on market predictions.
- Detailed explanation and practical applications of the Catboost model.
- Insights into data preprocessing, feature engineering, and model evaluation.
- Exploration of big data analytics and distributed computing in finance.
- Discussion of ethical considerations and regulatory challenges in AI for market predictions.
- Real-world case studies and examples.
Readership:
Suitable for students in finance, computer science, and data analytics, and professionals in finance and technology sectors, including data scientists, financial analysts, and investment managers.
This book is a valuable resource for anyone looking to deepen their understanding of FinTech's impact on cryptocurrency market forecasting using cutting-edge machine learning methodologies.
The book outlines the role of digital transformation in finance by introducing FinTech and its evolution. It then covers cryptocurrency market forecasting, addressing its unique challenges and opportunities. Detailed chapters follow the Catboost model, covering its features, practical applications, and advantages. Essential topics include data cleaning, feature engineering, and data preprocessing for robust predictive models. The book also explores big data analytics, distributed computing frameworks, and ethical considerations in AI for market predictions. Real-world case studies illustrate key concepts, concluding with a forward-looking analysis of emerging technologies.
Key Features:
- Comprehensive coverage of FinTech and its impact on market predictions.
- Detailed explanation and practical applications of the Catboost model.
- Insights into data preprocessing, feature engineering, and model evaluation.
- Exploration of big data analytics and distributed computing in finance.
- Discussion of ethical considerations and regulatory challenges in AI for market predictions.
- Real-world case studies and examples.
Readership:
Suitable for students in finance, computer science, and data analytics, and professionals in finance and technology sectors, including data scientists, financial analysts, and investment managers.
This book is a valuable resource for anyone looking to deepen their understanding of FinTech's impact on cryptocurrency market forecasting using cutting-edge machine learning methodologies.
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