Modeling And Prediction Of Cryptocurrency Prices Using Machine Learning Techniques
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Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-21
Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-21 with Computers categories.
In this project, we will be conducting a comprehensive analysis, prediction, and forecasting of cryptocurrency prices using machine learning with Python. The dataset we will be working with contains historical cryptocurrency price data, and our main objective is to build models that can accurately predict future price movements and daily returns. The first step of the project involves exploring the dataset to gain insights into the structure and contents of the data. We will examine the columns, data types, and any missing values present. After that, we will preprocess the data, handling any missing values and converting data types as needed. This will ensure that our data is clean and ready for analysis. Next, we will proceed with visualizing the dataset to understand the trends and patterns in cryptocurrency prices over time. We will create line plots, box plot, violin plot, and other visualizations to study price movements, trading volumes, and volatility across different cryptocurrencies. These visualizations will help us identify any apparent trends or seasonality in the data. To gain a deeper understanding of the time-series nature of the data, we will conduct time-series analysis year-wise and month-wise. This analysis will involve decomposing the time-series into its individual components like trend, seasonality, and noise. Additionally, we will look for patterns in price movements during specific months to identify any recurring seasonal effects. To enhance our predictions, we will also incorporate technical indicators into our analysis. Technical indicators, such as moving averages, Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD), provide valuable information about price momentum and market trends. These indicators can be used as additional features in our machine learning models. With a strong foundation of data exploration, visualization, and time-series analysis, we will now move on to building machine learning models for forecasting the closing price of cryptocurrencies. We will utilize algorithms like Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, Multi-Layer Perceptron Regression, Lasso Regression, and Ridge Regression to make forecasting. By training our models on historical data, they will learn to recognize patterns and make predictions for future price movements. As part of our machine learning efforts, we will also develop models for predicting daily returns of cryptocurrencies. Daily returns are essential indicators for investors and traders, as they reflect the percentage change in price from one day to the next. By using historical price data and technical indicators as input features, we can build models that forecast daily returns accurately. Throughout the project, we will perform extensive hyperparameter tuning using techniques like Grid Search and Random Search. This will help us identify the best combinations of hyperparameters for each model, optimizing their performance. To validate the accuracy and robustness of our models, we will use various evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. These metrics will provide insights into the model's ability to predict cryptocurrency prices accurately. In conclusion, this project on cryptocurrency price analysis, prediction, and forecasting is a comprehensive exploration of using machine learning with Python to analyze and predict cryptocurrency price movements. By leveraging data visualization, time-series analysis, technical indicators, and machine learning algorithms, we aim to build accurate and reliable models for predicting future price movements and daily returns. The project's outcomes will be valuable for investors, traders, and analysts looking to make informed decisions in the highly volatile and dynamic world of cryptocurrencies. Through rigorous evaluation and validation, we strive to create robust models that can contribute to a better understanding of cryptocurrency market dynamics and support data-driven decision-making.
Machine Learning And Modeling Techniques In Financial Data Science
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Author : Chen, Haojun
language : en
Publisher: IGI Global
Release Date : 2025-01-22
Machine Learning And Modeling Techniques In Financial Data Science written by Chen, Haojun and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Business & Economics categories.
The integration of machine learning and modeling in finance is transforming how data is analyzed, enabling more accurate predictions, risk assessments, and strategic planning. These advanced techniques empower financial professionals to uncover hidden patterns, automate complex processes, and enhance decision-making in volatile markets. As industries increasingly rely on data-driven insights, the adoption of these tools contributes to greater efficiency, reduced uncertainty, and competitive advantage. This technological shift not only drives innovation within financial sectors but also supports broader economic stability and growth by improving forecasting and mitigating risks. Machine Learning and Modeling Techniques in Financial Data Science provides an updated review and highlights recent theoretical advances and breakthroughs in professional practices within financial data science, exploring the strategic roles of machine learning and modeling techniques across various domains in finance. It offers a comprehensive collection that brings together a wealth of knowledge and experience. Covering topics such as algorithmic trading, financial technology (FinTech), and natural language processing (NLP), this book is an excellent resource for business professionals, leaders, policymakers, researchers, academicians, and more.
Modeling And Prediction Of Cryptocurrency Prices Using Machine Learning Techniques
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Author : Alireza Ashayer
language : en
Publisher:
Release Date : 2019
Modeling And Prediction Of Cryptocurrency Prices Using Machine Learning Techniques written by Alireza Ashayer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
With the introduction of Bitcoin in the year 2008 as the first practical decentralized cryptocurrency, the interest in cryptocurrencies and their underlying technology, Blockchain, has skyrocketed. Their promise of security, anonymity, and lack of a central controlling authority make them ideal for users who value their privacy. Academic research on machine learning, Blockchain technology, and their intersection have increased significantly in recent years. Specifically, one of the interest areas for researchers is the possibility of predicting the future prices of these cryptocurrencies using supervised machine learning techniques. In this thesis, we investigate their ability to make one day ahead price prediction of several popular cryptocurrencies using five widely used time-series prediction models. These models are designed by optimizing model parameters, such as activation functions, before settling on the final models presented in this thesis. Finally, we report the performance of each time-series prediction model measured by its mean squared error and accuracy in price movement direction prediction.
Blockchain Applications In The Smart Era
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Author : Sanjay Misra
language : en
Publisher: Springer Nature
Release Date : 2022-04-19
Blockchain Applications In The Smart Era written by Sanjay Misra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-19 with Technology & Engineering categories.
This book covers a variety of topics and trends related to blockchain technology for smart era applications. The applications span industries such as health, government, energy management, manufacturing, finance, information systems, all far beyond blockchain's original use in cryptocurrency. The authors present variants, new models, practical solutions, and technological advances related to blockchain in these fields and more. The applications within these fields include blockchain and cyber-security, IoT security and privacy using blockchain, and blockchain in industries and society . A variety of case studies are also included. The book is applicable to researchers, professionals, students, and professors in a variety of fields in communications engineering.
Proceedings Of The 2022 International Conference On Mathematical Statistics And Economic Analysis Msea 2022
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Author : Gaikar Vilas Bhau
language : en
Publisher: Springer Nature
Release Date : 2024-03-13
Proceedings Of The 2022 International Conference On Mathematical Statistics And Economic Analysis Msea 2022 written by Gaikar Vilas Bhau and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-13 with Mathematics categories.
This is an open access book. 2022 International Conference on Mathematical Statistics and Economic Analysis(MSEA 2022) will be held in Dalian, China from May 27 to 29, 2022. Based on probability theory, mathematical statistics studies the statistical regularity of a large number of random phenomena, and infers and forecasts the whole. Economic development is very important to people's life and the country. Through data statistics and analysis, we can quickly understand the law of economic development. This conference combines mathematical statistics and economic analysis for the first time to explore the relationship between them, so as to provide a platform for experts and scholars in the field of mathematical statistics and economic analysis to exchange and discuss.
Proceedings Of International Conference On Information Technology And Applications
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Author : Abrar Ullah
language : en
Publisher: Springer Nature
Release Date : 2024-03-17
Proceedings Of International Conference On Information Technology And Applications written by Abrar Ullah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-17 with Computers categories.
This book includes high-quality papers presented at 17th International Conference on Information Technology and Applications (ICITA 2023), held in Turin, Italy during 20 – –22 October 2023. The book presents original research work of academics and industry professionals to exchange their knowledge of the state-of-the-art research and development in information technology and applications. The topics covered in the book are cloud computing, business process engineering, machine learning, evolutionary computing, big data analytics, internet of things and cyber-physical systems, information and knowledge management, computer vision and image processing, computer graphics and games programming, mobile computing, ontology engineering, software and systems modelling, human computer interaction, online learning /e-learning, computer networks, and web engineering.
Data Science And Machine Learning
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Author : Diana Benavides-Prado
language : en
Publisher: Springer Nature
Release Date : 2023-12-04
Data Science And Machine Learning written by Diana Benavides-Prado and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-04 with Computers categories.
This book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11–13, 2023. The 20 full papers presented in this book were carefully reviewed and selected from 50 submissions. The papers are organized in the following topical sections: research track and application track. They deal with topics around data science and machine learning in everyday life.
Proceedings Of The International Conference On Computational Innovations And Emerging Trends Icciet 2024
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Author : K. Reddy Madhavi
language : en
Publisher: Springer Nature
Release Date : 2024-07-30
Proceedings Of The International Conference On Computational Innovations And Emerging Trends Icciet 2024 written by K. Reddy Madhavi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with Computers categories.
This is an open access book. International Conference on Computational Innovations and Emerging Trends ICCIET- 2K24 ICCIET’24 has emerged as an enduring techno-platform to connect education experts and passionate educators all over the world for improving the potential for excellence in engineering education. It provides a premier interdisciplinary forum for researchers, engineers, academicians to present and discuss the most recent trends, innovations, concerns, practical challenges encountered, solutions adopted in the field of Computational Intelligence with its allied areas. The conference also aims to provide a platform for scientists, scholars, students from universities all around the world and the industry to present ongoing research activities and hence to foster research relations between the universities and the industry. Scope of the Conference The conference focuses on mutually sharing the advances and innovative technologies for the scientists, scholars, engineers and students from different universities and industry practitioners, to present ongoing research activities in the recent trends of Computer Science and Engineering This conference addresses the relevant topics and research issues in the vicinity of Computational Intelligence and hence to foster collaborations among stakeholders and researchers from distinct universities, national laboratories, government funding bodies and the industry.
Blockchain Crypto Assets And Financial Innovation
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Author : Gang Kou
language : en
Publisher: Springer Nature
Release Date :
Blockchain Crypto Assets And Financial Innovation written by Gang Kou and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Machine Learning And Cryptographic Solutions For Data Protection And Network Security
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Author : Ruth, J. Anitha
language : en
Publisher: IGI Global
Release Date : 2024-05-31
Machine Learning And Cryptographic Solutions For Data Protection And Network Security written by Ruth, J. Anitha and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
In the relentless battle against escalating cyber threats, data security faces a critical challenge – the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography. Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats.