BITCOIN ANALYSIS, VISUALIZATION, FORECASTING, AND PREDICTION WITH PYTHON GUI

Name: BITCOIN ANALYSIS, VISUALIZATION, FORECASTING, AND PREDICTION WITH PYTHON GUI
Brand: BALIGE PUBLISHING
SKU: 2005334

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2046416BITCOIN ANALYSIS, VISUALIZATION, FORECASTING, AND PREDICTION WITH PYTHON GUIhttps://www.gandhi.com.mx/bitcoin-analysis-visualization-forecasting-and-prediction-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/756436/7a945072-f3ad-42e9-b854-12714c6af069.jpg?v=638336086918570000184184MXNBALIGE PUBLISHINGInStock/Ebooks/Bitcoin is a digital currency created in January 2009. It follows the ideas set out in a whitepaper by the mysterious and pseudonymous Satoshi Nakamoto.1 The identity of the person or persons who created the technology is still a mystery. Bitcoin offers the promise of lower transaction fees than traditional online payment mechanisms and, unlike government-issued currencies, it is operated by a decentralized authority. This dataset provides the history of daily prices of Bitcoin. The data starts from 17-Sep-2014 and is updated till 09-July-2021. It contains 2747 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation.To perform forecasting based on regression on Adj Close price of Bitcoin, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Nave Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression.The machine learning models used predict Bitcoin daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model....2005334BITCOIN ANALYSIS, VISUALIZATION, FORECASTING, AND PREDICTION WITH PYTHON GUI184184https://www.gandhi.com.mx/bitcoin-analysis-visualization-forecasting-and-prediction-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/756436/7a945072-f3ad-42e9-b854-12714c6af069.jpg?v=638336086918570000InStockMXN99999DIEbook20221230005601349_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1230005601349_Bitcoin is a digital currency created in January 2009. It follows the ideas set out in a whitepaper by the mysterious and pseudonymous Satoshi Nakamoto.1 The identity of the person or persons who created the technology is still a mystery. Bitcoin offers the promise of lower transaction fees than traditional online payment mechanisms and, unlike government-issued currencies, it is operated by a decentralized authority. This dataset provides the history of daily prices of Bitcoin. The data starts from 17-Sep-2014 and is updated till 09-July-2021. It contains 2747 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation.To perform forecasting based on regression on Adj Close price of Bitcoin, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Nave Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression.The machine learning models used predict Bitcoin daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.(*_*)1230005601349_Bitcoin is a digital currency created in January 2009. It follows the ideas set out in a whitepaper by the mysterious and pseudonymous Satoshi Nakamoto.1 The identity of the person or persons who created the technology is still a mystery. Bitcoin offers the promise of lower transaction fees than traditional online payment mechanisms and, unlike government-issued currencies, it is operated by a decentralized authority. This dataset provides the history of daily prices of Bitcoin. The data starts from 17-Sep-2014 and is updated till 09-July-2021. It contains 2747 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation.To perform forecasting based on regression on Adj Close price of Bitcoin, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Nave Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression.The machine learning models used predict Bitcoin daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model....1230005601349_BALIGE PUBLISHINGlibro_electonico_170fa598-c672-34f3-a2e7-beac8cbfe6f2_1230005601349;1230005601349_1230005601349Rismon HasiholanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/f2ca928b-958d-498f-bb70-f76c6615d8f6-epub-3e56467d-f47d-418d-8b02-9d785d94c6bc.epub2022-06-04T00:00:00+00:00BALIGE PUBLISHING