9786134959216 - Automated Stock Market Trading using Machine Learning de Shah, Parth (6 resultados)

Paperback. Condición: Brand New. 56 pages. 8.66x5.91x0.13 inches. In Stock.

Taschenbuch. Condición: Neu. Automated Stock Market Trading using Machine Learning | Parth Shah | Taschenbuch | 56 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786134959216 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about the stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of effective models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Artificial Neural Network(ANN)and Random Forest have been used for developing the prediction model. Technical indicators are calculated from the stock prices based on timeline data and it is used as inputs of the proposed prediction models. Ten years of stock market data have been used for signal prediction of stock. Based on the dataset, these models are capable to generate buy/hold signal for the stock market as an output. 56 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shah ParthParth Shah completed graduate and postgraduate in computer science with a specialization in Machine Learning. Currently, he is working as a Data Scientist for leading private company in Ahmedabad, India. He has expertise in.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about the stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of effective models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Artificial Neural Network(ANN)and Random Forest have been used for developing the prediction model. Technical indicators are calculated from the stock prices based on timeline data and it is used as inputs of the proposed prediction models. Ten years of stock market data have been used for signal prediction of stock. Based on the dataset, these models are capable to generate buy/hold signal for the stock market as an output.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about the stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of effective models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Artificial Neural Network(ANN)and Random Forest have been used for developing the prediction model. Technical indicators are calculated from the stock prices based on timeline data and it is used as inputs of the proposed prediction models. Ten years of stock market data have been used for signal prediction of stock. Based on the dataset, these models are capable to generate buy/hold signal for the stock market as an output.