Data Analytics for Finance Using Python (Paperback, 1)

Preface. About the Authors.?Chapter 1. Stock portfolio investments management by Appling K Mean clustering. Chapter 2. Predicting stock price using the ARIMA model. Chapter 3. Stock investment strategy by using a logistic regression model. Chapter 4. Working capital management by applying the Naive Bayes model. Chapter 5. The random forest technique is a tool for stock trading decisions. Chapter 6. Stock management and decision tree technique for proper investment. Chapter 7. Descriptive statistics for stock risk analysis and its management. Chapter 8. Stock prediction using multiple regression model Chapter 9. F-test for stock risk assessment. Chapter 10 T-test for stock risk assessment Chapter 11. Z- test for stock risk assessment Chapter 12. Support vector machine learning model for stock prediction. Chapter 13 Stock risk analysis by using visualization. Chapter 14. LSTM for stock price prediction. Chapter 15 NPL for sentiments analysis for stock.?