Data Analytics for Finance Using Python [Hardcover]

Unlock the power of data analytics in finance with this comprehensive guide. 'Data Analytics for Finance Using Python is your key to unlocking the secrets of the financial markets.?In this book, you'll discover how to harness the latest data analytics techniques.?

1. Stock Investments Portfolio Management by Applying K-Means Clustering. 2. Predicting Stock Price Using the ARIMA Model. 3. Stock Investment Strategy Using a Logistic Regression Model. 4. Predicting Stock Buying and Selling Decisions by Applying the Gaussian Na?ve Bayes Model Using Python Programming. 5. The Random Forest Technique Is a Tool for Stock Trading Decisions. 6. Applying Decision Tree Classifier for Buying and Selling Strategy with Special Reference to MRF Stock. 7. Descriptive Statistics for Stock Risk Assessment. 8. Stock Investment Strategy Using a Regression Model. 9. Comparing Stock Risk Using F-Test. 10. Stock Risk Analysis Using t-Test. 11. Stock Investment Strategy Using a Z-Score. 12. Applying a Support Vector Machine Model Using Python Programming. 13. Data Visualization for Stock Risk Comparison and Analysis. 14. Applying Natural Language Processing for Stock Investors Sentiment Analysis. 15. Stock Prediction Applying LSTM.

Unlock the power of data analytics in finance with this comprehensive guide. Data Analytics for Finance Using Python is your key to unlocking the secrets of the financial markets.

In this book, youll discover how to harness the latest data analytics techniques, including machine learning and inferential statistics, to make informed investment decisions and drive business success. With a focus on practical application, this book takes you on a journey from the basics of data preprocessing and visualization to advanced modeling techniques for stock price prediction.

Through real-world case studies and examples, youll learn how to:

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