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Full Description
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, 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, you'll learn how to:
Uncover hidden patterns and trends in financial data
Build predictive models that drive investment decisions
Optimize portfolio performance using data-driven insights
Stay ahead of the competition with cutting-edge data analytics techniques
Whether you're a finance professional seeking to enhance your data analytics skills or a researcher looking to advance the field of finance through data-driven insights, this book is an essential resource. Dive into the world of data analytics in finance and discover the power to make informed decisions, drive business success, and stay ahead of the curve.
This book will be helpful for students, researchers, and users of machine learning and financial tools in the disciplines of commerce, management, and economics.
Contents
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 Naïve 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.
