deep learning Lstm Stock model predictor for Google csv

Case study of my Alma matters project in the university of Adekunle Adjasin

1. OYEBADE OLUWATOBILOBA MATRIC NO ; 170404103 DEEP LEARNING PREDICTION OF UNCERTAINTIES IN STOCK MARKET PRICES

2. METHODOLOGY

3. 3.1 Methodology

4. 3.1.2 Methodology Details The Random Forest ensemble approach is a learning algorithm used for classification, regression, and other tasks This study uses the algorithm as a base model to achieve the research objectives To ensure the successful execution of the study, the following steps are taken: Data collection: Relevant variables are gathered and evaluated in a methodical manner to address research questions, test hypotheses, and evaluate outcomes

5. 3.2 Architectural Design

6. 3.3 Methodology Procedure

7. 3.3.1 Data Collection

8. 3.3.2 Data Preprocessing

9. 3.3.3 Training and Testing of Data

10. 3.3.4 Prediction The model predicts future stock prices based on the trained data and the input variables

11. 3.3.5 Evaluation

12. 3.3.4 Building the Model using PyTorch

13. 3.3.5 Training the Model

14. 3.3.6 Model Evaluation

15. 3.3.6 Model Evaluation

16. IMPLEMENTATION

17. 4.1 Introduction

18. 4.2 System Implementation

19. Creating the Agent

20. Define Basic Functions

21. Evaluation of the model

22. 4.2 Model Training and Testing results

23. 4.3 Details of Dataset

24. 4.4 System Documentation

25. 4.5 Confusion matrix plot for Prediction

26. 4.6 Score Metrics

27. RESULTS AND ANALYSIS

28. Training the Agent Training Output at the end of the first episode

29. CHAPTER 4.5 Figure:

30. 4.7 User’s Guide

31. Chapter 5: Results and Analysis

32. 5.1 Model Architecture and Hyper-parameters: A Symphony of Design Decisions

33. 5.2 Data Preprocessing and Feature Engineering: Refinement for Model Consumption

34. 5.3 Training Process Details: The Art of Model Refinement

35. 5.4 Evaluation Metrics: Quantifying Predictive Proficiency

36. 5.5 Handling Model Overfitting: The Guardian Against Complexity

37. 5.6 Interpretability and Visualization: Decoding Model Decisions

38. 5.7 Future Enhancements and Scalability: Paving the Path for Evolution

39. 5.8 Robustness Testing: Stress-Testing Model Fortitude

40. 5.9 Deployment Considerations: Bridging the Gap to Real-World Application