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Personal portfolio predictor

  1. Title: Clear and concise, reflecting the essence of your project.
    1. Personal portfolio predictor
  2. Authors: Your name and any collaborators.
    1. Patrik Maly
  3. Introduction: Brief overview of the problem, its significance, and your motivation.
    1. The "Personal Portfolio Predictor" uses an autoencoder neural network to predict changes in institutional investors' stock portfolios. By focusing on these key market players, who hold a significant market share, the project aims to understand and anticipate shifts that can notably influence stock movements. This approach provides a straightforward yet effective tool for deciphering market trends and investor behaviors, offering an advancement over traditional methods like random guessing. It represents a step towards more data-driven and insightful investment strategy analysis.
  4. Background/Related Work: Short literature review relevant to your project.
  5. Methodology:
  6. Model Description: Details about the machine learning model you used (e.g., neural network, decision tree).
  7. Data Description: Types of data used, data preprocessing steps.
  8. Training Process: How you trained the model (include algorithms, hyperparameters).
  9. Results:
  10. Visualizations (charts, graphs) to illustrate key findings.
  11. Performance metrics (accuracy, precision, recall, etc.).
  12. Discussion:
  13. Interpretation of results.
  14. Challenges faced and how they were addressed.
  15. Conclusion:
  16. Summarize findings.
  17. Potential applications or implications of your work.
  18. Future Work: Suggestions for further research or improvements.
  19. References: Citations of all sources and papers referenced.

Format and Division

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  • Graphics:
  • Include charts, diagrams, and images to illustrate points.
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Additional Tips

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