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Activity badge for JordiCorbilla/stock-prediction-deep-neural-learning repository

Why JordiCorbilla/stock-prediction-deep-neural-learning is active?

The result is based on ratio of number of commits and code additions from initial and final time ranges.

Initial time range – from 10 Feb, 2023 to 13 May, 2023

Final time range – from 11 Nov, 2023 to 10 Feb, 2024

From 0 to 1 commits per week
Infinity%
From 2 to 607 additions per week
30250%
From 1 to 26 deletions per week
2500%
Data calculated on 10 Feb, 2024

Summary of JordiCorbilla/stock-prediction-deep-neural-learning

The JordiCorbilla/stock-prediction-deep-neural-learning GitHub repository is a Python project that predicts stock prices using Deep Learning Neural Networks with TensorFlow.

The repository is public and does not have many stars, indicating that it may not have been viewed or used by many other developers. The code appears to be up-to-date since the last commit was made reasonably recently.

The repository is self-contained and does not seem to rely on external services other than those required by the code itself (such as Tensorflow), making it easy to clone and run for learning purposes. There are no contributing guidelines, suggesting that the owner does not actively seek contributions.

Features:

  • Uses deep neural network models to predict stock prices
  • Makes use of Tensorflow, a popular machine learning framework
  • Contains a .gitignore file, a README and an MIT License
  • Does not have a lot of external dependencies, making it easier to run and test locally
  • Samples of stock data provided in csv files

Potential limitations:

  • There is no clear indication of the repository's overall quality or effectiveness of the stock prediction model
  • Resources for learning how to use the project and understanding the code are limited
  • There is a lack of engagement and contribution from other developers.

    This code can be useful for anyone interested in financial predictions and applications of deep learning in predicting time series data. However, it shouldn't be used for real stock market investments without proper understanding and enhancements.

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