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NN models designed to analyze and predict stock market from any stock data given

License: Apache License 2.0

Jupyter Notebook 99.53% Python 0.46% Dockerfile 0.01% Procfile 0.01%
stock-market prediction-model tensorflow cnn lstm cnn-lstm

stock-x's Introduction

Stock-X

License Stars Release Heroku

⚠️ MODEL is now available at Hugging Face: https://huggingface.co/kryox64/stock-x with DOI ⚠️

This project is all about analysis of Stock Market and providing suggestions to stockholders to invest in right company

Note: The notebook used here (IPYNB) is made using Kaggle, a data-science and ML community website which provides free Jupyter Notebook environment to work on programs and GPUs and TPUs to work on Neural Networks easily.

Here's the ref link to Kaggle

Notebook link for CNN-LSTM: Click here

Docker Image link (contains bundled libraries): Click here Size

Helm charts: Artifact Hub

Libraries used:

- Tensorflow
- Keras
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn

Neural Network type

Here CNN (with Time Distributed function) and Bi-LSTM combined Neural Network is used to train. Other algorithms like XGBoost, RNN-LSTM, LSTM-GRU are also added for comparison. Here are the links to view the notebooks directly. You can also view the results in the app created using Mercury which is deployed over Heroku (free dyno).

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