https://github.com/jezbravo/lstm

Stock predictions with LSTM
https://github.com/jezbravo/lstm

jupiter-notebook keras keras-neural-networks keras-tensorflow lstm lstm-model lstm-neural-networks machine-learning matplotlib numpy pandas pandas-dataframe python scikit-learn scikitlearn-machine-learning stock-price-prediction

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Stock predictions with LSTM

• Host: GitHub
• URL: https://github.com/jezbravo/lstm
• Owner: jezbravo
• Created: 2024-03-03T13:51:53.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-16T08:07:48.000Z (6 months ago)
• Last Synced: 2024-05-16T10:36:00.535Z (6 months ago)
• Topics: jupiter-notebook, keras, keras-neural-networks, keras-tensorflow, lstm, lstm-model, lstm-neural-networks, machine-learning, matplotlib, numpy, pandas, pandas-dataframe, python, scikit-learn, scikitlearn-machine-learning, stock-price-prediction
• Language: Jupyter Notebook
• Homepage: https://bit.ly/3yo54Z1
• Size: 191 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Stock predictions with LSTM

## Description

This application exemplifies the implementation of an LSTM network for the prediction of stock values ​​in the market.

### Stack

- Python:

  - Keras

  - Matplotlib

  - Numpy

  - Pandas

  - Scikt Learn

  - Jupiter Notebook

## Setup

First of all you must clone the repository:

```sh

git clone https://github.com/jezbravo/lstm.git

cd lstm

```

Create a virtual environment to install dependencies in and activate it:

```python

python.exe -m venv venv

.\venv\Scripts\activate

```

Then install the dependencies:

```python

(venv)pip install -r requirements.txt

```

Note the `(venv)` in front of the prompt. This indicates that this terminal session operates in a virtual environment set up by "venv".

Once everything is ready:

```python

(venv)python.exe .\app.py

```

## Demo

Both the dataset and the training parameters can be adjusted to improve the performance of the model and obtain better results.

You can review the development of the project at the following link: [LSTM Notebook](https://bit.ly/3yo54Z1).