NOTE: STOCK PREDICTION IS VOLATILE. DO NOT USE THIS AS YOUR PREDICTION MODEL.
File info:
- stock-info.ipynb
- stock-lstm.ipynb
stock-info.ipynb: Contains data visualizations for the index predicted. Please be careful in inputting the correct index. Data source must be either Kaggle or Alphavantage. Handling other data sources will be implemented during revision.
stock-lstm.ipynb: Contains the actual code including model implementation, error of the model and quantitatively comparing Simple and Stacked LSTM networks.
Clone the repository:
$ git clone https://github.com/akshathmangudi/Stock-Prediction.git
$ Stock-Prediction/
For Python 3.6+ users:
$ python -m venv /path/to/virtualenv
For activation of virtualenv: bash/zsh:
$ source <venv>/bin/activate
fish:
$ source <venv/bin/activate.fish
cmd.exe:
C:\> <venv>\Scripts\activate.bat
PowerShell:
PS C:\> <venv>\Scripts\activate.ps1
For conda users, the following commands are to be sequentially passed into your terminal:
$ conda -V
$ conda update conda
$ conda create -n <envname> python=x.x anaconda
$ conda activate <envname>
For deactivation:
$ conda deactivate
The dependencies required to run this notebook can be installed using the below command.
$ pip install -r requirements.txt
For stock-info:
jupyter notebook stock-info.ipynb
For stock-lstm:
jupyter notebook stock-lstm.ipynb
Project by Akshath Mangudi (2023)