Stock Predictor

Machine learning course project that tries to predict if the price of the selected stocks are going to rise or fall. We used 3 models for predicting the price and they are Linear Regression, LSTM and RNN.

Installing the requirements

To install all the libraries:

pip3 install -r requirements.txt

How it works

The db.json stores all the information about the stocks used, the predicted values and the mean squared error of the training. If you wanna add other stocks check this. When ready to add just add it in the stocks:

{
    "stocks": ["add","here","the","selected","stocks"]
}

After adding all the stocks needed, run the setup.py to create all the models and update the files inside result folder :

python3 src/setup.py

Run server

export FLASK_APP=app # one time only
flask run