🔗 Google-Stock-Price-Predictor-Model

This project is build as a predictor model using LSTM methods, Google Stock Price Predictor.

It takes training data set as a .csv file named as Google_Stock_Price_Train.csv which contains the DataSet of Stock Prices of Google from March,2012 to December,2016.

The test dataset used contains the data of Stock Prices of Google for January,2017 names as Google_Stock_Price_Test.csv to predict the accuracy of the model.

The code is available in the file Google Stock Price Predictor.ipynb

🔗 Modules/Libraries Used:

1. Tensorflow
2. pandas
3. numpy
4. os
5. matplotlib
6. sklearn

🔗 Working with the model

Before working with the model ensure the modules in requirement.txt are pre installed. If it isn't, for installing follow the command:

   pip install -r requirements.txt

Run the code in JUPYTER NOTEBOOK and the results can be seen.

Thank You