Video tutorial for the code: https://youtu.be/gSYiKKoREFI
Build a Artificial Neural Network (ANN) with Long-Short Term Memory unit (LSTM) to predict value which can be impacted by multiple different features.
In this video I demonstrate how to use LSTM to predict Google Stock price (you can use any other case) by taking into consideration multiple predictors (features). Let's say, the final stock price can be predicted by finding importance of such features as historical low price, high price, volume, adj. price, etc.
In this tutorial I used Tensorflow 1.15.0 and Keras 2.3.1
Download data from: https://finance.yahoo.com/quote/GOOG/history
This is real life Python code example for demonstration purposes, so the model is not very accuracy and of course could be improved or tuned.
My goal of this Python tutorial is to demonstrate how to perform LSTM predictions with multiple features (complex dataset).
Hoping it will help to undersant the way it could be implemented in real Data Science or Data Analysis projects.