Stock Market Price Prediction Project using Deep Recurrent Neural Networks ( With Google Colab Notebook)

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In this post, we are applying Recurrent Neural Networks(RRN) and Long Short Term Memory (LSTM) techniques to predict and forecast Google Stock price for the next 3 months by analyzing patterns in past 3 months.  This project is going to develop in a step by step approach using Google Colab Notebooks for direct and easy access without any hesitation.

Table of Contents

  1.  What is the Stock Market Price Prediction?
  2.  What we are going to develop?
  3.  Requirements for Project 
  4. Algorithm Development
  5. Performance Analysis
  6. Further Improvements to Model

What is the Stock Market Price price Prediction?

Stock Market is a value or amount had by a company or person. The price of the stock changes for every moment with respect to time. 

Image Courtesy of Unsplash

The New advancements in Artificial Intelligence (AI) and Data-driven approaches have an incredible performance on stock market price estimation. In this context of price prediction, data is in the format of the value corresponding to time. AI Predict forecast or Predict stock market price by analyzing patterns in time series data. 

Time-series Data?

   Before going to implement the algorithm, we must have a basic understanding of time series data. The best example of time series data is a sinusoidal signal. 

i.e., The value of signal changes along the time at a particular time-space. 
The patterns in data are analyzed and forecasted with help of Memory stored in the previous cell.

What we are going to develop?

We are going to develop a Deep Learning algorithm specially Recurrent Neural Networks with Long Short Term Memory for this stock market price prediction.
  1. We are going to use 3 months Google Stock Market Price Data for training
  2.  Recurrent Neural Networks + Long Short Term Memory Model 
  3. Performance Comparision with original data over the predicted one. 

Requirements for Project 

These are basic requirements for the project
  1. Google Colab
  2. Google Stock Price Dataset
  3. Deep Learning Libraries ( Tensorflow and Keras)
  4. Python Programming Language

Algorithm Development

The project is developed in a step by step approach.

1.  Importing Libraries

2.  Reading Time series data 

3.  Data Pre-processing ( future scaling)

4. Creating a Data structure for storing memory of stock

5. Building RRN+LSTM

6. Making Prediction

7. Visualizing Results

    Click here to see full code in Github
    Google Colab Notebook for this Project

Performance Analysis

The graph drawn between predicted stock prices ad original stock price can tell us, Model is working at a 95% accuracy.

Further Improvements to Model

The Model performance can be improved by following approaches
  1. Adding more layers
  2. Changing loss function
  3. Changing the activation function
  4. Applying regularization

Thank you for your time !! 🤩🤩
Comment below if you have any doubts regarding code and everything
Google Neural Networks Deep Learning RRN LSTM