Case Study

project description

  • In this project, we created a time series prediction model where it can predict the price of any input stock.

  • For the time series model, we have used LSTM neural network.

  • To implement this we have created the AI model which will be live-trained as per the user request. So basically users can enter the date and number of symbols whose close price is to be predicted and the output will be a data frame with selected columns.

  • Moreover, we have also created a GUI for this, so here users can enter the request input and will get the expected output.

  • To create GUI we have used the Gradio library available in python, specifically for machine learning. It is easy to use and can create a good GUI only with a few lines of code

Technologies used

  • To accomplish this project we used Keras, Tensorflow, and Pytorch to build the model.

  • Django is used to create the backend for deploying ML models.

  • We have collected more than 600GB of data on the stock market, from various data sources. And currently, we are performing data cleaning on this data.

  • For FrontEnd, we are using React as it is the most responsive and scalable.

Difficulties we faced

  • The major problem with this project was handling the huge data and creating a model which can understand the entire data.

  • Another significant problem is handling multiple backends with a single frontend.

  • The significant problem with this project was implementing live training of the model.


  • We are using HDF5 format to handle the big data for faster reading and calculations.

  • We have integrated all the backends using API and created different Cpanel for every backend.

  • So we have created a minimal model which can be trained quickly with GPU and for better performance, we have used Google Colab.


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