This project has been made with an intension to give kickstart to beginners in field of machine learning and deep learning. This is a project-cum-tutorial to implement a Recurrent Neural Network (RNN) to predict stock prices on basis of Google Stock Price Dataset. It is highly recommended to beginners to go through the markdown files available in this repository for better understanding.
- Introduction.md - It provides a brief introduction and explains about the project and dataset.
- Installation Requirements.md - This file guides you for the downloading and installation processes of essential libraries, modules, text editor(Jupyter Notebook-in our case) and compatibilities for this project.
- Stock Prediction With RNN Model.ipynb - This is the place where you are going to find all the code and their explainations required to implement this project.
- Outputs - It is the folder which contains all the output images generated during data processing and visualization tasks.
- Google_Stock_Price_Train.csv - contains the training data.
- Google_Stock_Price_Test.csv - contains the testing data.
- stock-prediction-with-rnn-model.log - This is the log file generated while executing code at Kaggle Kernels.
- It is advised to go through the Introduction.md and Installation Requirements.md file before jumping to the notebook file.
- All the learners are free to ask any question regarding this project work. I'll try to answer them each.