1. Introduction
2. Prerequisites
3. Setup Instructions
4. How To run
5. Output

This repository contains an implementation of Stock Market prediction using Stacked LSTMs

Understanding of RNNs and LSTMs. Hands-on with Sklearn and Keras library.

Please make sure that Python is installed on your system, and also make sure to install the following libraries: numpy, pandas, keras, scikit-learn, and tensorflow.

In the LSTM folder, you can find the code for the market prediction code. Make sure you download a CSV of your favorite stock from Yahoo Finance API and rename it as stock_price.csv

The model, based on Stacked LSTM, analyzes historical market data with a sequence length of 100 days for improved forecasting accuracy. Explore the codebase for a straightforward solution to predict stock trends using machine learning.

Note: This is a basic implementation feel free to adjust the parameters as you need it.

Caption: This is the model predictions

Caption: This is the graph of Training Loss vs Validation Loss