Stock-Market-Prediction-Using-Stacked-LSTM-s

Table of Contents

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

Introduction

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

Prerequisites

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

Setup Instructions

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.

How to run

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.

Output

Caption: This is the model predictions

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