Introduction

This project focuses on forecasting and predicting stock prices using a Stacked Long Short-Term Memory (LSTM) neural network. The goal is to develop a model that can effectively analyze historical stock price data and provide predictions for future price movements.

Project Overview

The goal of this project is to utilize the Stacked LSTM architecture to forecast and predict stock prices based on historical data. The project involves the following steps:

1. Data Exploration: Analyze the historical stock price dataset to understand its structure, patterns, and trends. Visualize the data to gain insights into the historical stock price movements.

2. Data Preprocessing: Perform data preprocessing steps such as scaling the data and splitting it into training and testing sets. Prepare the dataset in a format suitable for training the Stacked LSTM model.

3. Model Development: Build and train a Stacked LSTM neural network using the training dataset. Configure the model architecture and tune hyperparameters to optimize its performance.

4. Model Evaluation: Evaluate the trained Stacked LSTM model’s performance on the testing dataset using appropriate evaluation metrics such as mean squared error (MSE) or root mean squared error (RMSE). Visualize the predicted stock price movements against the actual values.

Technologies used: Exploratory Data Analysis, Data Visualization, Deep Learning

GitHub Repository Link: -Stock Price Prediction and Forecasting using Stacked LSTM