Stock Trend Prediction with LSTM and Streamlit Web App

Overview

This project utilizes LSTM (Long Short-Term Memory) neural networks to predict stock price trends and presents the results through an interactive web application built with Streamlit. The goal is to provide users with a user-friendly interface to access real-time stock price predictions based on historical data.

Features

• Data Collection: Gather historical stock price data from reliable sources.
• Data Preprocessing: Clean, transform, and prepare the data for LSTM model training.
• LSTM Model: Develop a deep learning model using LSTM layers for time series prediction.
• Model Deployment: Create an interactive web application with Streamlit to showcase predictions.
• Real-time Updates: If desired, implement real-time data updates for the web app.
• Visualization: Display predictions and historical data through charts and graphs.

Technologies Used

• Python
• Pandas for data manipulation
• Matplotlib and Seaborn for data visualization
• Scikit-Learn for data preprocessing (if applicable)
• TensorFlow and Keras for LSTM model development
• Streamlit for web application development

Getting Started

To get started with this project using Git Bash, follow these steps:

1. Clone this repository:

   https://github.com/aayushsoni4/Stock-Trend-Prediction.git

2. Navigate to the project directory:

   cd stock-trend-prediction

3. Install the required libraries:

   pip install -r requirements.txt

4. Follow the notebooks in the notebooks/ directory for detailed instructions on data analysis and LSTM model development.

5. To run the Streamlit web app, use:

   streamlit run app.py