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Sales-Forecasting-Using-LSTM

This repository contains a project for forecasting sales using a Long Short-Term Memory (LSTM) model. It aims to analyze Walmart sales data and predict future trends using deep learning techniques.

Files and Directories

  • templates/: Contains HTML templates for the Flask application.
  • FAI_report.pdf: Final analysis and insights report.
  • LICENSE: License details for this project.
  • LSTM_Training_Notebook.ipynb: Jupyter notebook for training the LSTM model.
  • README.md: This file, providing project details.
  • Walmart.csv: Dataset used for sales analysis and forecasting.
  • app.py: Flask application for deploying the sales forecasting model.

Features

  • Preprocessing of Walmart sales data.
  • Development of an LSTM model for time-series forecasting.
  • Deployment of the model using a Flask web application.
  • Visualizations and insights provided in the final report.

Requirements

  • Python 3.8 or higher
  • Flask
  • Pandas
  • NumPy
  • TensorFlow/Keras
  • Matplotlib

Install dependencies using:

pip install -r requirements.txt

Usage

  1. Clone this repository: 
    git clone https://github.com/naveensankar5905/Sales-Forecasting-Using-LSTM.git
  2. Navigate to the project directory and run the Flask app: 
    python app.py
  3. Access the web application at http://127.0.0.1:5000.

License

This project is licensed under the MIT License.

© 2024 Naveen Sankar RS

All rights reserved.