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.

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© 2024 Naveen Sankar RS

All rights reserved.