AI Sales Forecasting Software is a production-ready application that uses four advanced machine learning models to predict future sales with quantifiable accuracy. You receive complete source code including a Flask REST API backend, React-based web interface, pre-trained models, and three years of synthetic training data. The package enables you to forecast sales across multiple time horizons, compare model performance side-by-side, and integrate forecasting capabilities directly into your own products or services.
 
The software includes LSTM neural networks for capturing complex temporal patterns, Facebook Prophet for seasonal trend analysis, XGBoost for gradient-boosted predictions, and an Ensemble model that combines all three using optimized weighted averaging. Each model generates forecasts with confidence intervals. The backend exposes six REST API endpoints for forecasting, data upload, model training, metrics evaluation, and health monitoring. The frontend provides drag-and-drop CSV upload, interactive visualizations with Recharts, model comparison dashboards, and detailed prediction tables. All training data is synthetically generated to ensure broad coverage while protecting user privacy.
 
Built for developers, data teams, and businesses seeking to add forecasting to existing platforms, the software requires Python 3.8 or higher and basic technical knowledge to deploy. You can use it internally, modify the code for your needs, or build a materially new closed-source product or SaaS offering on top of it. The technology stack includes TensorFlow/Keras, scikit-learn, pandas, Flask, and React 18 with Tailwind CSS. System requirements specify 8GB RAM minimum with 16GB recommended; an NVIDIA GPU with CUDA support is optional for faster LSTM training.
 
The application ships with 2,400 lines of backend code across data preprocessing, feature engineering, model implementations, and evaluation utilities, plus 700 lines of frontend code. Documentation includes a comprehensive setup guide, backend API reference, frontend usage instructions, and quickstart tutorial. Typical model accuracy ranges from 5-15 percent MAPE and 0.85-0.95 R-squared depending on data characteristics. CSV input requires date and sales amount columns; optional columns include product category, promotion flags, and weekend indicators.
 
IMPORTANT NOTE: This application has been fully developed with all features implemented. However, it has not been tested in a live production environment. Buyer should expect to perform integration testing and may encounter minor bugs that require fixing. Basic technical knowledge and development skills are required for deployment, customization, and troubleshooting.
 
NO REFUNDS: Due to the digital nature, all sales are final.
 
LICENSE TERMS: Seller retains full ownership and control. Purchase grants a non-exclusive, non-transferable, perpetual license—AS IS, no support/updates, no refunds, no other obligations. Buyer may build and operate a materially new, closed-source product (including SaaS/paid service) for their own business/customers. Buyer may not open-source or disclose the application, nor resell, redistribute, rebrand, sublicense, or use the application (or any derivative) to create a competing or substantially similar product. License terms may be updated or changed at any time; continued use constitutes acceptance.