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Next Product to Buy

Welcome to the Next Product to Buy project! This project leverages neural networks to predict the next product a customer is likely to purchase based on their historical buying patterns. By analyzing sequences of purchased products, the model learns patterns and dependencies, offering valuable insights into potential future purchases.

Key Features

• Neural Network Model: Utilizes a sequential model with an embedding layer, LSTM layer, and dense layer to capture intricate patterns in customer purchase sequences.
• Training and Inference: Separate scripts (trainer.py and inference.py) for model training and making predictions, allowing for flexibility and scalability.
• Configuration: Easily customizable through the config.conf file, enabling adjustments to paths, model parameters, and training settings.
• Metrics and Logging: Utilizes Weights & Biases (W&B) for tracking and logging metrics during model training.

Limitations

• Supported Products: The model currently supports predictions for nine specific products:
  1. Samsung Galaxy S21
  2. HP Wireless Mouse
  3. Dell XPS 13
  4. JBL Flip 5
  5. Nintendo Switch
  6. Sony Noise-Cancelling Headphones
  7. Acer Predator Helios
  8. Playstation 5
  9. Xiaomi Mi 11

Please note that the model is trained on data specific to these products, and predictions for other products may not yield accurate results.

This project serves as a powerful tool for businesses looking to enhance their understanding of customer behaviors and improve recommendation systems. Whether you are exploring machine learning or seeking predictive analytics for your e-commerce platform, Next Product to Buy provides a foundation for building intelligent recommendation systems.