An Italy-based retail business aiming to optimize inventory and customer targeting approached Oodles for an AI-driven solution. Their goal was to forecast customer buying behavior to streamline operations. The project focused on building a predictive model that could learn from historical purchases and accurately forecast future transactions.

The client sought a deep learning solution to predict the next value in customer purchase sequences. The project focused on neural network modeling using LSTM to identify patterns in time-series data. Areas of work included data preprocessing, model architecture design, training, validation, and deployment for real-time prediction.

To address the client’s need for accurate sequence prediction, Oodles implemented a custom LSTM-based model using Keras with TensorFlow backend. The team designed a preprocessing pipeline that structured time-series data using a sliding window method, making it ready for temporal learning.

Key Features Implemented:

• LSTM neural network architecture trained on historical purchase data
• Custom preprocessing for variable-length sequences
• Real-time forecasting of next purchase trends
• Evaluation using Mean Absolute Error and RMSE
• Interactive dashboards for monitoring trends and predictions.