AI-Driven GPC Code Prediction

Project Summary

The AI-Driven GPC Code Prediction Solution was developed to predict Global Product Classification (GPC) Brick Codes and provide probabilistic suggestions for product categorization based on historical product data. By leveraging advanced AI, data science, and natural language processing techniques, the solution automated and enhanced the accuracy of product classification, enabling scalable and efficient data management for the client.

Objectives

Build a robust model to predict GPC Brick Codes from historical product data.
Provide high-confidence suggestions for probable Brick Codes to streamline product categorization.
Improve operational efficiency by reducing manual classification efforts.
Ensure scalability and adaptability for diverse product datasets.

Approach

The project adopted a Developer Engagement Model, fostering close collaboration with the client to align on requirements, iterate on solutions, and ensure seamless integration. The solution was built using a multi-stage cascade model to enhance prediction accuracy and robustness:

Data Engineering:

1. Cleaned and preprocessed large-scale historical product data using Pandas.
2. Applied data augmentation techniques to enrich dataset diversity and address data sparsity.
3. Engineered features such as product descriptions, metadata, and categorical attributes to improve model performance.
NLP and Semantic Analysis:

1. Utilized Fuzzy Wuzzy and NLTK for text preprocessing and semantic matching of product descriptions.
2. Integrated a Semantic Lexicon to capture contextual relationships between product attributes and GPC codes.
3.Enhanced feature extraction with tokenization, lemmatization, and vectorization.
Cascade Model Development:

1. Designed a cascade model combining LSTM (Long Short-Term Memory) networks and TensorFlow for sequential learning of product data patterns.
2. The model hierarchically refined predictions by cascading outputs through multiple layers, improving accuracy for ambiguous or complex cases.
3. Fine-tuned the model to balance precision and recall, ensuring reliable code suggestions.
Deployment and Scalability:

1. Deployed the model as a serverless application using Azure Functions, enabling cost-efficient scaling and real-time predictions.
2. Integrated APIs for seamless interaction with the client’s existing systems.

Technology Stack

Programming & Frameworks: Python, TensorFlow, LSTM
NLP & Text Processing: FuzzyWuzzy, Semantic Lexicon, NLTK
Cloud & Deployment: Azure Functions
Data Processing: Pandas, Data Engineering, Feature Engineering, Data Augmentation
Model Architecture: Cascade Model performance optimization

Results

Accuracy: Achieved a 92% accuracy rate in predicting GPC Brick Codes, surpassing
the client’s baseline of 80%.
Efficiency: Reduced manual classification time by 65%, enabling faster product onboarding.
Scalability: Successfully processed datasets with over 1 million product records, with Azure Functions ensuring consistent performance.
User Adoption: The suggestion system was adopted across multiple departments,
improving cross-functional workflows.

Client Impact

The AI-Driven GPC Code Prediction Solution transformed the client’s product classification process, enabling faster decision-making, reducing operational costs, and improving data accuracy. The scalable architecture ensured the solution could adapt to growing product catalogs, while the developer engagement model fostered trust and alignment throughout the project lifecycle.

Challenges and Solutions

Challenge: Ambiguous product descriptions led to misclassifications.
Solution:Implemented FuzzyWuzzy and Semantic Lexicon to enhance text similarity scoring and contextual understanding.

Challenge: Limited training data for niche product categories.
Solution: Applied data augmentation and transfer learning to generalize the model across sparse categories.

Challenge: High computational cost for real-time predictions.
Solution: Leveraged Azure Functions for serverless deployment, optimizing resource usage.

Conclusion

This project showcases the power of AI and data science in solving complex classification problems. By combining cutting-edge technologies like LSTM, FuzzyWuzzy, and Azure Functions, the solution delivered measurable value and set a foundation for future enhancements in automated product management.

Talk to us