In the ever-expanding world of e-commerce, efficiently categorizing products is crucial for improving searchability, recommendations, and user experience. Traditional methods rely on manual tagging or basic keyword matching, which often leads to inconsistent or inaccurate product classifications. To address this, I developed Smart Shopping Assistant, an NLP-based recommendation system that accurately predicts categories and subcategories from natural language product descriptions.
🧠 Technical Details
✅ Data Processing & Feature Engineering
- Collected and preprocessed e-commerce product data, including titles, descriptions, and existing tags.
- Applied text cleaning, tokenization, and lemmatization to standardize input.
- Utilized TF-IDF, word embeddings (Word2Vec, FastText), and contextual embeddings (BERT) to represent product descriptions.
✅ Machine Learning & Deep Learning Models
- Trained a multi-class classification model to predict categories using:
- Traditional ML: Random Forest, SVM, XGBoost.
- Deep Learning: BiLSTM, Transformer-based models (BERT).
- Fine-tuned a pre-trained BERT model for more accurate semantic understanding of product descriptions.
- Implemented a hierarchical classification approach to predict both category and subcategory dynamically.
✅ Performance Optimization & Deployment
- Optimized hyperparameters to improve accuracy and reduce overfitting.
- Achieved significant improvement in classification accuracy, surpassing rule-based and traditional keyword-matching approaches.
- Designed a real-time API that integrates with e-commerce platforms, enabling automated product categorization at scale.
