Ecommerce-Product-Categorization

A machine learning pipeline for hierarchical e-commerce product categorization, predicting both top-level and bottom-level product categories from product metadata.
Built with TF-IDF, SMOTE, and a Voting Ensemble of Logistic Regression, Random Forest, and LightGBM for top-level categories, and Ridge Classifier for bottom-level categories.

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📊 Project Overview

• Goal: Automatically assign e-commerce products to their correct top and bottom categories.
• Top-level classification: Voting Ensemble → F1-score = 0.91
• Bottom-level classification: Ridge Classifier → F1-score = 0.75
• Data type: Structured + unstructured product metadata (titles, descriptions, tags)
• Key challenges: Class imbalance, high-dimensional sparse features, noisy text