Review Helpfulness Prediction

Built a machine learning model to predict the helpfulness of user reviews based on their content, rating, and metadata (e.g., review length, sentiment score, and product category). This helps platforms prioritize more useful reviews for better user experience and trust.

Technologies & Tools Used:

Python (Pandas, NumPy, Scikit-learn)

Natural Language Processing (NLP)

TF-IDF & Word Embeddings

Sentiment Analysis

Logistic Regression, Random Forest, and XGBoost

Evaluation metrics: Accuracy, Precision, Recall, ROC-AUC

Key Achievements:

Achieved over 85% accuracy in predicting helpful vs. non-helpful reviews

Improved visibility of informative reviews for users

Enabled smarter filtering for e-commerce platforms