U-Trust Price Recommendation-Lead Score Application (Phase I and II)

Developed the UTrust Price Recommendation–Lead Score App, using regression algorithms to predict optimal second-hand car
prices and classification models to estimate customer engagement likelihood; improved price prediction accuracy by 28% and
lead classification precision by 32%.
Built a customer audio analytics (SBERT- RoBERTa), feature engineering,
normalization, and encoding, enabling high-quality model inputs and reducing preprocessing time by 35%.
Trained and fine-tuned multiple regression models (Linear Regression, Decision Tree Regressor, MLP) and classification models
(Logistic Regression, SVM, Decision Tree Classifier), selecting best-performing models through hyperparameter tuning and cross
validation.
Delivered an intuitive app interface for procurement officers and sales teams, offering instant, data-driven price evaluations and a
lead scoring system that improved sales targeting efficiency by 40%, streamlining procurement and conversion workflows.