This repository contains the Turing Test team’s solution for the Home Depot Product Search Relevance competition. The approach uses natural language processing with semantic matching techniques to predict how relevant search results are to user queries. It combines multiple models including word embedding approaches via gensim, neural networks built with Keras, and gradient boosting via xgboost. The solution features extensive feature engineering including text preprocessing, relevance scoring, and ensemble methods to achieve top competition rankings.