Research and Discussion on Comparative Prediction Models Based on XGBoost and Random Forest and Clustering Analysis

This research aims to explore a set of data sets with multiple numerical features, make machine learning prediction by XGBoost and random forest algorithm, and make a comprehensive comparison of their performances. Through in-depth analysis of the performance of these two algorithms in key performance indicators such as accuracy, precision and recall, it provides a preliminary evaluation for model selection. Combined with the use of K-means algorithm to classify numerical features, it is to reveal the inherent clustering structure of data. By comprehensively analyzing the prediction results of machine learning model and the classification effect of K-means clustering, this study aims to deeply understand the complex association of data and the potential patterns between features. The conclusions obtained will hopefully provide profound insights for model selection and feature engineering in practical applications, and provide useful methodological reference for further research in the field of data science.