Research on Multi-Source Heterogeneous Data Processing Technology Based on Machine Learning

Abstract

Given that multi-source heterogeneous data is scattered, mixed in format, and full of noise and missing parts, making traditional methods struggle to fully tap into its value, this article explores machine learning-based techniques for handling such data. First, break down the basic characteristics of multi-source heterogeneous data, and categorize the data sources and types; then build a complete data quality management process covering gaps filling, outlier detection and handling, normalization, and feature dimensionality reduction and selection; finally, study data fusion strategies at different levels and compare how machine learning models like logistic regression and XGBoost perform on heterogeneous datasets. The experiment results show that after a complete treatment process, the XGBoost model performs better in accuracy, F1 score, AUC, and other metrics, and has stronger robustness against noise and abnormal data. This article analyzes the limitations of existing technologies and looks ahead to future developments in this field, which can provide technical references for analyzing multi-source heterogeneous data in industrial scenarios.