Optimization of Multidimensional Data Analysis Methods Based on Machine Learning Algorithms

Multi-dimensional data analysis is a fundamental approach to gaining critical insights in industries such as finance, healthcare and artificial intelligence. However, given the computational complexity and risk of overfitting, there are still significant challenges to continuously improve the techniques for analysing multidimensional datasets. This investigation employs cutting-edge machine learning techniques, covering feature selection methods and integration models, to construct an improved system for parsing multidimensional data. The study incorporates principal component analysis (PCA), feature ranking and random forest techniques to improve prediction accuracy and reduce computational overhead. Experimental results based on the base dataset of the study reveal that the classification accuracy is improved by 12 percentage points and the processing time is reduced by 18 percentage points compared with the conventional method. Evidence from the study shows that the constructed improved model successfully achieves the reconciliation of accuracy and efficiency, and proposes a scalable solution strategy to meet the challenges of multidimensional data analysis.