Big Data Evolution: A Deep Dive Into Gradient Descentand Smart Data Analytics

Study examines the obstacles presented by big data processing and evaluates optimization strategies, with a particular emphasis on Tailored Principal Component Analysis for efficient data management. The vast and complex nature of big data often overwhelms conventional methods. A crucial challenge in feature engineering is identifying the most impactful features for machine learning models by eliminating redundant or irrelevant variables. Tailored PCA, a dimensionality reduction technique, enhances computational efficiency and model generalization by retaining components that explain 90% of the data variance. It transforms correlated features into uncorrelated principal components, reducing multicollinearity and improving accuracy. To further refine this approach, we propose incorporating Sample Gradient Descent with Tailored PCA. Our experiments demonstrate that applying Tailored PCA before gradient descent methods like Stochastic Gradient Descent and Mini-Batch Gradient Descent significantly enhances model performance.