Overview

Machine learning algorithms are classified into classification and regression, clustering, feature engineering, recommendation, and pattern mining algorithms based on actual application scenarios.

• Classification and regression analysis is a predictive modeling technology that explores the relationship between labels and features,
• Clustering is widely used. For example, in business, it helps market analysts distinguish different consumer groups from the consumer database and summarize the consumption patterns or habits of each group of consumers.
• Feature engineering is the process of transforming raw data into model training data. It aims to extract most relevant features so that a machine learning model can improve its accuracy. As the Internet industry develops and information resources grow at scale, it becomes increasingly difficult to obtain desired information from a large amount of complex data, which is why extraction of valuable information is urgently needed.
• Recommendation and pattern mining algorithms compare users' historical purchase records with products through collaborative filtering to better profile users' preferences.