Yosh olimlar 1-jild 13-son (2023) · 4–10-betlar

RESEARCHING MACHINE LEARNING ALGORITHMS AND BIG DATA ANALYSIS TO PREDICT DEMAND AND CUSTOMER BEHAVIOR

Tashmukhamedova, Gulchekhra, Saidov, Arslon, Shavkatov, Olimboy

DOI: 10.5281/zenodo.8363423 · Manbada o'qish →

Annotatsiya

This article explores the application of machine learning algorithms and big data analytics in predicting demand and customer behavior. With the increasing availability of vast amounts of data and advancements in machine learning techniques, organizations can leverage these tools to gain insights into customer preferences, anticipate demand patterns, and make data-driven decisions. The article discusses several commonly used machine learning algorithms, such as logistic regression, random forest, gradient boosting, support vector machines, neural networks, k-nearest neighbors, and naive Bayes, that have proven effective in customer behavior prediction tasks. Considerations for algorithm selection, including data availability, interpretability, scalability, and model complexity, are also discussed. Furthermore, the article highlights evaluation metrics commonly used to assess the performance of these algorithms, such as accuracy, precision, recall, F1 score, ROC curve, AUC, mean squared error, R-squared, lift, and mean average precision. By understanding and applying these techniques, organizations can gain a competitive advantage by accurately predicting demand and effectively targeting their customer base.

machine learning, big data analytics, demand prediction, customer behavior, logistic regression, random forest, gradient boosting, support vector machines, neural networks, k-nearest neighbors, naive Bayes, evaluation metrics.

Metadata manbasi: jurnal OAI-PMH arxivi · Sindex to'liq matnni saqlamaydi, manbaga havola beradi.