Leveraging Machine Learning for Impactful Data Mining Insights

Abstract

Big Data and Data Mining Practice of Machine Learning Algorithm in Data Mining Due to acceleration of the processing speed of various information data, traditional information data processing algorithm is no longer an option for many organizations. For this reason, the organization under consideration uses machine learning algorithm practice to mine its data. Machine learning algorithm proves an impactful practice since it can use artificial intelligence, use a large number of data sample to practice as well as automatically finding the operation parameters and models. While considering the three-stage positioning based on support vector machine and K-nearest neighbor machine which improves positioning accuracy and speed, the end results enhances perfect positioning. Machine learning algorithms play a significant role in customer segmentation. It is a practice that helps an organization automatically segment customers into distinct groups based on their preferences, behaviors and demographics. Such segmentations help organizations plan their marketing strategies, product recommendations and communications to their targets segments. Through this data mining practice, an organization enjoys predictive analytics whereby they are able to predict customer’s behavior and anticipate what customers are likely to do in future through historical data analysis. An organization is also able to analyze its market basket in that this practice helps uncover connections between products frequently purchased together, product bundling and targeted marketing enhancing growth of the organization regardless of the competitive waves within the market. Works Cited Li, Yongxu. "Practice of machine learning algorithm in data mining field." 2020 International Conference on Advance in Ambient Computing and Intelligence (ICAACI). IEEE, 2020. file:///C:/Users/Admin/Downloads/Practice_of_Machine_Learning_Algorithm_in_Data_Mining_Field.pdf