Analysis and Mining of 5G Potential Customers Based on Machine Learning

With the continuous development and improvement of communication network engineering and new infrastructure technologies, China is gradually realizing the transition from a 4G society to a 5G society. 5G, with its technical advantages of low latency, large bandwidth and wide connectivity, has become an important technical background for the construction of smart cities and digital villages. In order to achieve the conditions for large-scale connectivity of 5G networks required for the construction of smart cities, a higher utilization rate of 5G users is required. Based on this problem, this paper obtains data from a mobile big data platform, builds a classification prediction model based on the prediction problem of potential 5G users, correctly identifies potential 5G users and makes accurate service recommendations to them, improves the 5G utilization rate in China, and promotes the rapid upgrade of the construction of new smart cities. The process of building the prediction model mainly includes data pre-processing, feature engineering, training and evaluation of the model. Firstly, data pre-processing and exploratory analysis were performed, and a series of pre-processing work including data cleaning, removal of unique value attributes, data transformation, etc. were carried out for the data, followed by variable screening of the features in the dataset of this paper through chi-square test, statistical t-test and Pearson correlation coefficient method, and 24 feature variables with high feature importance were screened out. Models were constructed based on the screened feature variables, including Random Forest model, CatBoost model, and LightGBM model, and parameter tuning was performed to find the optimal parameters. The models are built according to the obtained optimal parameters and tested by the test set, and the models are evaluated by accuracy, recall, and AUC value indexes, and the comparison reveals that the LightGBM model is generally better than other models for 5G potential user prediction. In addition, the importance scores of the features are obtained by the above model and ranked in importance. Through the method of this paper to achieve more accurate identification and mining of 5G potential users, operators can accordingly realize accurate marketing for different customers, promote more users to realize the transition from 4G to 5G, and accelerate the sustainable development of China’s 5G market and the construction of smart cities.