Telecom Platform Churn Prediction and Retention Strategies

The telecommunications industry is one of the pillar industries of the country, and with the popularization of mobile Internet and the vigorous development of the digital economy, the importance of network infrastructure has become increasingly prominent. The purpose of this paper is to use machine learning methods to predict telecom subscriber churn and identify the key factors influencing subscriber churn. By analyzing the Telco Customer Churn dataset on the Kaggle platform, this study provides an in-depth analysis of the attributes and behaviors of more than 7,000 users. During the data processing phase, data cleansing, preprocessing, and feature engineering were performed to better understand user data and build predictive models. The random forest algorithm was used to evaluate the performance of the model by calculating precision, recall and F1-Score. Through model testing and iterative optimization, model parameters are continuously adjusted to improve prediction accuracy. This study finally identified the important factors influencing user churn and analyzed these important factors through a series of visualization methods. Then, based on the conclusions drawn from the analysis, it provides recommendations for marketing strategies and user retention measures for telcos. The successful implementation of this study in the real world can help telcos prevent subscriber churn more effectively and improve customer satisfaction.