随着信息化建设的迅速推进,电信市场趋于饱和,如何应对用户流失成为通信运营商亟待解决的问题。本文基于电信用户数据,对用户流失趋势进行了深入预测分析。首先,针对数据缺失进行了填补,并对特征进行编码和衍生,使用SMOTE与Tomek Link...随着信息化建设的迅速推进,电信市场趋于饱和,如何应对用户流失成为通信运营商亟待解决的问题。本文基于电信用户数据,对用户流失趋势进行了深入预测分析。首先,针对数据缺失进行了填补,并对特征进行编码和衍生,使用SMOTE与Tomek Link技术处理了数据不均衡问题。接着,本文使用随机森林、XGBoost、SVM、逻辑回归、AdaBoost和GBDT六种单一模型分别进行用户流失预测。为了提高预测的准确性和稳健性,本文采用了Stacking多模型融合的方式,模型对比结果表明,第二层模型选用SVM达到了最高的准确率(0.8645),各项指标均优于单一模型。研究证明,Stacking集成模型在用户流失预测中具有较高的有效性,并通过分析识别了影响用户流失的关键因素,为电信运营商提供了减少客户流失的针对性建议,进而提升企业收益和利润。With the rapid advancement of information technology, the telecommunications market is becoming increasingly saturated, making customer churn a critical issue that telecom operators must address urgently. This paper conducts an in-depth predictive analysis of customer churn trends based on user data from Telecom. Initially, missing data was imputed, and feature encoding and derivation were performed. The SMOTE and Tomek Link techniques were employed to address the problem of data imbalance. Following this, six individual models—Random Forest, XGBoost, SVM, Logistic Regression, AdaBoost, and GBDT—were used to predict customer churn. To improve the accuracy and robustness of the predictions, this study applied the Stacking ensemble learning approach. The model comparison results indicate that the second-layer model using SVM achieved the highest accuracy (0.8645), with performance metrics surpassing those of the individual models. The study demonstrates the effectiveness of the Stacking ensemble model in predicting customer churn and identifies the key factors influencing churn through detailed analysis. These findings provide telecom operators with targeted recommendations to reduce customer churn and enhance corporate revenue and profitability.展开更多
心脏病对人体的危害极大,甚至会危及人们的生命。相比于医院检测,使用机器学习方法预测心脏病,可以节约大量的时间。本文以Kaggle心脏病数据集中的1025条真实心脏病数据为例,分析了引起心脏病的相关因素,并构建了K近邻、决策树、随机森...心脏病对人体的危害极大,甚至会危及人们的生命。相比于医院检测,使用机器学习方法预测心脏病,可以节约大量的时间。本文以Kaggle心脏病数据集中的1025条真实心脏病数据为例,分析了引起心脏病的相关因素,并构建了K近邻、决策树、随机森林、逻辑回归四种不同的分类算法模型,对心脏病进行预测。以混淆矩阵、准确率、召回率、精确率、ROC曲线和AUC值作为模型的评价指标,发现K近邻和随机森林的预测效果更好,从而为心脏病预测和诊断提供了有效的科学依据。Heart disease poses great harm to the human body, even endangering people’s lives. Compared to hospital testing, using machine learning methods to predict heart disease can save a lot of time. This article takes 1025 real heart disease data in the Kaggle heart disease dataset as examples to analyze the relevant factors that cause heart disease, and constructs four different classification algorithm models: K-nearest neighbor, decision tree, random forest, and logistic regression to predict heart disease. Using confusion matrix, accuracy, recall, precision, ROC curve, and AUC value as evaluation indicators for the model, it was found that K-nearest neighbor and random forest had better prediction performance, providing an effective scientific basis for heart disease prediction and diagnosis.展开更多
文摘随着信息化建设的迅速推进,电信市场趋于饱和,如何应对用户流失成为通信运营商亟待解决的问题。本文基于电信用户数据,对用户流失趋势进行了深入预测分析。首先,针对数据缺失进行了填补,并对特征进行编码和衍生,使用SMOTE与Tomek Link技术处理了数据不均衡问题。接着,本文使用随机森林、XGBoost、SVM、逻辑回归、AdaBoost和GBDT六种单一模型分别进行用户流失预测。为了提高预测的准确性和稳健性,本文采用了Stacking多模型融合的方式,模型对比结果表明,第二层模型选用SVM达到了最高的准确率(0.8645),各项指标均优于单一模型。研究证明,Stacking集成模型在用户流失预测中具有较高的有效性,并通过分析识别了影响用户流失的关键因素,为电信运营商提供了减少客户流失的针对性建议,进而提升企业收益和利润。With the rapid advancement of information technology, the telecommunications market is becoming increasingly saturated, making customer churn a critical issue that telecom operators must address urgently. This paper conducts an in-depth predictive analysis of customer churn trends based on user data from Telecom. Initially, missing data was imputed, and feature encoding and derivation were performed. The SMOTE and Tomek Link techniques were employed to address the problem of data imbalance. Following this, six individual models—Random Forest, XGBoost, SVM, Logistic Regression, AdaBoost, and GBDT—were used to predict customer churn. To improve the accuracy and robustness of the predictions, this study applied the Stacking ensemble learning approach. The model comparison results indicate that the second-layer model using SVM achieved the highest accuracy (0.8645), with performance metrics surpassing those of the individual models. The study demonstrates the effectiveness of the Stacking ensemble model in predicting customer churn and identifies the key factors influencing churn through detailed analysis. These findings provide telecom operators with targeted recommendations to reduce customer churn and enhance corporate revenue and profitability.
文摘心脏病对人体的危害极大,甚至会危及人们的生命。相比于医院检测,使用机器学习方法预测心脏病,可以节约大量的时间。本文以Kaggle心脏病数据集中的1025条真实心脏病数据为例,分析了引起心脏病的相关因素,并构建了K近邻、决策树、随机森林、逻辑回归四种不同的分类算法模型,对心脏病进行预测。以混淆矩阵、准确率、召回率、精确率、ROC曲线和AUC值作为模型的评价指标,发现K近邻和随机森林的预测效果更好,从而为心脏病预测和诊断提供了有效的科学依据。Heart disease poses great harm to the human body, even endangering people’s lives. Compared to hospital testing, using machine learning methods to predict heart disease can save a lot of time. This article takes 1025 real heart disease data in the Kaggle heart disease dataset as examples to analyze the relevant factors that cause heart disease, and constructs four different classification algorithm models: K-nearest neighbor, decision tree, random forest, and logistic regression to predict heart disease. Using confusion matrix, accuracy, recall, precision, ROC curve, and AUC value as evaluation indicators for the model, it was found that K-nearest neighbor and random forest had better prediction performance, providing an effective scientific basis for heart disease prediction and diagnosis.
