The customer churn prediction (CCP) is one of the challenging problems in the telecom industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to predict customer churn has increased significantly. Our proposed methodology, consists of six phases. In the first two phases, data pre-processing and feature analysis is performed. In the third phase, feature selection is taken into consideration using gravitational search algorithm. Next, the data has been split into two parts train and test set in the ratio of 80% and 20% respectively. In the prediction process, most popular predictive models have been applied, namely, logistic regression, naive bayes, support vector machine, random forest, decision trees, etc. on train set as well as boosting and ensemble techniques are applied to see the effect on accuracy of models. In addition, K-fold cross validation has been used over train set for hyperparameter tuning and to prevent overfitting of models. Finally, the obtained results on test set have been evaluated using confusion matrix and AUC curve. It was found that Adaboost and XGboost Classifier gives the highest accuracy of 81.71% and 80.8% respectively. The highest AUC score of 84%, is achieved by both Adaboost and XGBoost Classifiers which outperforms over others.

客户流失预测（CCP）是电信行业中具有挑战性的问题之一。随着机器学习和人工智能领域的发展，预测客户流失的可能性大大增加。我们提出的方法包括六个阶段。在前两个阶段中，执行数据预处理和特征分析。在第三阶段，使用重力搜索算法考虑特征选择。接下来，将数据分为两个部分，分别是训练集和测试集，比例分别为80％和20％。在预测过程中，已应用了最流行的预测模型，即逻辑回归，朴素贝叶斯，支持向量机，随机森林，决策树等。在火车上使用模型以及增强和合奏技术来观察对模型准确性的影响。此外，K列交叉验证已在训练集上用于超参数调整并防止模型过度拟合。最后，使用混淆矩阵和AUC曲线评估了在测试集上获得的结果。发现Adaboost和XGboost分类器的最高准确度分别为81.71％和80.8％。Adaboost和XGBoost分类器的AUC得分最高，达到84％，其表现优于其他分类器。发现Adaboost和XGboost分类器的最高准确度分别为81.71％和80.8％。Adaboost和XGBoost分类器的AUC得分最高，达到84％，其表现优于其他分类器。发现Adaboost和XGboost分类器的最高准确度分别为81.71％和80.8％。Adaboost和XGBoost分类器的AUC得分最高，达到84％，其表现优于其他分类器。