以客户是否购买新车型为预测目标,对比分析逻辑回归（LR）、K近邻（KNN）、高斯朴素贝叶斯（GaussianNB）和支持向量机（SVM）四种分类算法的性能。基于汽车消费相关数据,经数据预处理后,针对各模型开展超参数调优：为逻辑回归优化正则化参数与惩罚项;对于 KNN 算法,需调整近邻数量并选择适宜的距离度量方式;对于高斯朴素贝叶斯,则要验证特征独立性假设的适配程度并进行相应微调;为SVM优化核函数、正则化系数等关键参数。经训练与测试评估,调优后SVM的综合表现突出,AUC达0.965,预测准确率为93%;KNN、高斯朴素贝叶斯AUC均为0.963,准确率均为93%;逻辑回归AUC为0.955,准确率为91%。研究表明,SVM在非线性数据拟合上优势显著,适用于汽车消费预测场景,为企业精准制定营销策略提供有力的数据支持和决策依据。

Focusing on the prediction of customers' new car model purchase behavior, this paper carries out a comparative analysis on the performance of four classification algorithms: Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GaussianNB), and Support Vector Machine (SVM). Based on data related to car consumption, following data preprocessing, hyperparameter tuning is performed for each model. For Logistic Regression, the regularization parameters and penalty terms are optimized. For the KNN algorithm, the number of neighbors is adjusted, and an appropriate distance measurement method is selected. For Gaussian Naive Bayes, the adaptability of the feature independence assumption is verified, and corresponding fine-tuning is carried out. Optimization is performed on key parameters of SVM, including the kernel function and regularization coefficient. After training and testing evaluation, the SVM demonstrates outstanding comprehensive performance after tuning, with an AUC reaching 0.965 and a prediction accuracy of 93%. Both KNN and Gaussian Naive Bayes achieve an AUC of 0.963 and an accuracy of 93%, while Logistic Regression has an AUC of 0.955 and an accuracy of 91%. The research shows that SVM has significant advantages in fitting nonlinear data, is suitable for the car consumption prediction scenario, and provides powerful data support and decision basis for enterprises to develop targeted marketing strategies.