视频简介
This video shows the student project reports of the 11th Australian Business Data Analysis course. Three teams conducted data mining and modeling analysis on traffic accident data, bank marketing strategies and business analysis. The first group, Noble Team, studied traffic accident data in Queensland from 2001 to 2021, used SQL and Python for data exploration, and applied the SARIMA model to predict future trends, revealing the impact of the epidemic blockade on the accident rate. The second group, Team Lowbox, focused on bank marketing data, using random forest and neural network models to analyze customer behavior and identify key customer characteristics that may subscribe to term deposits. The third group, Team QTM Solution, studied the relationship between driver demographic characteristics and accident severity, built a visual dashboard, and used a decision tree classifier to make recommendations for optimizing traffic safety. The speech emphasized the importance of data preprocessing, model selection and optimization, and explored the application of data analysis in business decision-making and social governance. 该视频展示了澳洲商业数据分析课程第 11 期的学生项目汇报，三组团队分别围绕交通事故数据、银行营销策略和商业分析进行数据挖掘和建模分析。第一组 Noble Team 研究了昆士兰 2001 至 2021 年的交通事故数据，利用 SQL 和 Python 进行数据探索，并应用 SARIMA 模型预测未来趋势，揭示了疫情封锁对事故发生率的影响。第二组 Team Lowbox 关注银行营销数据，使用随机森林和神经网络模型分析客户行为，识别可能订阅定期存款的关键客户特征。第三组 Team QTM Solution 研究了司机人口特征与事故严重程度的关系，构建可视化仪表盘，并采用决策树分类器提出优化交通安全的建议。演讲强调了数据预处理、模型选择及优化的重要性，同时探讨了数据分析在商业决策和社会治理中的应用。
