AccidentPredictor:准确的交通事故预测因子可以潜在地挽救生命。 我在此描述了使用scikit-learn,Google Maps API,Flask和PythonAnywhere创建和部署这种模型的过程-源码

上传者: 42099087 | 上传时间: 2021-09-28 21:25:02 | 文件大小: 17.79MB | 文件类型: ZIP
CSE6242数据和视觉分析(2019年Spring) 第74小组-道路交通事故预测Web应用程序 项目说明 在此项目中,我们着手提供一种预测RTA风险的工具,以解决英国伦敦的道路交通事故(RTA)问题,从而使用户可以做出有关其行驶路线的明智决定。 我们还对过去的数据和图像进行了详细分析,以更好地了解RTA。 我们的Web界面包含两个部分,即探索和交互。 在探索部分,我们介绍了我们的研究方法,所使用的算法,数据的分析和可视化。 在交互部分中,用户可以利用交互式仪表板来预测他们选择的路线中RTA的可能性。 该应用程序使用机器学习模型进行预测。 用户将必须输入日期和时间,出发地和旅行目的地。 然后,该应用程序将调用进行路线规划和天气API,进行天气预报。 这些数据随后将被输入到模型中,并且在用户路线中出现RTA的可能性将在地图上显示为危险图标。 该Web应用程序是使用Python Fla

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