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所在平台: Udemy |
课程主页: https://www.udemy.com/course/logistic-regression-decision-tree-and-neural-network-in-r/
课程评论：没有评论
本课程将介绍两种数据分析技术：描述性统计和预测性分析。 在预测性分析部分，我们将重点关注如何在 R 语言中实现逻辑回归模型、决策树和神经网络。 此外，我们还将学习如何解读分析结果、计算预测准确率以及构建混淆矩阵。 课程结束后，您将能够： * 有效地总结和可视化您的数据。 * 检测并处理缺失值。 * 运用上述分析技术预测未来结果。 * 构建混淆矩阵。 * 导入和导出数据。
In this course, we cover two analytics techniques: Descriptive statistics and Predictive analytics. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. We well also see how to interpret our result, compute the prediction accuracy rate, then construct a confusion matrix. By the end of this course , you will be able to effectively summarize your data , visualize your data , detect and eliminate missing values, predict futures outcomes using analytical techniques described above , construct a confusion matrix, import and export a data.
