PCA

说明：  

利用python进行数据分析，英文书籍，从pandas库的数据分析工具开始利用高性能工具对数据进行加载、清理、转换、合并以及重塑；利用matpIotlib创建散点图以及静态或交互式的可视化结果；利用pandas的groupby功能对数据集进行切片、切块和汇总操作；处理各种各样的时间序列数据。(Starting from the data analysis tools of pandas database, high performance tools are used to load, clean, transform, merge and remodel data; scatter plots and static or interactive visualization results are created by matpIotlib; data sets are sliced, sliced and aggregated by pandas group by function; and various operations are processed. Time series data.)

说明：  通过时事数据可视化系统，可以清楚地了解全球疫情分布的状况以及密度，以便做出相应的对策(Through the current affairs data visualization system, it is possible to clearly understand the distribution and density of the global epidemic in order to make corresponding countermeasures)

资源描述决策树是建立在信息论基础之上，对数据进行分类挖掘的一种方法。其思想是，通过一批已知的训练数据建立一棵决策树，然后利用建好的决策树，对数据进行预测。决策树的建立过程可以看成是数据规则的生成过程。由于基于决策树的分类方法结构简单，本身就是人们能够理解的规则。其次，决策树方法计算复杂度不大，分类效率高，能够处理大数据量的训练集；最后，决策树方法的分类精度较高，对噪声数据有较好的健壮性，符合一般系统的要求。

GWR能够实现地理加权回归计算，里面有GWR4操作说明，虽然是英文，但有图片介绍，很容易理解，是GWR模型入门的好工具，适合经济学、数据挖掘等人员使用(GWR can realize geographically weighted regression calculation, including GWR4 operation instructions. Although it is in English, it has pictures to introduce, and is easy to understand. It is a good tool for GWR model entry. It is suitable for personnel such as economics and data mining.)

基于混合信号的小波包分解技术在故障特征提取中的应用(Feature Extraction Using Multisignal Wavelet Packet Decomposition)

说明：  简单的LSTM进行预测，附带数据集方便测试(simple test of LSTM is used for prediction , and related datasets is attached in the file.)

基于P-Q分解法的快速潮流计算,可用于实际系统()

说明：  深度学习框架Keras入门资料，里面的代码包括课件和DEMO有利于新书入门学习，简单易懂(Keras Introductory Information of Deep Learning Framework, which includes courseware and DEMO, is helpful for introductory learning of new books. It is easy to understand.)

说明：  boxcox函数的python实现，引用该函数可将偏态分布调整为正态分布(Python implementation of box Cox function)