knn_svm_tree

说明：  对于iris数据集的knn、svm、决策树算法分类的简单实现(Simple implementation of KNN, SVM and decision tree algorithm classification for iris dataset)

说明：  深入理解Spark核心技术书籍，书籍对spark进行了深入讲解，并对spark源码进行了剖析(In-depth understanding of Spark core technology books, books on Spark in-depth explanation, and analysis of spark source code)

用Apriori算法挖掘出入侵检测数据集KDD99的数据关联性，从而检测出未知的攻击(Apriori algorithm is used to extract the data association of the intrusion detection data set KDD99, and the unknown attack is detected)

一个xgboost实现的回归模型预测，数据集来源于kaggle的taxi竞赛(Regression model prediction based on a xgboost implementation)

追敢法,主要是n阶线形方程组Ax b的列主元高斯消去法的一个特殊情况,为上三角矩阵,通用程序如附件所示()

c语言求解四阶龙格库塔法的算法源代码,例子：某一地区的病菌传染,三种人员的人数的状态方程,即可能受传染的人数x1,已被传染(C language to solve the fourth order Runge-Kutta algorithm source code, example: a certain area of bacterial infection, the number of three kinds of personnel equation of state, that is, the number of people who may be infected x 1, has been infected)

Python是一种面向对象、解释型计算机程序设计语言，其应用领域非常广泛，包括数据分析、自然语言处理、机器学习、科学计算以及推荐系统构建等。 本书用Python语言来讲解算法的分析和设计。本书主要关注经典的算法，但同时会为读者理解基本算法问题和解决问题打下很好的基础。(Python is an object-oriented, interpretive computer programming language. It has a wide range of applications, including data analysis, natural language processing, machine learning, scientific computing and recommendation system construction. This book uses Python language to explain the analysis and design of algorithms. This book focuses on classical algorithms, but at the same time it will lay a good foundation for readers to understand basic algorithms and solve problems. The book consists of 11 chapters. The tree, graph, counting problem, inductive recursion, traversal, decomposition and merging, greedy algorithm, complex dependency, Dijkstra algorithm, matching and cutting problem, difficult problem and its dilution are introduced. The book has exercises and reference materials at the end of each chapter, which provides readers with more convenience for self-examination and further stu)

一些常见算法实现和牛客网以及leetcode刷题(Some Common Algorithms Implementing and Title Brushing)

本文件封装好K-MEANS算法，只需要调用函数就可以使用

Python机器学习及实践，第二章到第四章源码(Python machine learning and Practice)