基于KNN、贝叶斯和决策树的水质检测

X. Jia
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引用次数: 1

摘要

在这项研究中,我们使用不同的方法分析了水质:描述性分析和机器学习。首先,我们从kaggle网站获取数据源。处理完数据后,使用Python的sklearn包进行数据挖掘。首先,通过描述分析选择机器学习数据挖掘方法。最后,我们选择KNN、贝叶斯算法和决策树对kaggle网站的水数据进行分析。目标是通过机器学习算法将数据划分为可用和不可用。最后,通过这三种方法,得出了三种方法的结果,并进行了相应的比较和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Water Quality Using KNN, Bayesian and Decision Tree
In this study, we have analyzed water quality using different approaches: descriptive analysis and machine learning. First, we get the data source from kaggle website. After processing the data, we use Python sklearn package for data mining. Firstly, the machine learning data mining method is selected through description analysis. Finally, we choose KNN, Bayesian algorithm and decision tree to analyze the water data from kaggle website. The goal is to divide the data into available and unavailable by machine learning algorithm. Finally, through these three methods, we get the results of three methods and make some corresponding comparison and analysis.
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