|
|
农产品的价格波动更多的是因为在某一个地区某一个季节，某一种蔬菜的价格突然因为一些外在的因素而提高。而这样就导致了很多的人跟风，而这一情况必然导致下一季度这种蔬菜的价格必然出现下跌，这样就会出现很多的农户或者商家赚不到钱。准确地掌握作物历史产量的性质是建模输入的重要依据，有助于农民和政府组织决策过程中制定适当的政策。在计算和信息存储的数据提供了一个最广阔的进展。面临的挑战是从原始数据中提取知识，这就导致了新的方法和技术，如数据挖掘，可以将数据的知识与作物估产结合起来。
本文通过在农产品（蔬菜）的数据挖掘数据处理中，我们首先要寻找到足够的大量的农产品相关的数据信息的来源，因为拥有大量的数据信息是数据挖掘和数据处理的基础。其次是我们要做一些数据的准备：选择数据，就是确定待挖掘的数据的目标；数据预处理：研究数据的质量，确定将要进行的数据类型；数据转换：就是转换成一个分析模型。然后进行数据的挖掘：选择合适的挖掘算法。最后就是结果分析，主要是对提取的数据信息的可靠性、有效性等进行评估。
关键词：农产品；爬虫；数据挖掘；Python
Prices of agricultural products fluctuate more because， in a certain region and season， the price of a certain vegetable suddenly rises because of some external factors. This has led to a lot of people following the trend， and this is bound to lead to the next quarter of the vegetable prices are bound to fall， so there will be many farmers or businesses can not make money. Accurate understanding of the nature of crop historical yield is an important basis for modeling input， and it is helpful for farmers and government organizations to make appropriate policies in the decision-making process. The data in computing and information storage provides one of the broadest advances. The challenge is to extract knowledge from raw data New methods and techniques， such as data mining， can combine data knowledge with crop yield estimates.
In this paper， through the data mining of agricultural products (vegetables)， we first need to find a large number of agricultural products related to the source of data information， because having a large amount of data information is the basis of data mining and data processing. Secondly， we need to do some data preparation: select data， is to determine the target of the data to be mined; data preprocessing: to study the quality of data， determine the data type to be carried out; data conversion: is to convert into an analysis model. Then carry on the data mining: select the appropriate mining algorithm. Finally， the result analysis is mainly about the reliability of the extracted data information. Evaluate effectiveness， etc.
Key words: agricultural products; crawlers; data mining; Python
目 录
