The Sugarcane Price Prediction System Research Based On Spark MLlib Regression Algorithm

As an important agricultural product in China's agricultural field,sugar cane is not only an important raw material for sugar production,but also a new type of energy material.In many provinces of China,sugarcane plantation has become the main source of income for local people and the basic industry that drives economic growth on the one hand.Sugar cane price fluctuations have an important impact on the national tax revenue and the basic income and production enthusiasm of the majority of sugar cane farmers,and even have an important significance for social stability and international sugar market stability.This article explores the inherent relationship and laws between the factors affecting sugar cane price fluctuations by mining data related to sugar cane price fluctuations,establishing a sugar cane price fluctuation index system,and establishing a price prediction model based on the Spark MLlib regression algorithm to implement a sugar cane price prediction system,With a view to providing guidance for the production and sales of sugarcane production areas in China.The main research work and contributions of this article are as follows:(1)Research on the influencing factors of sugarcane price and the law of price fluctuation.From the perspectives of socioeconomics and sales,this paper analyzes the correlation of sugar cane price influencing factors based on the production cost,circulation cost,inherent attribute characteristics and sales market of sugar cane,and proposes a sugar cane price fluctuation index system.(2)Data set acquisition and preprocessing.This article uses the Python language to write a scrapy framework to obtain the sugarcane data characteristics.Through the specific implementation of the crawler program,the target data in the relevant website is downloaded,parsed and saved,and the data dimension and correlation analysis of the obtained data set are performed.And data pre-processing such as data set attribute division.(3)Research on sugarcane price prediction model.Adopt the method of data-driven modeling,use linear and nonlinear representative regression algorithms with full samples to continuously select samples and model training and optimization,and finally obtain the optimal samples and matches that are most closely related to sugar cane price fluctuation The algorithm model K nearest neighbor algorithm.(4)Implementation of sugar cane price prediction system based on Spark MLlib.The sugar cane price prediction system is designed as two modules: the client and the server.The client writes a graphical interface through Python.The interface functions include configuring server information and querying prediction results.The server provides core computing functions.It receives the optimal sample files and data models uploaded by the client to perform calculations to obtain prediction results.The development of sugar cane price prediction system has been completed through the specific realization of these two functions.The application results show that the system has high prediction accuracy and can be applied to sugar cane price prediction,which is convenient for demanders to grasp their price information in time and promote the good development of the sugar cane industry.