Research And Implementation On WEB Data Mining Technology Based On Python

With the rapid development of the Internet and the advent of the era of big data,data mining has played an important role in exploring potential value information from massive data,and has become one of the hottest research and practice directions.As a hot programming language in the data mining field,python's rich technology library and powerful scientific computing capabilities have become indispensable tools in the data mining process.This research is mainly based on the Python language data mining and analysis of the data of Zhilian Recruitment Network,and then come up with a classification model for salary information of recruiting information.This study is mainly divided into the following steps: data source selection,data acquisition,data storage,data preprocessing,data modeling,and model evaluation.Two classification models of neighbors and decision trees are built by using algorithms.The matrix data is calculated and compared with the prediction accuracy of the model.Finally,a data model with higher accuracy is obtained.The classification model obtained in this study can help recruiters to predict the level of salary and benefits when browsing website recruitment information,effectively assess whether the recruitment content is appropriate,and whether they are satisfied with the salary and salary provided by the recruitment position,thereby effectively improving the efficient when seeking job opportunities.At the same time,this model also plays a feedback role for enterprises to optimize recruitment information.The company can effectively classify the salary level of existing recruitment information in the market according to the model,so as to obtain the status quo of salary level distribution in different positions in the market,and then make reasonable adjustments to the content of enterprise recruitment,improve the efficiency of enterprise recruitment,save recruitment costs,optimize the talent structure of the enterprise and improve the competitiveness of enterprises in the industry.