用机器学习方法预测失业

Moupali Sen, Shreya V. Basu, A. Chatterjee, Anwesha Banerjee, Saheli Pal, Pritam Kumar Mukhopadhyay, Stobak Dutta, Arunabha Tarafdar
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引用次数: 0

摘要

失业是当超过特定年龄的人不从事任何有助于个人和国家经济福利的活动时出现的情况。失业问题日益引起人们的关注,使人们的日常生活变得困难。失业在公民中造成贫穷和抑郁。如今,不同的行业有不同的机会。但人们并没有意识到这些机会。不同的州缺乏熟练劳动力,而许多州有熟练劳动力,但机会较少。自2020年以来失业的另一个原因是COVID-19大流行。我们选择这个话题是为了提高市民的意识。这项工作试图发现迫切需要增加就业机会的印度各邦。我们已经应用了监督机器学习算法的概念来检测就业率最低的状态。数据可视化能更好地反映多年来失业率的趋势。已经使用了不同的流行算法,如逻辑回归,支持向量机,k近邻(kNN)算法和决策树。最后,我们试图找到一种算法,它将给我们提供更多的准确性,以便采取必要的步骤来雇用合格和值得的人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Unemployment using Machine Learning Approach
Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people.
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