幸福

新手第一次使用数据，用于在社会科学领域，幸福感的研究占有重要的位置。这个涉及了哲学、心理学、社会学、经济学等多方学科的话题复杂而有趣；同时与大家生活息息相关，每个人对幸福感都有自己的衡量标准。如果能发现影响幸福感的共性，生活中是不是将多一些乐趣；如果能找到影响幸福感的政策因素，便能优化资源配置来提升国民的幸福感。目前社会科学研究注重变量的可解释性和未来政策的落地，主要采用了线性回归和逻辑回归的方法，在收入、健康、职业、社交关系、休闲方式等经济人口因素；以及政府公共服务、宏观经济环境、税负等宏观因素上有了一系列的推测和发现。

赛题尝试了幸福感预测这一经典课题，希望在现有社会科学研究外有其他维度的算法尝试，结合多学科各自优势，挖掘潜在的影响因素，发现更多可解释、可理解的相关关系。

For first-time users of this dataset, happiness research holds a pivotal position in the field of social sciences. This topic, which spans multiple disciplines including philosophy, psychology, sociology, economics and others, is both complex and intriguing; it is also closely tied to people’s daily lives, as every individual has their own criteria for measuring happiness. If we can identify the common factors that influence happiness, our lives could become more enjoyable; if we can uncover policy-related factors affecting happiness, we will be able to optimize resource allocation to enhance national well-being. Current social science research focuses on the interpretability of variables and the implementation of future policies, primarily adopting linear regression and logistic regression methods. A series of inferences and findings have been made regarding socioeconomic and demographic factors such as income, health, occupation, social relationships, and leisure activities, as well as macro-level factors including government public services, macroeconomic environment, and tax burden.

This competition problem targets the classic topic of happiness prediction, aiming to explore algorithmic attempts from other dimensions beyond existing social science research, combine the respective advantages of multiple disciplines, excavate potential influencing factors, and discover more interpretable and understandable correlational relationships.

搜集汇总

背景与挑战

背景概述

该数据集专注于社会科学领域的幸福感预测研究，基于中国综合社会调查（CGSS）的截面面访数据，包含经济人口和宏观等多维度变量。数据提供完整版和精简版两种格式，用于分析影响幸福感的因素，并支持线性回归等方法的算法尝试。