临平区企业人员招聘需求预测数据

本数据集提供了临平区内企业的人员招聘需求预测，旨在为以下应用场景提供决策支持：1.人力资源规划：临平区内的企业可以利用本数据预测未来的人员需求，进行人力资源规划和招聘策略的制定。2.市场研究：分析人员可以依据本数据了解临平区企业的招聘趋势，为市场研究提供宏观数据支持。3.政府就业政策：临平区的政府机构可以利用本数据来评估区域就业市场的健康状况，制定或调整相关就业政策。4.投资者决策：投资者可以依据本数据评估企业的发展潜力和人才需求，作为投资决策的参考。1.数据采集：通过权威官方平台和四大招聘网站（智联招聘/猎聘网/前程无忧/Boss直聘），检索收集或统计临平区企业的人力资源信息，包括企业名称、2021年底参保人数、2022年底参保人数、2023年底参保人数、当前企业在四大平台合计发布招聘信息数量；另采集最近2期全国城镇调查失业率数据。
2.数据预处理：（1）利用唯一标识符对企业名称进行脱敏；（2）计算近三年参保人数的复合增长率：（2023年底参保人数-2021年底参保人数）÷2021年底参保人数×100%；（3）基准招聘人数β0=当前企业在四大平台合计发布招聘信息的数量÷4；（4）计算全国城镇调查失业率增长值：上期全国城镇调查失业率-上上期全国城镇调查失业率。
3.预测模型构建：使用多元线性回归模型，将基准招聘人数、近三年参保人数复合增长率作为自变量，预测2025年度预测新增招聘人数；其中，全国城镇调查失业率增长率用于给近三年参保人数复合增长率的模型系数进行调整；具体公式为：2025年度预测新增招聘人数=β0+[β1×（1-全国城镇调查失业率增长值）]×近三年参保人数复合增长率；模型系数β0、β1通过历史数据训练得到，不定期调整。

This dataset provides recruitment demand forecasting for enterprises within Linping District, aiming to provide decision support for the following application scenarios:
1. Human Resource Planning: Enterprises in Linping District can use this data to forecast future personnel needs, conduct human resource planning and formulate recruitment strategies.
2. Market Research: Analysts can use this data to understand the recruitment trends of enterprises in Linping District, providing macro-level data support for market research.
3. Government Employment Policies: Government agencies in Linping District can use this data to assess the health of the regional employment market and formulate or adjust relevant employment policies.
4. Investor Decision-making: Investors can use this data to evaluate the development potential and talent demand of enterprises, serving as a reference for investment decisions.

1. Data Collection: Retrieve, collect and count human resource information of enterprises in Linping District through official authoritative platforms and four major recruitment websites (Zhaopin, Liepin, 51job, BOSS Zhipin), including enterprise name, number of insured employees at the end of 2021, number of insured employees at the end of 2022, number of insured employees at the end of 2023, and the total number of job postings released by each enterprise on the four platforms; additionally, collect the latest two rounds of national urban survey unemployment rate data.
2. Data Preprocessing: (1) Desensitize enterprise names using unique identifiers; (2) Calculate the compound growth rate of insured employees over the past three years: (Number of insured employees at the end of 2023 - Number of insured employees at the end of 2021) ÷ Number of insured employees at the end of 2021 × 100%; (3) Benchmark recruitment headcount β₀ = Total number of job postings released by the current enterprise on the four platforms ÷ 4; (4) Calculate the growth value of the national urban survey unemployment rate: National urban survey unemployment rate of the previous period - National urban survey unemployment rate of the period before the previous one.
3. Prediction Model Construction: Adopt a multiple linear regression model, taking benchmark recruitment headcount and the compound growth rate of insured employees over the past three years as independent variables, to predict the predicted new recruitment headcount for 2025; specifically, the national urban survey unemployment rate growth rate is used to adjust the model coefficient of the compound growth rate of insured employees over the past three years; the specific formula is: Predicted new recruitment headcount in 2025 = β₀ + [β₁ × (1 - National urban survey unemployment rate growth value)] × Compound growth rate of insured employees over the past three years; The model coefficients β₀ and β₁ are trained using historical data and adjusted irregularly.

搜集汇总

数据集介绍

特点

该数据集包含临平区企业的人员招聘需求预测数据，涵盖1508条记录，按需更新。数据用于支持人力资源规划、市场研究、政府就业政策和投资者决策等应用场景，并通过多元线性回归模型预测招聘需求。