南通市区域产业链点发展评估数据

我司基于南通市区域产业发展的特征，结合数据的可获得性和有效性，基于不同产业链点的产业规模、龙头效应、资本热度、科技创新、发展效率等多个维度构建南通市区域产业链点的综合发展效能评估体系，并完成数据收集、数据清洗、特征衍生、模型构建、模型验证全过程，形成涵盖南通市主要产业链下核心链点的区域产业链点发展评分，能有效帮助金融机构在制定产业相关信贷政策以及确定特定产业预授信额度时，更准确全面地评价区域产业的发展效能和动态变化，辅助金融机构更好做好区域产业金融服务。区域产业链点发展指数 = ∑ Si * Xi ，其中Si是指标相应的权重系数，Xi是评价指标，i=1,2,3,……,21，21个指标包括“区域产业链点在营企业数量”“区域产业链点近1年新注册企业数量”等，指标经归一化处理后参与计算。模型结合专家经验和机器学习算法得出，专家主要基于行业实践、政策导向和国际贸易特点，帮助筛选关键指标并初步设定权重范围，然后通过主成分分析和随机森林算法进一步筛选指标并对初始权重进行优化。

Our company, based on the characteristics of regional industrial development in Nantong and combined with data availability and validity, constructed a comprehensive development efficiency evaluation system for regional industrial chain nodes in Nantong from multiple dimensions including industrial scale, leading firm effect, capital enthusiasm, technological innovation and development efficiency of different industrial chain nodes. We completed the entire process of data collection, data cleaning, feature derivation, model construction and model validation, and generated development scores of regional industrial chain nodes covering core nodes under Nantong's major industrial chains. This system can effectively help financial institutions accurately and comprehensively evaluate the development efficiency and dynamic changes of regional industries when formulating industrial-related credit policies and determining pre-credit limits for specific industries, so as to assist financial institutions in providing better regional industrial financial services. The development index of regional industrial chain nodes is calculated as: Regional Industrial Chain Node Development Index = ∑ Si * Xi, where Si is the corresponding weight coefficient of each indicator, Xi is the evaluation indicator, and i=1,2,3,…,21. The 21 indicators include "number of operating enterprises at regional industrial chain nodes", "number of newly registered enterprises at regional industrial chain nodes in the past year" and others, and all indicators are normalized before being used in the calculation. The model was developed by combining expert experience and machine learning algorithms. Experts mainly helped screen key indicators and initially set the weight range based on industry practices, policy orientations and international trade characteristics, and then applied principal component analysis and random forest algorithms to further screen indicators and optimize the initial weights.

搜集汇总

背景与挑战

背景概述

该数据集为南通市区域产业链点发展评估数据，包含产业链名称、核心链点名称、发展指数、企业数量、融资情况等26个字段，数据规模为1000条，每周更新。主要用于评估区域产业链的发展效能，辅助金融机构制定产业相关信贷政策。