哈尔滨市区域产业链点发展评估数据

我司基于哈尔滨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哈尔滨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，形成涵盖哈尔滨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 Harbin and combined with the availability and validity of data, constructed a comprehensive development efficiency evaluation system for regional industrial chain nodes in Harbin from multiple dimensions including industrial scale, leading enterprise effect, capital enthusiasm, technological innovation and development efficiency. We completed the entire workflow of data collection, data cleaning, feature derivation, model construction and model validation, thus forming the development scores of regional industrial chain nodes covering the core nodes under the main industrial chains in Harbin. This dataset 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, thereby assisting financial institutions in delivering better regional industrial financial services. The development index of regional industrial chain nodes is calculated as ∑ Si * Xi, where Si represents the corresponding weight coefficient of each indicator, Xi represents the evaluation indicator, and i ranges from 1 to 21. The 21 indicators include "the number of operating enterprises in regional industrial chain nodes", "the number of newly registered enterprises in regional industrial chain nodes in the past year", etc. All indicators are normalized prior to calculation. The model is developed by integrating expert experience and machine learning algorithms. Experts primarily screen key indicators and preliminarily set the weight range based on industry practices, policy orientations and international trade characteristics, and then further screen indicators and optimize the initial weights through principal component analysis and random forest algorithms.

搜集汇总

背景与挑战

背景概述

该数据集为哈尔滨市区域产业链点发展评估数据，包含1000条记录，每周更新，涵盖产业链点的产业规模、龙头效应、资本热度等多个维度，用于金融机构评估区域产业发展效能。