河南省网格化机会挖掘数据

将河南省所有街道（镇）称作网格，共计2581个网格。创建每个网格的宏观经济、商业氛围、厨电行业、住宅区等维度的数据集。创建并训练机器学习、深度学习算法模型。输出每个网格是否应该开店，开多少家店，并输出河南省所有网格对开店机会的预测评分；实地操盘手可根据预测评分高低，进行网格化工作策略制定、资源分配优化及策略纠偏，同行业也可通过网格化机会挖掘算法预测开店机会。1、数据收集：整理并清洗河南省所有网格（2581街道/镇）的前一年份第三产业GDP、洗衣店、集成灶、烟酒专卖店、房产中介、竞品、宠物诊所、商场、住宅区、区县_乡村总数量、格均社消额、格均土地面积等12个维度的所有数据。 2、模型建立：针对有店的网格数据，建立RF（随机森林）、LGBM（梯度提升决策树）、MLP（人工神经网络）模型并进行集成学习，训练出12个维度与店之间的得分模型：网格得分=前一年份第三产业GDP*7.5%+洗衣店*13.9%+集成灶*6.6%+烟酒专卖店*6.2%+房产中介*1.7%+竞品*30.1%+宠物诊所*13.7%+商场*7.0%+住宅区*2.4%+区县_乡村总数量*3.8%+格均社消额*2.8%+格均土地面积*4.3% 3、通过训练好的模型对全部网格进行预测评分并输出无店网格的开店机会及空间： 网格得分<51，则无开店机会；50<网格得分<108，则有1个开店机会；107<网格得分<164，则有2个开店机会；网格得分>163，则有多个开店机会。All streets and towns in Henan Province are defined as grids, with a total of 2581 grids. Datasets covering dimensions such as macroeconomics, business atmosphere, kitchen appliance industry and residential areas are constructed for each individual grid. Machine learning and deep learning algorithm models are developed and trained. The output includes whether each grid is suitable for opening stores, the number of stores to be opened, and the predicted store opening opportunity scores for all grids in Henan Province. On-site operators can formulate grid-based work strategies, optimize resource allocation and adjust strategies based on the predicted scores; peers in the same industry can also predict store opening opportunities via the grid-based opportunity mining algorithm. 1. Data Collection: Sort out and clean data across 12 dimensions for all 2581 grids (streets/towns) in Henan Province from the previous year, including the previous year's tertiary industry GDP, number of laundromats, integrated stoves, tobacco and wine specialty stores, real estate agencies, competitive stores, pet clinics, shopping malls, residential areas, total number of districts/counties and rural areas, per-grid social consumer goods retail sales, and per-grid land area. 2. Model Establishment: For grid data with existing stores, establish RF (Random Forest), LGBM (Light Gradient Boosting Machine) and MLP (Multilayer Perceptron, Artificial Neural Network) models, and conduct ensemble learning to train a scoring model linking the 12 dimensions to store openings: Grid Score = Previous year's tertiary industry GDP × 7.5% + Number of laundromats × 13.9% + Number of integrated stoves × 6.6% + Number of tobacco and wine specialty stores × 6.2% + Number of real estate agencies × 1.7% + Number of competitive stores × 30.1% + Number of pet clinics × 13.7% + Number of shopping malls × 7.0% + Number of residential areas × 2.4% + Total number of districts/counties and rural areas × 3.8% + Per-grid social consumer goods retail sales × 2.8% + Per-grid land area × 4.3% 3. Predict and score all grids using the trained model, and output store opening opportunities and space for grids without stores: If Grid Score < 51, there is no store opening opportunity; if 50 < Grid Score < 108, there is 1 store opening opportunity; if 107 < Grid Score < 164, there are 2 store opening opportunities; if Grid Score > 163, there are multiple store opening opportunities.

搜集汇总

数据集介绍

特点

河南省网格化机会挖掘数据集包含2581个网格的12个维度数据，用于机器学习模型预测开店机会，适用于制造业的资源分配和策略优化。