外卖定向选点建店选址分析模型

将平台产生的数据清洗加工后（如：剔除重复&异常数据，类目映射等），采用随机森林回归模型，输入人流量、竞争门店数量，输出订单量预测值。数据预处理阶段通过地理围栏技术划分网格，清洗无效定位（误差＞50米），记录不同时段人群访问量（用户注册账号授权后，平台记录用户ID，当用户访问了店铺后，平台自动记录为1个流量），再根据订单预测值，竞争门店数量，测算出新店开业后的店铺销量，商家再结合店铺租金，评估新店开业后的盈利情况，判断是否在此区域开店。

After cleaning and processing the data generated by the platform (e.g., removing duplicate and abnormal data, category mapping, etc.), a Random Forest Regression model is adopted, which takes foot traffic and the number of competing stores as inputs and outputs the predicted order volume. In the data preprocessing stage, grids are divided using geofencing technology, and invalid positioning with an error exceeding 50 meters is cleaned out. The visitor volume at different time periods is recorded: after users register their accounts and grant authorization, the platform records their user IDs, and when a user visits a store, the platform automatically counts this as 1 unit of traffic. Then, based on the predicted order volume and the number of competing stores, the projected sales of the new store after its opening are calculated. Combining with the store rent, merchants evaluate the profitability of the new store after opening and determine whether to open a store in this area.

搜集汇总

背景与挑战

背景概述

该数据集名为'外卖定向选点建店选址分析模型'，由贵州电子商务云运营有限责任公司自行产生，数据规模为5GB，更新周期为月。数据集通过分析外卖平台的匿名化人流量定位信息和历史订单数据，为连锁餐饮品牌提供选址决策支持，包括识别高潜力商圈和量化人流-订单转化率。