-I- 摘要 ND 公司是一家以提供城市出行智能解决方案的高科技创新公司，专注于为 全球出行用户提供更环保、更便捷、更智能的城市出行交通工具。业务主要包括 智能电动自行车的设计、生产、销售等。有效的销售预测能够帮助线下门店管理 者建立高效的工作团队，并帮助门店管理者发现在日常销售中影响客户和团队的 重要影响因素，从而改进生产模式，提高门店的赢利能力。 本文以ND公司748家线下门店的日常销售数据及门店信息数据为研究对象， 对门店销售现状以及现有门店销售数据进行分析，基于 XGBoost 方法预测未来 门店销量。 首先对 ND 公司门店销售现状进行研究，分析了 ND 公司门店销售预测中存 在的问题。通过观察数据，分析数据缺失情况，同时对数据进行预处理。剔除缺 失比例较大的数据变量，并且针对数据的分布情况使用恰当的方法插补缺失值。 在对数据集中的变量进行处理后，挑选与业务场景契合的变量以及重要的变量作 为数据特征集，构建模型的特征工程。最终选择门店属性特征、地理位置特征、 服务特征、时间特征等四个特征作为特征集。最后分别使用 XGBoost、线性回归、 决策树和随机森林等四个模型对零售门店销量进行预测。通过比较分析真实数据 与预测值之间的平均绝对百分误差，XGBoost 模型的效果更好。并且模型预测的 运行速度由于 XGBoost 模型的并行运算能力而得到了提高。 本论文不仅适用于 ND 公司门店销售预测，还可以将此方法应用于国内零售 实体业甚至电商平台的销售预测，对于提高门店的运营效率、商品的价格、提高 销量及针对性的精准销售具有重要的意义。 关键词： 销售预测；机器学习；XGBoostAbstract -III- Abstract As a high-tech and innovation enterprise, ND company is the world’s leading provider of smart urban mobility solutions. The company is committed to change urban commuting globally and it currently designs, manufactures and sells high-performance smart e-scooters. A strategic performance sales forecasting can help store managers to build a high efficiency working team. Also, it can help to find the key factor which influence consumers and team members. Therefore, the company will improve the mode of production as well as gaining profitability. Based on this background, this article analysis information of 748 retail stores and sales data which is belong to the ND company, then forecasts future sales using XGBoost method. This paper firstly studies the current situation of retail stores and analyzes the existing problems of sales forecasting in ND Company. And then, think about the missing data and analyze the situation while preprocessing the data. Replace the missing data with the appropriate method according to the distribution of data. Besides, the variables with a large degree of loss are eliminated. And then manage the variables in the data set meanwhile pick the crucial variables which is fit to business scenario. As a result, this article decide to use the following four features: the attribute of store, geographical feature, feature of service and feature of date finally, the feature set is substituted into XGBoost, Linear Regression, Decision Tree and Random Forest model to predict the sales of retail stores. In conclusion, XGBoost get a better performance because of the value of mean absolute percentage error(MAPE) between the value of test set and prediction is lower. Moreover, XGBoost model improves the speed of model prediction greatly because of its parallel computing capacity. The model based on XGBoost applies not only to predict retail sales of the ND company, but also extend to the domestic retail industry and electronic business platform. It is also significant to improve the operation mode, commodity price, sales growth and corresponding precision marketing of stores. Keywords： Sales Forecasting; Machine Learning; XGBoost目 录 - V - 目 录 摘要.......................................................................................................................I Abstract....................................................................................................................III 第 1 章 绪论............................................................................................................. 1 1.1 研究背景与意义 ........................................................................................ 1 1.1.1 研究背景 ......................................................................................... 1 1.1.2 研究意义 ......................................................................................... 2 1.2 国内外研究现状 ........................................................................................ 2 1.3 研究内容与文章组织结构 ......................................................................... 4 第 2 章 相关理论..................................................................................................... 5 2.1 机器学习概述 ............................................................................................ 5 2.1.1 有监督学习...................................................................................... 5 2.1.2 无监督学习...................................................................................... 5 2.1.3 半监督学习...................................................................................... 6 2.2 XGBoost 算法 ............................................................................................. 6 2.2.1 XGBoost 概述................................................................................... 6 2.2.2 XGBoost 算法优势........................................................................... 8 2.3 本章小结.................................................................................................... 9 第 3 章 ND 公司门店销售现状分析 ......................................................................11 3.1 ND 公司门店销售现状 ..............................................................................11 3.1.1 ND 公司门店销售业务现状............................................................11 3.1.2 ND 公司门店销售预测现状............................................................11 3.2 探索性数据分析与数据可视化 ................................................................12 3.2.1 探索性数据分析的特点 .................................................................13 3.2.2 探索性数据分析的内容 .................................................................13 3.2.3 数据可视化.....................................................................................14 3.3 ND 公司门店销售数据分析及可视化 .......................................................15 3.4 本章小结...................................................................................................20 第 4 章 ND 公司门店销售预测模型构建...............................................................21 4.1 ND 公司销售数据准备 ..............................................................................21 4.1.1 ND 公司销售数据来源 ...................................................................21 4.1.2 ND 公司销售数据理解 ...................................................................21 4.1.3 ND 公司销售数据缺失值处理 ........................................................25北京工业大学工商管理硕士学位论文 -VI- 4.1.4 特征工程 ........................................................................................26 4.2 ND 公司门店销售预测模型构建描述 .......................................................27 4.2.1 实验环境 ........................................................................................27 4.2.2 评估标准 ........................................................................................28 4.3 ND 公司门店销售预测模型参数及优化 ...................................................28 4.3.1 基于 XGBoost 的模型参数 ............................................................29 4.3.2 平衡偏差-方差 ...............................................................................30 4.3.3 独热编码 ........................................................................................30 4.4 ND 公司门店销售预测模型结果分析与对比 ...........................................31 4.4.1 线性回归模型结果分析 .................................................................31 4.4.2 决策树模型结果分析 ..............................