利用机器学习线性回归算法方法进行销售趋势分析

Alwidahyani Sipahutar, Ibnu Rasyid Munthe, Angga Putra Juledi
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引用次数: 0

摘要

目前,印度尼西亚的在线业务发展非常迅速,其流程是通过转售商或分销商利用其中一种社交媒体订购商品。根据产品信息、价格、折扣和库存数量,利用决策模型进行商品采购。在销售过程中,Toko Serbu Aek Batu 通常会发布几种不同的商品,以不同的价格提供给市场,但并不是所有商品都很抢手。多元线性回归是一种描述因变量与影响一个以上自变量的因素之间关系的分析。本研究的目的是利用 rapidminer 的线性回归方法分析销售趋势。本研究的结果是使用人工计算的预测计算结果与 rapidminer 的结果相同,使用线性回归算法预测买家期望的价格与原始价格相差不大,而且 rapidminer 非常准确,可用于预测客户期望价格的销售趋势,这样卖家就可以更加关注在销售过程中非常有影响力的事情。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sales Trend Analysis With Machine Learning Linear Regression Algorithm Method
The development of online business in Indonesia is now very rapid, with the process being done by ordering goods through resellers or distributors using one of the social media. Item purchases are made based on product information, prices, discounts and inventory quantities using a decision model. In the sales process, Toko Serbu Aek Batu usually releases several different items to be offered to the market at different prices, but not all items are in high demand. Multiple linear regression is an analysis that describes the relationship between dependent variables and factors that affect more than one independent variable. The purpose of this study is to analyze sales trends using a linear regression method using rapidminer. The results of this study are prediction calculations using manual calculations with rapidminer the same results, predicting the price desired by buyers using a linear regression algorithm with the original price is not much different and rapidminer is very accurate to be used in predicting sales trends at the price desired by customers, so that sellers can pay more attention to things that are very influential in the sales process.
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