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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66788| Title: | 運用正規化迴歸分析線上銷售預測： 以在亞馬遜上的運動商品為例 Online Sales Forecasting by Regularized Regression for functional products: Taking Sport Goods on Amazon.com as an Example. |
| Authors: | Yu-Yin Liu 劉育吟 |
| Advisor: | 孔令傑(Ling-Chieh Kung) |
| Keyword: | 電子商務,銷售預測,機器學習,套索回歸,嶺回歸, e-commerce,sales forecasting,machine learning,LASSO regression,Ridge regression, |
| Publication Year : | 2020 |
| Degree: | 碩士 |
| Abstract: | Abstract
In this study, we research on a company’s sport good sales forecasting on Amazon.com. We analyze data including transactions, advertisement reports, customer reviews, competitors’ prices and customer reviews, holiday-or-not, and weekend-or-not for more than 500 days. We implement machine learning models to tackle the sales forecasting problem. The main objective of this study is to discover the most efficient model among linear, LASSO, and Ridge regression by comparing their mean absolute error in the testing set. We find that the most efficient model is LASSO regression in general, whose performance may be better than linear regression by 87 % on a certain product. |
| URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66788 |
| DOI: | 10.6342/NTU202000275 |
| Fulltext Rights: | 有償授權 |
| Appears in Collections: | 管理學院企業管理專班(Global MBA) |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-109-1.pdf Restricted Access | 5.9 MB | Adobe PDF |
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