多平台相機價格爬蟲與市場分析

Abstract

中文摘要 本研究旨在解決攝影器材市場中嚴重的資訊不對稱與跨國定價差異問題。專業相機 與鏡頭具備高單價、規格複雜及品牌忠誠度高等特性，消費者在選購時常面臨價格不透 明及二手分級標準不一的困境。此外，不同區域市場（如台灣、日本、馬來西亞）受匯 率波動與供需結構影響，存在顯著價差。因此，本專案建構一套自動化跨國市場情報系 統，旨在協助消費者與行銷決策者快速掌握行情，並識別潛在的跨境套利機會。 在研究實施方面，本研究以 Python 為核心開發語言，運用 Requests 與 BeautifulSoup 模組開發網路爬蟲，針對台灣（PChome 24h、Dpowers）、日本（Fujiya Camera）及馬來西亞（KLDSLR）等電商平台進行異質數據採集。數據處理階段利用 Pandas 進行資料清洗、匯率轉換與去重，並導入 IQR（四分位距）統計法剔除價格異 常值。隨後實作特徵工程，運用 Scikit-learn 建立隨機森林（Random Forest）價格預測 模型與 K-Means 市場分群模型，最終透過 Streamlit 框架建置互動式視覺化儀表板， 提供直觀的數據決策介面。 根據分析結果顯示，攝影器材市場呈現高度寡占特徵，Nikon、Sony 與 Canon 三 大品牌主導市場份額，且鏡頭與機身之供應量比例約為 5:1，驗證了「生態系鎖定」之 商業策略。價格分析證實日本二手市場在特定中高階機種上，相較於台灣市場具備顯著 價格優勢，具備跨境套利之經濟可行性。機器學習模型亦能有效預測市場合理價格，誤 差控制在實務操作之容許範圍內。本系統不僅優化個人購買決策，亦可供電商業主作為 動態定價之參考。Abstract This study addresses the issues of information asymmetry and cross-border pricing disparities within the photography equipment market. High-end cameras and lenses are characterized by high unit prices, complex technical specifications, and varied second-hand grading standards, which often lead to market opacity. Furthermore, significant price gaps exist between regional markets (e.g., Taiwan, Japan, and Malaysia) due to exchange rate fluctuations and supply-demand imbalances. This project develops an automated cross-border market intelligence system to empower consumers and marketers with real-time insights and identify potential arbitrage opportunities. Utilizing Python as the primary development language, web scrapers were developed using Requests and BeautifulSoup to collect heterogeneous data from major e-commerce platforms, including PChome 24h and Dpowers (Taiwan), Fujiya Camera (Japan), and KLDSLR (Malaysia). Data processing involved cleansing, currency conversion, and deduplication via the Pandas library, with the Interquartile Range (IQR) method applied for outlier detection. Feature engineering was implemented to support machine learning models, specifically a Random Forest Regressor for price prediction and K-Means for market segmentation. Finally, an interactive visualization dashboard was deployed using the Streamlit framework. The findings reveal a highly oligopolistic market dominated by Nikon, Sony, and Canon. A lens-to-body ratio of approximately 5:1 confirms the prevalence of an "ecosystem lock-in" business model. Price analysis verifies that the Japanese second-hand market offers a significant competitive advantage for specific mid-to-high-end models compared to the Taiwanese market, proving the economic feasibility of cross-border arbitrage. The machine learning models effectively predict fair market values with an acceptable margin of error. This system serves as a valuable tool for optimizing individual purchasing decisions and providing a strategic reference for e-commerce competitive pricing.