title: Machine learning techniques of price prediction analysis
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: ÐÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÐ½
Creators: name: Ð£ÑÐ±Ð¸Ð½Ð° ÐÐµÐ½Ð´ÐµÑ, Ð¥Ð¾ÑÑÐµ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
e-commerce
internet
profitability
dynamic prices
ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½Ð°Ñ ÐºÐ¾Ð¼Ð¼ÐµÑÑÐ¸Ñ
Ð¸Ð½ÑÐµÑÐ½ÐµÑ
Ð¿ÑÐ¸Ð±ÑÐ»ÑÐ½Ð¾ÑÑÑ
Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ðµ ÑÐµÐ½Ñ

description: This thesis explores the transformative potential of artificial intelligence (AI) in refining and optimizing pricing strategies within the highly competitive e-commerce industry. Specifically, it investigates how Long Short-Term Memory (LSTM) networks, a sophisticated type of recurrent neural network designed to analyze sequence data, can be utilized to dynamically adjust pricing in real-time. This study meticulously develops and evaluates an LSTM model that processes extensive datasets incorporating variables such as consumer purchase patterns, market fluctuations, and detailed product characteristics to determine optimal pricing strategies. The research methodology includes the collection and analysis of large-scale e-commerce transaction data, followed by the application of LSTM networks to predict optimal price points. This approach not only enhances the accuracy of pricing models but also adapts to the ever-changing e-commerce market dynamics, thereby increasing business rofitability and market responsiveness. Furthermore, the thesis addresses the significant challenges associated with implementing AI in pricing strategies, including technical hurdles, the need for vast amounts of training data, and potential biases in AI algorithms. It also delves into ethical considerations, such as the implications of data privacy and the risk of unintentional discrimination in AI-driven pricing decisions. The study proposes robust strategies and best practices for mitigating these risks, ensuring that AI integration adheres to ethical standards and regulatory requirements.
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