International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 03 | Mar 2024
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p-ISSN: 2395-0072
Clothes Price Comparison Using Machine Learning Tainiyat K Hanchinal1, Vaishali D Bhavani2 1UG Student, Dept of Computer Science and Engineering, Jain College of Engineering and Research, Karnataka,
India
2UG Student, Dept of Computer Science and Engineering, Jain College of Engineering and Research, Karnataka,
India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Current e-commerce platforms include a range of
The price comparison of clothes website resolves this issue by compiling data from several online merchants and displaying it to customers in an understandable and structured way. Consumers may go through several categories or do a targeted search to locate the apparel item they’re looking for. Users are then able to rapidly compare and get the best deal due to the website’s presentation of alternatives and pricing from many stores. To improve and streamline the procedure of comparing clothing prices, this system applies machine learning techniques. This is motivated by the enormous amount and complexity of data that online merchants produce, which includes information on everything from product features and specifications to pricing policies and user reviews. The size of this data makes manual comparison techniques impossible, therefore it's critical to take use of machine learning’s ability to identify patterns, correlations, and trends in huge datasets.
features to enable the purchase of any cloths from their websites. But comparing any garments pricing, deals, and quality across numerous apps takes time, and the customer has to spend their time reading reviews and visiting other websites to compare costs. Most of the clients prefer to purchase their necessities online since it saves them time, particularly those who currently live in metropolitan areas with hectic lifestyles. Furthermore, while buying things, people always look for the lowest possible price. Therefore, a strong artificial intelligence system is developed that can analyze large, varied datasets that include product attributes and pricing data from different online shops. The algorithm for the model is created with the use of machine learning techniques to identify trends and correlations that affect price, considering factors such as material quality, fashion trends, and brand popularity. Natural language processing is used by the AI system to analyze product evaluations and descriptions, which helps it to better grasp customer preferences. The goal is to develop a dynamic and flexible tool that predicts and adjusts to market changes aside from offering precise and upto-date pricing comparisons for clothes. This system reveals the effectiveness of using AI in order to enhance the efficiency and accuracy of apparel price comparison through rigorous review, hence empowering customers to make better-informed selections. In this manner, paper aims to provide online shoppers a method to purchase goods at a reasonable price while also saving them money, time, and effort.
In India, there are not as many price comparison websites available as in other nations. The majority of them compare just local brands’ prices. While it is crucial for a comparison website to deliver results with affordable pricing that corresponds with consumers’ preferences, precise results are equally important in ensuring users receive their desired products. Additionally, it relies on how frequently the information is updated; if not, users who compare it to another website might get confused. Most of the people do not have time to go shopping for clothes. It is users freedom to select the vendor that gives the greatest deal on the garments you are interested in. However, comparing costs and pulling From the purchase of some items at a higher price requires a significant amount of time in relation to those price supplied by any vendor. A catalog that is published online allows retailers to cut expenses. Due to their hectic schedules, the majority of people who manage their businesses do not have the time to make offline purchases. The fig. 1 demonstrates the percentage of the total population that do online or offline shopping in India.
Key Words: Price Comparison, Artificial Intelligence, Web Scraping, Machine Learning, E-commerce.
1.INTRODUCTION The fashion industry is huge and fiercely competitive, with several brands and outlets offering a diverse range of apparel alternatives. Because of this, costs for comparable apparel goods could range greatly amongst online retailers. Customers find it difficult to find the greatest offers and ensure that they are getting the greatest value for their money. as a result. Due to time and financial constraints, especially in the current fashion trends where variety of clothing styles and their prices have increased and users have limited time to look at properties.
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Machine learning plays an essential role in transforming clothing price comparison systems. The systems use sophisticated algorithms to sort through large information and identify pricing patterns, historical data, and market swings. The algorithms make accurate pricing predictions and price comparisons by using characteristics like fabric quality, user feedback, and brand reputation. This technology improves the user experience by providing
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