Page 183 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
systems not only increase customer engagement but also across categories such as electronics, furniture, clothing, and
improve sales conversion rates. Quick Mart employs this books. Data was also obtained from public repositories to
technology to ensure users are directed toward items they supplement Quick Mart's own database. This dataset
are most likely to value, which makes the platform more contains thousands of images of second-hand products, each
tailored to individual needs and preferences. This tagged with category labels, condition ratings, and historical
personalization encourages repeat usage and fosters a sense price data. Table 1 outlines the product categories included
of connection to the platform. in the dataset.
Another crucial component in reimagining second-hand Table 1. Product Categories in the Dataset
commerce is quality assurance. In traditional marketplaces, Sr. No Category
concerns about the condition of pre-owned items can deter 1 Electronics
potential buyers. Verified seller systems and transparent 2 Furniture
review mechanisms have been shown to enhance trust, 3 Clothing
making the platform safer for transactions. Quick Mart 4 Books
addresses this challenge by integrating strict quality checks, 5 Kitchen Appliances
a comprehensive seller verification process, and a robust
6 Sports Equipment
feedback system that provides visibility into product
7 Tools and Gadgets
conditions and seller reputations. These measures build a 8 Jewelry
more reliable marketplace, ensuring that buyers can trust
9 Musical Instruments
the quality of what they are purchasing.
10 Collectibles
Sustainability is at the heart of Quick Mart’s mission. As the 11 Miscellaneous
circular economy gains momentum, many initiatives have
Table 2. Number of Images in Model Evaluation
successfully demonstrated the viability of eco-conscious
Number of Images Folder Directory
business models. Quick Mart stands at the forefront of this
4736 Training
movement by promoting the reuse, recycling, and
1184 Testing
repurposing of goods. The platform educates users on the
environmental impact of their consumption choices and 1184 Validating
actively promotes sustainable practices within its ecosystem.  Validation Set: Used during training to adjust model
Through its efforts, Quick Mart fosters a retail environment parameters.
that not only benefits consumers but also contributes
positively to the environment.  Testing Set: Used solely for the final assessment of the
model's performance.
In summary, Quick Mart’s integration of AI-driven pricing,
real-time inventory management, personalized
recommendations, quality assurance protocols, and
sustainability initiatives represents a comprehensive
innovation in the second-hand marketplace. By reimagining
the retail experience through these advanced technologies,
Quick Mart is setting new standards for the industry,
creating a more efficient, trustworthy, and eco-conscious
ecosystem for both buyers and sellers.
III. PROPOSED WORK
In this phase, we redefine the process of second-hand
commerce through Quick Mart's innovative solutions, aiming
to enhance the buying and selling experience of pre-owned
goods. The approach is centered on optimizing the
marketplace for second-hand products by leveraging cutting-
edge technologies. The proposed framework for Quick Mart
integrates advanced algorithms to improve product
categorization, pricing prediction, and condition evaluation.
This framework relies on structured datasets for both Fig. 2. Sample Images of Second-Hand Products in the
training and testing purposes. In the first step, products are Dataset.
categorized based on their condition (e.g., new, like-new,
lightly used), and then processed into structured formats for Data Preprocessing
feature extraction. Following this, machine learning Effective data preprocessing is crucial for the success of
algorithms are applied to classify products and predict their machine learning models. During this phase, missing values
prices accurately. and redundant data are handled, and data augmentation is
applied to expand the dataset. The key steps include:
The process is divided into four key phases: data collection,  Loading the Data: The dataset is loaded and split into
data preprocessing, classifier implementation, and training and testing sets.
performance evaluation. Each phase is described in greater
detail below:  Shuffling and Splitting: The data is shuffled and split
into training, validation, and testing subsets in an 80:20
Data Collection ratio.
For this study, data was sourced from Quick Mart's platform,  Label Encoding: Text-based labels are converted into
which features a diverse array of second-hand products
numerical representations using LabelEncoder.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 173