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@article{179744,
        author = {Ajay Ankel and Dr. sanjiv sharma},
        title = {Design an efficient algorithms of E - Commerce},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7917-7920},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179744},
        abstract = {The ongoing increase in online shopping has
resulted in e-commerce platforms becoming very
competitive and reliant on data. To differentiate
themselves in this landscape, companies need to provide
customized user experiences, effective product
suggestions, flexible pricing, and safe operations. This
study discusses the development of effective algorithms
combined with machine learning to improve an ecommerce website's performance and user interaction.
The project employs machine learning methods like
Collaborative Filtering for suggesting products, Linear
Regression for forecasting prices, and K-Means
Clustering for segmenting customers. These algorithms
are developed with Python libraries and incorporated
into a complete web application. The frontend is built
with React.js, the backend is powered by Node.js, and
MongoDB functions as the database. By incorporating
these smart capabilities, the e-commerce platform can
assess user activities, forecast trends, and enhance
decision-making. The study also explores system design,
algorithm effectiveness, and performance indicators,
showcasing how machine learning can greatly enhance
e-commerce functions and user contentment.},
        keywords = {E-commerce, Efficient Algorithms, Product Search, Recommendation System, Inventory Management, Machine Learning, User Experience, Scalability, Optimization},
        month = {May},
        }