• Dewang Sampgaonkar
• Sonali Sethi
• Jayesh Naphade
• Ganeshkumar Jain
• Tousif Inamdar

In today's fast-paced world, the courier service industry plays a vital role in facilitating the efficient movement of goods. With the increasing demand for reliable and timely delivery, the need for advanced technology to optimize courier service operations has never been more critical. This synopsis presents a comprehensive overview of a research project aimed at developing a Courier Service Recommendation System (CSRS) to revolutionize how courier services are managed and executed. The proposed CSRS leverages cutting-edge technologies such as machine learning, and data analytics to create a dynamic and intelligent recommendation system. The primary objective is to enhance the overall courier service experience for service providers and customers. Additionally, the CSRS incorporates customer preferences and feedback into its recommendations, creating a personalized user experience. Customers can specify delivery preferences, such as delivery time windows or preferred delivery personnel, leading to a customized and convenient service. The proposed system will have a bar plot that will help users recognize better service providers to select the best without the extra efforts of the user well as can be used in the (P2P) peer-to-peer and as well as (B2C) Business-tocustomer. For the convenience of user decisions, this system displays outcomes graphically.

Courier Service, Recommendation System, Delivery Optimization, Time Estimation, Price Estimation, Machine Learning, Efficiency, Cost Savings, Customer Satisfaction.

"Courier service recommendation system", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.c552-c556, November-2023, Available :http://www.jetir.org/papers/JETIR2311276.pdf

"Courier service recommendation system", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppc552-c556, November-2023, Available at : http://www.jetir.org/papers/JETIR2311276.pdf