MarketRecommenderSystem using machine learning by applying Stochastic gradient descent method

Tracking #: 528-1508

Authors:
NameORCID
Gurumurthy SwaminathanORCID logo https://orcid.org/0000-0001-6293-8668


Submission Type: 

Research Paper

Abstract: 

The insurance broking, driven by suggesting right market/carrier for the product where client would like to cover for ,by carefully making calculus on their history of data (like placements ,bind, quotes and its ratios.,etc ) these calculations helps to evaluate the suggested market is right fit for the client and for the increased client retention (renewals). However, the current system may not a primitive enough to handle this case. This paper intended to analyze the machine learning algorithms to check if it has primitive role in this area and to analyze how much the side data has an effect on deciding the scores.

Manuscript: 

Supplementary Files (optional): 

Tags: 

  • Reviewed

Data repository URLs: 

https://github.com/sgmoorthy/datascience/blob/master/Market%20Recommender%20System/Dataset.csv

Date of Submission: 

Friday, December 8, 2017

Date of Decision: 

Monday, December 11, 2017

Decision: 

Reject (Pre-Screening)