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README.md

Detailed-supervised-machine-learning-analysis-in-travel-insurance-data


Problem Statement

Risk management processes form an integral part of the insurance industry. Insurers consider every available quantifiable factor to develop profiles of high and low insurance risk for their prospective policyholders. Level of risk determines the insurance premiums of these policies. To this end, insurers collect a vast amount of information about policyholders and insured objects.

Dataset: 

The data consists of records of roughly 52000 instances and 11 features. There are 10 independent variables and 1 target that describes whether the insurances will be claimed or not.

Target: Claim Status (Claim)

Name of agency (Agency)

Type of travel insurance agencies (Agency.Type)

Distribution channel of travel insurance agencies (Distribution.Channel)

Name of the travel insurance products (Product.Name)

Duration of travel (Duration)

Destination of travel (Destination)

Amount of sales of travel insurance policies (Net.Sales)

The commission received for travel insurance agency (Commission)

Age of insured (Age)

The identification record of every observation (ID)


Author : Shubendu Biswas


Acknowledgement

https://stackoverflow.com/

appliedai

greyatom

wikipidea

kaggle

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Supervised machine learning

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