Insurance Premium Charges in US with important details for risk underwriting. (teertha/ushealthinsurancedataset)   [more]
The venerable insurance industry is no stranger to data driven decision making. Yet in today's rapidly transforming digital landscape, Insurance is struggling to adapt and benefit from new technologies compared to other industries, even within the BFSI sphere (compared to the Banking sector for example.) Extremely complex underwriting rule-sets that are radically different in different product lines, many non-KYC environments with a lack of centralized customer information base, complex relationship with consumers in traditional risk underwriting where sometimes customer centricity runs reverse to business profit, inertia of regulatory compliance - are some of the unique challenges faced by Insurance Business.
Despite this, emergent technologies like AI and Block Chain have brought a radical change in Insurance, and Data Analytics sits at the core of this transformation. We can identify 4 key factors behind the emergence of Analytics as a crucial part of InsurTech:
This dataset can be helpful in a simple yet illuminating study in understanding the risk underwriting in Health Insurance, the interplay of various attributes of the insured and see how they affect the insurance premium.
This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. There are no missing or undefined values in the dataset.
This relatively simple dataset should be an excellent starting point for EDA, Statistical Analysis and Hypothesis testing and training Linear Regression models for predicting Insurance Premium Charges.
Proposed Tasks: - Exploratory Data Analytics - Statistical hypothesis testing - Statistical Modeling - Linear Regression
Size: 16.0 KB Source: Kaggle Last updated: 2022-01-28 14:40
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