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Predict for predict whether customers will be interested in our health insurance policy based on customer demographic, economic, social, and other banking features. Interested in the relationship between customer history on recommended health insurance.

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Jchow2/python-health-insurance-cross-sell-lead

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Health Insurance Cross-Sell Lead

Data Preparation

Identify potential factors related to customer interest in a premium health insurance plan. We should consider information related to potential customers and the insurance given at any point in time, including:

  • Demographics (city, age, region etc.)
  • Information regarding holding policies of the customer
  • Recommended Policy Information

Problem Description

Your client is a financial services company offering various products like loans, investments, and insurance. They want to recommend health insurance to existing customers visiting their website based on their profiles, aiming to reduce churn and enhance scalability.

Supervised Machine Learning Problem Type: Binary Classification and K-Means Clustering

Target

Our target is to build a model to predict whether the person will be interested in their proposed health plan/policy given the information above.

Grading Metric: ROC_AUC_SCORE

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Predict for predict whether customers will be interested in our health insurance policy based on customer demographic, economic, social, and other banking features. Interested in the relationship between customer history on recommended health insurance.

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