Sold by: MphasisÂ
Deployed on AWS
The solution uses a classical regression approach to predict the number of customers churning in/out of a given insurance premium policy.
Overview
The solution has been built upon the IHDS (Indian Human Development Survey) dataset conducted in the years 2005 (IHDS-I) and 2011 (IHDS-II). It covers 41554 households across 1503 villages and 971 urban places. The socioeconomic survey was divided into 8 categories of data which consists of individual, household, medical, non-resident, primary school, birth history, village and crop yield. Out of these datasets, household data along with a few individual data variables have been taken into consideration for this churn prediction solution.
Highlights
- The solution aims to understand customer behavior based on offered premium amounts as well as the financial growth of the insurance company in the context of macroeconomic factors such as GDP, unemployment and inflation rate.
- This solution considers key performance metrics which indicate the following: * Number of customers churned out of their insurance policy * Total amount the insurance company loses due to churn * Number of customers retained * Amount gained by the insurance company
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $10.00/host/hour |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $5.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00/host/hour |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
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Additional details
Inputs
- Summary
This solution takes in a zip file with 2 csv. It mush contain train.csv which is used to train the model and test.csv which is used to extract the KPI from the model. The model takes 16 features(look at sample data) as mentioned for training.
- Limitations for input type
- The parameters should contain "target_life_insurance" and "Insurance_Premium_paid" variables
- Input MIME type
- text/csv, application/zip, application/gzip, text/plain
https://github.com/Mphasis-ML-Marketplace/Insurance-Churn-Prediction/blob/main/input.zip
https://github.com/Mphasis-ML-Marketplace/Insurance-Churn-Prediction/blob/main/input.zip
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Support
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Target High-Value Policyholders and Insurance Switchers Using Predictive Propensities
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