https://github.com/am-ankitgit/cross-sell-prediction
Classification of cross sell prediction of insurance
https://github.com/am-ankitgit/cross-sell-prediction
Last synced: about 1 year ago
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Classification of cross sell prediction of insurance
- Host: GitHub
- URL: https://github.com/am-ankitgit/cross-sell-prediction
- Owner: AM-Ankitgit
- Created: 2022-06-15T05:03:56.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-01-05T15:45:52.000Z (over 3 years ago)
- Last Synced: 2025-02-15T20:52:35.728Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 7.86 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Health-Insurance-Cross-Sell-Prediction
Predict Health Insurance Owners' who will be interested in Vehicle Insurance
# Problem Statement
Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the customers from past year will also be interested in Vehicle Insurance provided by the company.
# Business Goal
Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.
# Data Discription
Data Source : Kaggle
Data Source Link : https://www.kaggle.com/anmolkumar/health-insurance-cross-sell-prediction?select=train.csv
The Dataset used for the analysis includes the following columns:
id : Unique ID for the customer
Gender : Gender of the customer
Age : Age of the customer
Driving_License : 0 - Customer does not have DL,1 - Customer already has DL
Region_Code : Unique code for the region of the customer
Previously_Insured : 1 - Customer already has Vehicle Insurance, 0-Customer doesn't have Vehicle Insurance
Vehicle_Age : Age of the Vehicle Vehicle_Damage : 1 - Customer got his/her vehicle damaged in the past. 0 -Customer didn't get his/her vehicle damaged in the past.
Annual_Premium : The amount customer needs to pay as premium in the year
PolicySalesChannel : Anonymized Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.
Vintage : Number of Days, Customer has been associated with the company
Response : 1 - Customer is interested, 0 - Customer is not interested
# Conclusion
The ML model for the problem statement was created using python with the help of the dataset, and the ML model created with Random Forest and XGBClassifier models performed better than Logistics Regression model. Thus, for the given problem, the models created by Random Forest and XGBClassifier.