As a customer shops an insurance policy, he/she will receive a number of quotes with different coverage options before purchasing a plan. We would like to predict the purchased coverage options using a limited subset of the total interaction history provided by Allstate Insurance. If the eventual purchase can be predicted sooner in the shopping window, the quoting process is shortened and the issuer is less likely to lose the customer's business.
Dataset from Kaggle : "Allstate Purchase Prediction Challenge" by Allstate Insurance
Source: https://www.kaggle.com/c/allstate-purchase-prediction-challenge/data (requires login)
The jupyter notebook is roughly divided into three segments.
We first clean the data and then explore it to understand patterns and trends within the data.
Then, we predict what cost a customer will pay for the insurance cover, given his customer details and choice of policy. This is for the aid of the customers who approach Allstate insurance with a particular cover in their mind.
Finally, we predict what policy a customer will pick given some customer details.
This project was created by the members of team Faraday, namely, Chopra Dhruv, Krithika Jayaraman Karthikeyan, Louis Wirja and Neha Ramesh.
The members of the team express their gratitude and thanks to Professor Dr Sourav Sen Gupta, whose constant support and guidance made this project possible.
MIT License
Copyright (c) 2021 Dhruv Chopra
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