{"id":15911573,"url":"https://github.com/devamoghs/av-cross-sell-prediction","last_synced_at":"2025-04-03T02:22:45.170Z","repository":{"id":97395762,"uuid":"297224508","full_name":"devAmoghS/AV-Cross-Sell-Prediction","owner":"devAmoghS","description":"Predict if a person who is already insured will purchase a Vehicle Insurance or not. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AV-Cross-Sell-Prediction\n![](https://www.aureusanalytics.com/hs-fs/hubfs/Aureus%20cross-sell.jpeg?width=1254\u0026name=Aureus%20cross-sell.jpeg)\n\nJust like **medical insurance**, there is **vehicle insurance** where every year customer needs to pay a **premium** of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation **(called ‘sum assured’)** to the customer.  \n\n# Objective\nBuilding 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. \n\n# Data Dictionay\n| Variable             | Definition                                                                                                                  |\n|----------------------|-----------------------------------------------------------------------------------------------------------------------------|\n| id                   | Unique ID for the customer                                                                                                  |\n| Gender               | Gender of the customer                                                                                                      |\n| Age                  | Age of the customer                                                                                                         |\n| Driving_License      | 0 : Customer does not have DL, 1 : Customer already has DL                                                                  |\n| Region_Code          | Unique code for the region of the customer                                                                                  |\n| Previously_Insured   | 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance                                     |\n| Vehicle_Age          | Age of the Vehicle                                                                                                          |\n| Vehicle_Damage       | 1 : Customer got his/her vehicle damaged in the past. 0 : Customer didn't get his/her vehicle damaged in the past.          |\n| Annual_Premium       | The amount customer needs to pay as premium in the year                                                                     |\n| Policy_Sales_Channel | Anonymised Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc. |\n| Vintage              | Number of Days, Customer has been associated with the company                                                               |\n| Response             | 1 :  Customer is interested, 0 : Customer is not interested                                                                 |\n\n# Evaluation Metric\nROC_AUC score\n\n# Model and Score\n#### Model Type: Random Forest\t\n#### Public Score: 0.754661115842268\t\n#### Private Score: 0.758023433881523\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevamoghs%2Fav-cross-sell-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdevamoghs%2Fav-cross-sell-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevamoghs%2Fav-cross-sell-prediction/lists"}