This kernel aims to predict car insurance cold call success. It shows data exploration and visualization, along with feature engineering and model selection.
This is a dataset from one bank in the United States. Besides usual services, this bank also provides car insurance services. The bank organizes regular campaigns to attract new clients. The bank has potential customers’ data, and bank’s employees call them for advertising available car insurance options. We are provided with general information about clients (age, job, etc.) as well as more specific information about the current insurance sell campaign (communication, last contact day) and previous campaigns (attributes like previous attempts, outcome). You have data about 4000 customers who were contacted during the last campaign and for whom the results of campaign (did the customer buy insurance or not) are known.
The task is to predict for 1000 customers who were contacted during the current campaign, whether they will buy car insurance or not.