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We found that caravan insurance buyers are likely to live in wealthy area. Customer sub type MOSTYPE variable has 41 value types which can be categorised under two broad The central idea behind their target marketing being that the penetration price pricing directly influences the conversion rate. ANALYZING AND CATEGORIZING THE VARIABLES: When your caravan is being towed, your car insurance policy often only extends to third party cover, so any damage to the caravan itself would be covered under your caravan insurance. TICDATA2000.txt: Dataset to train and validate prediction models and build a description (5822 customer records). 2000. P. van der Putten and M. van Someren. Recapping from the previous two posts, this post will utilise machine learning algorithms to predict customers who are mostly likely to purchase caravan policy based on 85 historic socio-demographic and product-ownership data attributes. This is something that should be kept in mind and taken care of when using this rule. You signed in with another tab or window. Science Technical Report 2000-09. Note: All the variables starting with M are zipcode variables. In 2000, a Europe insurance company that offered various insurance services including life, auto, boat insurances to a large customer faced this challenge of cross-selling where the companys newest service Caravan insurance policy turned to be disappointing in terms of sales. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes in the cloud, making it easy for anyone to extend Caravan to new catchments. All customers living in areas with the same zip code have the same sociodemographic attributes. Now, I have calculated the profits associated with each of my models for classification cutoff values ranging from 0 to 1. Note that the most significant part of my analysis is to identify the success class observations correctly, and hence, the two most important performance features for us are PPV and sensitivity. SIGKDD Explorations, 2. Moreover, the unbalanced nature of this dataset required us to use sampling techniques to capture the characteristics of the success class (only 5.9% of the observations). Compute time series of spatially-averaged meteorological forcings on Google Earth Engine. 95. The results from these allowed us to state the relationship between For more information on customizing the embed code, read Embedding Snippets. 177-195, Kluwer Academic Publishers All Rights Reserved,