The client belongs to the manufacturing industry and is responsible for development and selling of a large number of products in the global market. To constantly keep on increasing the reliability and improving the quality of their products with every batch of production, they want to accurately predict the future warranty claims based on the customer and product behavior information. This would help the client to reveal early warnings or defects of the product.
R, SQL (for data extraction) and Tableau (for visualization)
Predicting the warranty claims is a regression problem and there are various factors that affect the percentage of claims and these can be classified broadly under customer behavior (end of warranty period) and product behavior (defect information). Few different regression models were tried in R, namely Generalized Linear Models, Generalized Additive models and Random Forest. After evaluating and comparing the results from these 3 models, it was decided that Generalized Additive Models and Random Forest are the two most accurate in predicting the warranty claims percentage and hence an average of both models was used as the final output. In this way, the client was able to detect any issues in the new product at an early stage and was able to fix the problem in the new batches.
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