- The aim of this project is to build a model that can detect auto insurance fraud.
- The main challenge behind this is that fraud is far less common as compared to legit insurance claims.
- This is classification problem
- This type of problems is known as imbalanced class classification
- frauds are unethical & are losses to the company
- By building a model that can classify auto insurance fraud I am able to cut the losses for the insurance company
- less losses equates to more earning.
- The objective of this project is to predict the fraud on the basis of the data provided by using machine learning models.
- The data was collected is from one of the genuine website UC Irvine mchine learning repository.