Insurance companies are extremely interested in predicting the future. Accurate prediction gives the company a chance to reduce financial loss. The insurers use rather complex methodologies for this purpose. The major models are a decision tree, a random forest, a binary logistic regression, and a support vector machine. A great number of different variables are under analysis in this case. The algorithms involve the detection of relations between claims, implementation of high dimensionality to reach all the levels, detection of missing observations, etc. In this way, the individual customer's portfolio is made.
The insurance company is facing the issue of running out of money when providing the claims. So please help the company by predicting the amount of money that will be required for a claim based on historical transactions. This will help the company to keep some reserve money for future claims.
This is the "Sample Insurance Claim Prediction Dataset" which is based on "[Medical Cost Personal Datasets][1]" to update sample value on top.
We are Predicting the Insurance Claim by each user. Machine Learning algorithms for Regression analysis are used and Data Visualization is also performed to support Analysis.