Question 3 Prediction of insurance price using Regression models 

A health insurance company has provided you with data about their customers and the amount of insurance paid for each customer. Your task is to develop a machine learning application that can predict the potential insurance amount for new customers.

Here is a sample of some portion of the data(insurance.csv)

To compare the performance of Machine Learning and Deep Learning models, you are required to:

1. Develop a prototype with a Machine Learning Model. Split the dataset into a training set and a testing set. Train the model using the training dataset, then evaluate its performance using the testing dataset. You must achieve an R2 score of at least 0.7 on the testing dataset. Take a screenshot of the result and include it in the submission document.
2. Develop a prototype with the Deep Learning Model. Split the dataset into a training set and a testing set. Train the model using the training dataset, then evaluate its performance using the testing dataset. You must achieve an R2 score of at least 0.8 on the testing dataset. Take a screenshot of the result and include it in the submission document.
3. Evaluate the fairness of the insurance.csv dataset using AI Ethics fairness principles. Identify and justify 2 potential fairness issues that could arise when using this dataset to develop a machine learning/deep learning prediction application. What are the of causes the unfairness for each of the case?.

Note: The insurance.csv dataset includes the following columns: age, sex, bmi, children, smoker, region, and charges.