Medical Insurance Cost Predictor

This project predicts the Medical Insurance cost of an individual.

The model predicts the cost by taking into account the age of the person, gender, bmi, the number of children and whether the individual smokes or not.

Why this Dataset?

I have chosen this dataset as predicting the medical insurance costs of an individual will help insurance companies choose their prices, banks to issue loans accordingly and also, the individual will get an idea about how much it will cost him for an insurance and plan his finances accordingly.

Algorithm Used

Linear Regression has been used to train the model. 80% of the dataset has been used to train the dataset and 20% of it has been used for testing.
After plotting the data, it was seen that the graph was a straight line. Hence Linear Regression has been used.

The Variance Score of this model is approximately 0.80. If a model has a variance score of more than 0.60, it is considered to be a good model.

Python Libraries Used

• pandas - for viewing and manipulating data
• matplotlib - for plotting the data
• numpy - for dividing the data into 2 parts, one to train and another to test
• sklearn - for applying linear regression and train the model
• tkinter - making the GUI

Dataset Source

The dataset has been taken from kaggle.

Link to the Dataset