sonu275981 / insurance-prediction

Exploratory Data Analysis & predicting medical insurance cost with machine learning.

Insurance prediction

As shown above, this Project leverages machine learning models to predict your insurance charges, and helps the customer understand how smoking or decreasing your BMI affects insurance premiums.

As we see the value of gross insurance premiums worldwide continue to skyrocket past 5 trillion dollars, we know that most of these costs are preventable. For example, just by eliminating smoking, and lowering your BMI by a few points could mean shaving thousands of dollars off of your premium charges. In this application, we study the effects of age, smoking, BMI, gender, and region to determine how much of a difference these factors can make on your insurance premium. By using our application, customers see the radical difference their lifestyle choices make on their insurance charges. By leveraging AI and machine learning, we help customers understand just how much smoking increases their premium, by predicting how much they will have to pay within seconds.

Project Overview

• Seek insight from the dataset with Exploratory Data Analysis
• Performed data processing, data engineering to prepare data before modeling
• Built a model to predict Insurance Cost based on the features

Data Processing

• Check missing value - there are none
• Feature transformation Encoding sex, region Ordinal encoding smoker
• Modeling - Separating target & features - Splitting train & test data
• Modeling using Linear Regression, Random Forest, Decision Tree algorithm

Conclusion

Based on the predictive modeling, Decision Tree algorithm has the best score compared to the others, Decision Tree algorithm is fit based on the train & test accuracy.