Which algorithms are used in this premium prediction project?

The system implements and compares four regression algorithms: XGBoost, Random Forest, Ridge Regression, and Lasso Regression. XGBoost emerged as the best performer with 86% R² accuracy.

What is the accuracy of this premium prediction model?

The XGBoost model achieves a Test R² Score of 0.860, with RMSE of $4,681 and MAE of $3,412, making it highly accurate for medical expense prediction.

Who can benefit from this insurance cost prediction system?

Insurance companies can use it for accurate premium pricing and risk assessment. Underwriters can make data-driven decisions. Customers benefit from transparent, fair pricing based on their specific risk profile.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

AI-Based Insurance Premium Cost Prediction System - Complete Project Demo Video

Watch Demo Video

Machine Learning

AI-Based Insurance Premium Cost Prediction System

The AI-Based Insurance Premium Cost Prediction System is a comprehensive machine learning system that predicts individual medical expenses and insurance premiums using demographic and health-related factors. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like XGBoost, Random Forest, Ridge, and Lasso Regression to achieve 86% R² accuracy.

The system leverages features including age, sex, BMI, number of children, smoking status, and geographic region. Engineered features like BMI-Smoker interaction, age-smoker, and age² significantly improve prediction accuracy. It provides interactive visualizations, feature importance analysis, model comparison, and real-time premium predictions to help insurance companies make data-driven pricing decisions.

Python 3.8+ Machine Learning XGBoost Random Forest Ridge Regression Lasso Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Premium Prediction
XGBoost (86% R² Accuracy)
Random Forest, Ridge & Lasso
BMI-Smoker Interaction Feature
Log-Transform for Skewed Data
Real-time Premium Predictions
Feature Importance Analysis
Model Performance Comparison
Interactive Visualizations
Flask Web Application

Algorithms Used

⚡ XGBoost Regressor

Optimized gradient boosting with regularization, captures non-linear relationships and feature interactions

🎯 R²: 0.860

🌲 Random Forest

Ensemble learning with multiple decision trees, robust to outliers and overfitting

🎯 R²: 0.848

📈 Ridge Regression

Linear regression with L2 regularization, handles multicollinearity effectively

🎯 R²: 0.832

📉 Lasso Regression

Linear regression with L1 regularization, performs automatic feature selection

🎯 R²: 0.828

Methodology & Workflow

1 Data Loading & Inspection
Insurance dataset with 1,338 records and 7 features

2 Data Preprocessing
Cleaning, categorical encoding, outlier trimming

3 Feature Engineering
BMI-Smoker, Obese, Obese-Smoker, Age-Smoker, Age²

4 Model Training
XGBoost, Random Forest, Ridge, Lasso with hyperparameter tuning

5 Model Evaluation
R², RMSE, MAE, 5-fold cross-validation

6 Web Deployment
Flask web app for real-time premium predictions

Model Performance Comparison

Metric	XGBoost	Random Forest	Ridge	Lasso	Best
Test R²	0.860	0.848	0.832	0.828	XGBoost
Test RMSE	$4,681	$5,021	$5,416	$5,489	XGBoost
Test MAE	$3,412	$3,589	$4,112	$4,187	XGBoost
Within 10%	38.4%	35.8%	31.7%	30.9%	XGBoost
CV Mean (5-Fold)	0.852	0.841	0.826	0.819	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (1,338 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

Check Payment Status

LIMITED TIME OFFER -70%

Complete Project Package Lifetime Access

Original Price

₹9,999

Today's Price ₹2,999 💎 Save ₹7,000

You Save ₹7,000 (70% OFF)

Complete Source Code

Documentation & PPT

Video Tutorial

24/7 Expert Support

Scan & Pay with UPI

SECURE

Payee Thirumalai Kumar

UPI ID 9600095045@icici

Amount ₹2,999

Submit Your Payment

100% SECURE

Full Name *

Email *

Phone Number *

College/University *

Degree

Passout Year

Department

Payment Details

Enter your UPI Transaction ID and upload payment screenshot for verification.

Transaction ID *

Payment Screenshot *

Click to upload screenshot JPG, PNG, GIF, PDF • Max 5MB

📚 Academic & Research Support Services

Need help with Thesis, Dissertation, Assignments, or PhD Research? We've got you covered!

📝

Thesis & Dissertation

Complete thesis writing, research guidance, and formatting support

Expert Help

📄

Assignment Help

Quality assignment writing, editing, and proofreading services

100% Original

🔬

PhD Research

Research proposal, literature review, data analysis & publication

PhD Level

📊

Project Guidance

Academic projects, mini projects, and final year project support

Hands-on

📞 Need custom support? Contact us directly!

Chat on WhatsApp → Email Us

Final Year Projects