💊 Medical Insurance Price Prediction using Machine Learning 📌 Project Overview

The Medical Insurance Price Prediction project focuses on predicting the cost of medical insurance using machine learning techniques. Insurance companies determine premium costs based on several personal and lifestyle factors such as age, BMI, smoking habits, number of dependents, gender, and region.

This project analyzes these variables and builds a predictive machine learning model to estimate the insurance charges for individuals based on their attributes.

The objective is to demonstrate how data analysis and machine learning can support pricing strategies and risk assessment in the healthcare insurance industry.

🎯 Project Objectives

• Analyze medical insurance dataset • Identify factors affecting insurance costs • Perform Exploratory Data Analysis (EDA) • Build a machine learning model to predict insurance charges • Evaluate model performance

📊 Dataset Information

The dataset typically contains the following features:

Age

Gender

BMI (Body Mass Index)

Number of children / dependents

Smoking status

Region

Medical insurance charges

These variables are used as input features to predict the insurance premium price.

🔎 Exploratory Data Analysis (EDA)

EDA was performed to understand relationships between variables and insurance charges.

Analysis included:

• Distribution of insurance charges • Correlation analysis between features • Impact of smoking on insurance cost • Effect of BMI and age on insurance premium

Data visualization techniques help identify patterns and trends in healthcare expenses.

🧠 Machine Learning Model

The project applies machine learning algorithms to predict medical insurance prices.

Typical steps include:

1️⃣ Data preprocessing 2️⃣ Feature selection 3️⃣ Train-test data split 4️⃣ Model training 5️⃣ Model evaluation

Machine learning libraries such as Scikit-learn provide algorithms for regression, classification, and model evaluation in Python.

🛠 Technologies Used

Programming Language

Python

Libraries

Pandas

NumPy

Matplotlib

Seaborn

Scikit-learn

Tools

Jupyter Notebook

Git

GitHub

📈 Applications

This project demonstrates how predictive analytics can be used in:

• Insurance premium estimation • Healthcare cost analysis • Risk assessment models • Data-driven insurance pricing strategies 🔗 Project Repository

GitHub Link:https://github.com/atharv212004/Medical-Insurance-Price-Prediction-using-Machine-Learning-