Insurance Charge Prediction using Linear Regression

This project focuses on predicting medical insurance charges using a Linear Regression machine learning model. The model estimates insurance costs based on personal and demographic attributes.

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Project Overview

Medical insurance costs depend on multiple factors such as age, BMI, smoking habits, and region. The objective of this project is to build a regression model that accurately predicts insurance charges using these features.

This is a supervised learning regression problem where the target variable (charges) is continuous.

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Dataset Information

The dataset used in this project is insurance.csv, which contains the following features:

• age : Age of the policyholder
• sex : Gender (male/female)
• bmi : Body Mass Index
• children : Number of dependents
• smoker : Smoking status (yes/no)
• region : Residential region (northeast, northwest, southeast, southwest)
• charges : Medical insurance cost (Target Variable)

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• Jupyter Notebook

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Project Workflow

1. Data Loading
2. Data Exploration and Analysis
3. Data Preprocessing
   • Encoding categorical variables
   • Feature selection
4. Train-test split
5. Model training using Linear Regression
6. Model evaluation
7. Prediction of insurance charges

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Model Evaluation Metrics

The model performance was evaluated using:

• R² Score
• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)

These metrics measure how accurately the model predicts insurance charges.

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How to Run the Project

1. Clone the repository:

   git clone https://github.com/Divyansh1802/Insurance-Charge-Predictor.git

2. Navigate to the project folder:

   cd Insurance-Charge-Predictor

3. Install required dependencies:

   pip install -r requirements.txt

4. Open the Jupyter Notebook and run all cells.

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Example Prediction

Input:

• Age: 30
• Sex: Male
• BMI: 25.3
• Children: 1
• Smoker: No
• Region: Southeast

Output:

• Predicted Insurance Charges: (Model Generated Value)

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Future Improvements

• Apply advanced regression models (Random Forest, Gradient Boosting, XGBoost)
• Perform hyperparameter tuning
• Deploy the model using Flask or Streamlit
• Build a user-friendly interface for real-time predictions

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Author

Divyansh Upadhyay