Medical Insurance Cost Prediction

Overview

This project focuses on predicting individual medical insurance costs using demographic and health-related features. By leveraging machine learning models, the repository provides insights into how factors such as age, BMI, and smoking status affect insurance premiums.

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Features

• Data Preprocessing: Handling missing values, encoding categorical data, and feature scaling.
• Exploratory Data Analysis (EDA): Visualizing trends and correlations between features and insurance costs.
• Model Training: Implementing machine learning algorithms to predict insurance charges.
• Performance Metrics: Evaluating the accuracy and reliability of models using metrics like Mean Absolute Error (MAE).

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Project Structure

Medical-Insurance/
├── data/                  # Dataset for training and testing
├── notebooks/             # Jupyter notebooks for EDA and model development
├── scripts/               # Python scripts for preprocessing and model training
├── visualizations/        # Charts and graphs for insights
├── models/                # Trained machine learning models
├── README.md              # Project documentation
└── LICENSE                # License information

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Technologies Used

• Python: Core programming language.
• Pandas: For data manipulation and preprocessing.
• Matplotlib/Seaborn: Visualizing relationships between features.
• Scikit-learn: Building and evaluating machine learning models.
• NumPy: Efficient numerical computations.

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Dataset

The dataset includes the following features:

• Age: Age of the individual.
• Sex: Gender (male/female).
• BMI: Body mass index.
• Children: Number of dependents.
• Smoker: Whether the individual is a smoker.
• Region: Geographical region.
• Charges: Medical insurance costs (target variable).

The dataset can be sourced from platforms such as Kaggle.

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Resources

For further reading and reference:

1. Medical Cost Personal Dataset - Kaggle
2. Scikit-learn Documentation
3. Exploratory Data Analysis Guide