💸 Insurance Charges Prediction

This project predicts individual medical insurance charges based on demographic and lifestyle factors using machine learning regression models.

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📁 Dataset

The dataset includes the following features:

• age — Age of the policyholder
• sex — Gender
• bmi — Body Mass Index
• children — Number of dependents
• smoker — Smoker status
• region — Residential region
• charges — Target variable (Insurance premium)

The dataset is available in the data/ directory.

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📊 Methodology

🧪 Data Exploration

• View summary statistics and unique categorical values.
• Visualize distributions of key variables like age, BMI, and charges.

🧼 Data Preprocessing

• Handle missing values (if any).
• Encode categorical variables using one-hot encoding.
• Standardize numerical columns for uniformity.

🤖 Model Training

Train the following regression models:

• K-Nearest Neighbors (KNN)
• Linear Regression
• Support Vector Machine (SVM)
• Decision Tree Regressor
• Random Forest Regressor

📏 Model Evaluation

• Metric: Mean Absolute Error (MAE) on the test set.

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📈 Results

Model	MAE
KNN	3532.65
Linear Regression	4243.65
SVM	8478.46
Decision Tree	2817.28
Random Forest	2575.89 ✅

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🔍 Conclusion

• The Random Forest Regressor outperformed all other models with the lowest Mean Absolute Error of 2575.89.
• Simpler models like Linear Regression and SVM were less effective for this dataset.
• Proper preprocessing (like encoding and scaling) had a significant impact on overall model performance.

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⚙️ Tools Used

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