https://github.com/alphacrypto246/insurance-charges-prediction

The Predicting Insurance Charges project uses Decision Tree Regression to predict insurance charges based on features like age, sex, BMI, and smoking habits. It involves data preprocessing, feature scaling, and model evaluation with metrics like MAE and R².
https://github.com/alphacrypto246/insurance-charges-prediction

machine-learning numpy pandas scikit-learn scikitlearn-machine-learning

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The Predicting Insurance Charges project uses Decision Tree Regression to predict insurance charges based on features like age, sex, BMI, and smoking habits. It involves data preprocessing, feature scaling, and model evaluation with metrics like MAE and R².

• Host: GitHub
• URL: https://github.com/alphacrypto246/insurance-charges-prediction
• Owner: alphacrypto246
• Created: 2024-12-02T08:38:37.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-02T08:39:34.000Z (2 months ago)
• Last Synced: 2024-12-18T09:19:15.817Z (about 2 months ago)
• Topics: machine-learning, numpy, pandas, scikit-learn, scikitlearn-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 3.65 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Predicting Insurance Charges Using Decision Tree Regression

## Project Overview

This project aims to predict insurance charges based on personal and demographic information using a Decision Tree Regression model. The dataset contains information such as age, sex, BMI, number of children, smoking habits, and the region of the insured individual. 

## Dataset Details

The dataset used for this project (`insurance.csv`) contains 1,338 entries with the following columns:

- `age` (int): The age of the individual.

- `sex` (str): The gender of the individual (`male` or `female`).

- `bmi` (float): Body Mass Index, a measure of body fat.

- `children` (int): Number of children/dependents covered by health insurance.

- `smoker` (str): Smoking habits (`yes` or `no`).

- `region` (str): Region of residence (`northeast`, `northwest`, `southeast`, `southwest`).

- `charges` (float): Medical insurance charges.

## Objective

The goal is to develop a machine learning model that predicts insurance charges (`charges`) based on the input features (`age`, `sex`, `bmi`, `children`, `smoker`, and `region`).

## Technologies and Tools Used

### Libraries:

1. **Pandas**: For data manipulation and analysis.

2. **NumPy**: For numerical computations.

3. **Matplotlib & Seaborn**: For data visualization.

4. **Scikit-learn**: For implementing the Decision Tree Regression model and evaluating its performance.

### Techniques:

- Data preprocessing: Handling categorical data using encoding techniques.

- Feature scaling: Applying techniques like Min-Max scaling and standardization.

- Machine Learning: Using Decision Tree Regression to predict insurance charges.

- Performance Metrics: Evaluated the model using metrics such as Mean Absolute Error (MAE) and R² score.

## Steps Performed

1. **Data Exploration**:

   - Visualized relationships between features and the target variable (`charges`).

   - Checked for missing values and cleaned the data.

2. **Data Preprocessing**:

   - Encoded categorical features (`sex`, `smoker`, and `region`) into numerical representations.

   - Split the dataset into training and testing sets using an 80-20 split.

   - Scaled features to improve model performance.

3. **Model Building**:

   - Built a Decision Tree Regressor using Scikit-learn.

   - Optimized hyperparameters to improve prediction accuracy.

4. **Evaluation**:

   - Measured model performance using Mean Absolute Error (MAE) and R² score.

   - Visualized predictions to understand model behavior.

## Results

- **R² Score**: Indicates how well the model explains variance in insurance charges.

- **MAE**: Measures the average prediction error in terms of insurance charges.