MachineLearningRegressionModel

This project demonstrates a machine learning regression pipeline using a Decision Tree Regressor to predict insurance charges based on demographic and lifestyle features.

Dataset

The model is trained on the insurance.csv dataset, which contains the following columns:

• age
• sex
• bmi
• children
• smoker
• region
• charges (target)

Features

• Data preprocessing with one-hot encoding for categorical variables.
• Train/test split with stratification.
• Decision Tree regression with hyperparameter tuning.
• Model evaluation using R² score.
• Model serialization with joblib.
• Optional upload to Hugging Face Hub.

Usage

1. Install dependencies:

   pip install -r requirements.txt

2. Prepare your environment:

   • Create a .env file with your Hugging Face credentials (optional for model upload):

     HF_TOKEN=your_huggingface_token
     HF_REPO_ID=your-username/your-repo-name
     

3. Run the training and upload script:

   python hugging-face/decision_tree_regressor_upload.py

Model Upload

If both HF_TOKEN and HF_REPO_ID are set, the trained model will be uploaded to the Hugging Face Hub under the repository specified by HF_REPO_ID:

HF_REPO_ID=your-username/your-repo-name

License

This project is licensed under the MIT License.