Linear regressor medical cost predicter

A scikit-learn implementation of linear regressors.

Key Features • How To Use • Credits • License

Key Features

This machine learning model predicts the amount of charge of a given patient. This prediction is a number given in dollars. The dataset is taken from the Medical Cost Personal Datasets . So here are the key features of this project:

• Prediction is based on this patient's features:

  • age: age of primary beneficiary

  • sex: insurance contractor gender, female, male

  • bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9

  • children: Number of children covered by health insurance / Number of dependents

  • smoker: Smoking

  • region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

  • charges: Individual medical costs billed by health insurance

• Conventional notebook data science scheme.

• Specialized dataviz tools .

• Based on Scikit-Learn modules and functions such like:

  • preprocessing.StandardScaler : Data standarization.
  • linear_model.LinearRegression : Linear Regression modeling.
  • metrics.r2_score : R2 score metric.

How To Use

To clone and run this application, you will need Git.

# Clone this repository
$ git clone https://github.com/santiagoahl/medical-cost-prediction.git

# Go into the repository
$ cd linear-algebra-in-python

# Install Jupyter Notebooks
$ pip install jupyterlab
$jupyter-lab
$pip install notebook

# Run
$jupyter notebook

Credits

This model uses the following open source datasets packages:

• Scikit-learn
• Insurance US Census Bureau
• Pandas
• Numpy
• Seaborn
• Matplotlib

License

MIT

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