Key Features • How To Use • Credits • License
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:
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Prediction is based on this patient's features:
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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
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Conventional notebook data science scheme.
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Specialized dataviz tools .
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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.
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
This model uses the following open source datasets packages:
MIT
Web Site santiagoal.super.site · GitHub @santiagoahl · Twitter @sahumadaloz