Implemetation of cost prediction for medical insurance using linear regression with python.
you can see the data set here:
| age | sex | bmi | children | smoker | region | charges |
|---|---|---|---|---|---|---|
| 19 | female | 27.9 | 0 | yes | southwest | 16884.924 |
| 18 | male | 33.77 | 1 | no | southeast | 1725.5523 |
| 28 | male | 33 | 3 | no | southeast | 4449.462 |
| 33 | male | 22.705 | 0 | no | northwest | 21984.47061 |
| 32 | male | 28.88 | 0 | no | northwest | 3866.8552 |
| 31 | female | 25.74 | 0 | no | southeast | 3756.6216 |
| 46 | female | 33.44 | 1 | no | southeast | 8240.5896 |
| 37 | female | 27.74 | 3 | no | northwest | 7281.5056 |
| ... | ... | ... | ... | ... | ... | ... |
overview of what our dataset is.
you can analyze data using the following code:
from predictor import InsuranceCostPredictor
icp = InsuranceCostPredictor('dataset/insurance.csv')
icp.analyze(hue='smoker', based_on=['age', 'bmi', 'smoker', 'charges'])see the example file to undrestand exactly how to use it.
to implement this project this libraries have been used:
- pandas
- matplotlib
- sklearn
- seaborn
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clone project using this code in you shell:
git clone https://github.com/Amir-Shamsi/insurance-cost-prediction-LR/.git
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install requirements
pip install -r requirements.txt
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Done 👾