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50 lines
2.5 KiB
Markdown
50 lines
2.5 KiB
Markdown
---
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id: 5e46f8edac417301a38fb930
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title: Calculadora de costos de salud por regresión lineal
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challengeType: 10
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forumTopicId: 462379
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dashedName: linear-regression-health-costs-calculator
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---
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# --description--
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You will be <a href="https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb" target="_blank" rel="noopener noreferrer nofollow">working on this project with Google Colaboratory</a>.
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Después de acceder al enlace, crea una copia del cuaderno ya sea en tu propia cuenta o de manera local. Una vez hayas completado el proyecto y superes el test (incluido en el enlace), envía el enlace del proyecto a continuación. Si envias un enlace a Google Colaboratory, asegúrate de que activas el uso compartido para "cualquiera con el enlace."
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Todavía estamos desarrollando el contenido instructivo interactivo para el plan de estudios de aprendizaje automático. Por ahora, puedes pasar por los desafíos en video de esta certificación. También puede que tengas que buscar recursos de aprendizaje adicionales, similares a lo que harías cuando trabajas en un proyecto del mundo real.
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# --instructions--
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In this challenge, you will predict healthcare costs using a regression algorithm.
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You are given a dataset that contains information about different people including their healthcare costs. Use the data to predict healthcare costs based on new data.
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The first two cells of this notebook import libraries and the data.
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Make sure to convert categorical data to numbers. Use 80% of the data as the `train_dataset` and 20% of the data as the `test_dataset`.
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`pop` off the "expenses" column from these datasets to create new datasets called `train_labels` and `test_labels`. Use these labels when training your model.
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Create a model and train it with the `train_dataset`. Run the final cell in this notebook to check your model. The final cell will use the unseen `test_dataset` to check how well the model generalizes.
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To pass the challenge, `model.evaluate` must return a Mean Absolute Error of under 3500. This means it predicts health care costs correctly within $3500.
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The final cell will also predict expenses using the `test_dataset` and graph the results.
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# --hints--
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Debería pasar todas las pruebas de Python.
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```js
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```
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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