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97 lines
5.9 KiB
Markdown
97 lines
5.9 KiB
Markdown
---
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id: 5e46f7f8ac417301a38fb92a
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title: Medical Data Visualizer
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challengeType: 10
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forumTopicId: 462368
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dashedName: medical-data-visualizer
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---
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# --description--
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Du wirst <a href="https://replit.com/github/freeCodeCamp/boilerplate-medical-data-visualizer" target="_blank" rel="noopener noreferrer nofollow">mit unserem Replit-Startercode an diesem Projekt arbeiten</a>.
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- Start by importing the project on Replit.
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- Next, you will see a `.replit` window.
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- Select `Use run command` and click the `Done` button.
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Wir sind noch dabei, den interaktiven Teil des Python-Kurses zu entwickeln. Hier sind erstmal einige Videos auf dem freeCodeCamp.org YouTube-Kanal, die dir alles beibringen, was du wissen musst, um dieses Projekt abzuschließen:
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- <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a>(14 hours)
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- <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)
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# --instructions--
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In diesem Projekt wirst du mit Hilfe von matplotlib, seaborn und pandas Berechnungen aus medizinischen Untersuchungsdaten visualisieren und durchführen. Die Datensatzwerte wurden bei medizinischen Untersuchungen gesammelt.
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## Datenbeschreibung
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Die Zeilen des Datensatzes repräsentieren Patienten und die Spalten stellen Informationen wie Körpermessungen, Ergebnisse verschiedener Bluttests und Lebensweisen dar. Du wirst den Datensatz verwenden, um die Beziehung zwischen Herzkrankheiten, Körpermessungen, Blutmarkern und Lebensweisen zu erforschen.
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Dateiname: medical_examination.csv
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| Merkmal | Variablentyp | Variable | Wert |
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|:----------------------------------------:|:-------------------:|:-------------:|:------------------------------------------------:|
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| Alter | Objective Feature | `age` | int (Tage) |
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| Größe | Objective Feature | `height` | int (cm) |
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| Gewicht | Objective Feature | `weight` | float (kg) |
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| Geschlecht | Objective Feature | `gender` | Kategorie-Code |
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| Systolischer Blutdruck | Examination Feature | `ap_hi` | int |
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| Diastolischer Blutdruck | Examination Feature | `ap_lo` | int |
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| Cholesterin | Examination Feature | `cholesterol` | 1: normal, 2: above normal, 3: well above normal |
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| Glucose | Examination Feature | `gluc` | 1: normal, 2: above normal, 3: well above normal |
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| Rauchen | Subjective Feature | `smoke` | binary |
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| Alkoholkonsum | Subjective Feature | `alco` | binary |
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| Körperliche Aktivität | Subjective Feature | `active` | binary |
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| Leiden unter Herz-Kreislauf-Erkrankungen | Target Variable | `cardio` | binary |
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## Tasks
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Create a chart similar to `examples/Figure_1.png`, where we show the counts of good and bad outcomes for the `cholesterol`, `gluc`, `alco`, `active`, and `smoke` variables for patients with cardio=1 and cardio=0 in different panels.
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Use the data to complete the following tasks in `medical_data_visualizer.py`:
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- Add an `overweight` column to the data. To determine if a person is overweight, first calculate their BMI by dividing their weight in kilograms by the square of their height in meters. If that value is > 25 then the person is overweight. Use the value 0 for NOT overweight and the value 1 for overweight.
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- Normalize the data by making 0 always good and 1 always bad. If the value of `cholesterol` or `gluc` is 1, make the value 0. If the value is more than 1, make the value 1.
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- Convert the data into long format and create a chart that shows the value counts of the categorical features using seaborn's `catplot()`. The dataset should be split by 'Cardio' so there is one chart for each `cardio` value. The chart should look like `examples/Figure_1.png`.
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- Clean the data. Filter out the following patient segments that represent incorrect data:
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- diastolic pressure is higher than systolic (Keep the correct data with `(df['ap_lo'] <= df['ap_hi'])`)
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- height is less than the 2.5th percentile (Keep the correct data with `(df['height'] >= df['height'].quantile(0.025))`)
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- height is more than the 97.5th percentile
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- weight is less than the 2.5th percentile
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- weight is more than the 97.5th percentile
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- Create a correlation matrix using the dataset. Plot the correlation matrix using seaborn's `heatmap()`. Mask the upper triangle. The chart should look like `examples/Figure_2.png`.
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Any time a variable is set to `None`, make sure to set it to the correct code.
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Unit tests are written for you under `test_module.py`.
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## Development
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For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
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## Testing
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We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
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## Submitting
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Copy your project's URL and submit it to freeCodeCamp.
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# --hints--
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It should pass all Python tests.
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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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