freeCodeCamp/curriculum/challenges/german/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md

id, title, challengeType, forumTopicId, dashedName

id	title	challengeType	forumTopicId	dashedName
5e46f7f8ac417301a38fb92a	Medical Data Visualizer	10	462368	medical-data-visualizer

--description--

Du wirst mit unserem Replit-Startercode an diesem Projekt arbeiten.

• Start by importing the project on Replit.
• Next, you will see a .replit window.
• Select Use run command and click the Done button.

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:

• Python for Everybody Video Course(14 hours)

• How to Analyze Data with Python Pandas (10 hours)

--instructions--

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.

Datenbeschreibung

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.

Dateiname: medical_examination.csv

Merkmal	Variablentyp	Variable	Wert
Alter	Objective Feature	age	int (Tage)
Größe	Objective Feature	height	int (cm)
Gewicht	Objective Feature	weight	float (kg)
Geschlecht	Objective Feature	gender	Kategorie-Code
Systolischer Blutdruck	Examination Feature	ap_hi	int
Diastolischer Blutdruck	Examination Feature	ap_lo	int
Cholesterin	Examination Feature	cholesterol	1: normal, 2: above normal, 3: well above normal
Glucose	Examination Feature	gluc	1: normal, 2: above normal, 3: well above normal
Rauchen	Subjective Feature	smoke	binary
Alkoholkonsum	Subjective Feature	alco	binary
Körperliche Aktivität	Subjective Feature	active	binary
Leiden unter Herz-Kreislauf-Erkrankungen	Target Variable	cardio	binary

Tasks

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.

Use the data to complete the following tasks in medical_data_visualizer.py:

• 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.
• 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.
• 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.
• Clean the data. Filter out the following patient segments that represent incorrect data:
  • diastolic pressure is higher than systolic (Keep the correct data with (df['ap_lo'] <= df['ap_hi']))
  • height is less than the 2.5th percentile (Keep the correct data with (df['height'] >= df['height'].quantile(0.025)))
  • height is more than the 97.5th percentile
  • weight is less than the 2.5th percentile
  • weight is more than the 97.5th percentile
• 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.

Any time a variable is set to None, make sure to set it to the correct code.

Unit tests are written for you under test_module.py.

Development

For development, you can use main.py to test your functions. Click the "run" button and main.py will run.

Testing

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.

Submitting

Copy your project's URL and submit it to freeCodeCamp.

--hints--

It should pass all Python tests.

--solutions--

  # Python challenges don't need solutions,
  # because they would need to be tested against a full working project.
  # Please check our contributing guidelines to learn more.