freecodecamp-data-analysis/medical-data-visualizer
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Fix README typos and code highlighting (#3)

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Co-authored-by: DepressMode <trekkiemonster345@gmail.com>
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README.md
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@ -4,7 +4,7 @@ In this project, you will visualize and make calculations from medical examinati
#### Data description #### Data description
The rows in the dataset represent patiets and the columns represent information like body measurements, results from various blood tests, and lifestyle choices. You will use the dataset to exploring the relationship between cardiac disease, body measurements, blood markers, and lifestyle choices. The rows in the dataset represent patients and the columns represent information like body measurements, results from various blood tests, and lifestyle choices. You will use the dataset to explore the relationship between cardiac disease, body measurements, blood markers, and lifestyle choices.
File name: medical_examination.csv File name: medical_examination.csv
@ -25,21 +25,21 @@ File name: medical_examination.csv
#### Tasks #### Tasks
Create a chart similar to `examples/Figure_1.png`, where we show the counts of good and bad outcomes for cholesterol, gluc, alco variable, active, and smoke for patients with cardio=1 and cardio=0 in different panels. 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`: 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. * 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 data by making 0 always good and 1 always bad. If the value of 'cholestorol' or 'gluc' is 1, make the value 0. If the value is more than 1, make the value 1. * 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". * 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: * Clean the data. Filter out the following patient segments that represent incorrect data:
- diastolic pressure is higher then systolic (Keep the correct data with `df['ap_lo'] <= df['ap_hi'])`) - 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 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 - height is more than the 97.5th percentile
- weight is less then the 2.5th percentile - weight is less than the 2.5th percentile
- weight is more than the 97.5th 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". * 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. 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`. Unit tests are written for you under `test_module.py`.