You will be working on this project with our Replit starter code.
Start by importing the project on Replit. Next, you will see a window..replit Select and click the button.Use run commandDone We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:
Python for Everybody Video Course(14 hours)
How to Analyze Data with Python Pandas (10 hours)
In this project, you will visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas. The dataset values were collected during medical examinations.
Data description 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
Feature Variable Type Variable Value Type Age Objective Feature age int (days) Height Objective Feature height int (cm) Weight Objective Feature weight float (kg) Gender Objective Feature gender categorical code Systolic blood pressure Examination Feature ap_hi int Diastolic blood pressure Examination Feature ap_lo int Cholesterol 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 Smoking Subjective Feature smoke binary Alcohol intake Subjective Feature alco binary Physical activity Subjective Feature active binary Presence or absence of cardiovascular disease Target Variable cardio binary Tasks Create a chart similar to , where we show the counts of good and bad outcomes for the , , , , and variables for patients with cardio=1 and cardio=0 in different panels.examples/Figure_1.pngcholesterolglucalcoactivesmoke
Use the data to complete the following tasks in :medical_data_visualizer.py
Add an 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.overweight Normalize the data by making 0 always good and 1 always bad. If the value of or is 1, make the value 0. If the value is more than 1, make the value 1.cholesterolgluc Convert the data into long format and create a chart that shows the value counts of the categorical features using seaborn's . The dataset should be split by 'Cardio' so there is one chart for each value. The chart should look like .catplot()cardioexamples/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 . Mask the upper triangle. The chart should look like .heatmap()examples/Figure_2.png Any time a variable is set to , make sure to set it to the correct code.None