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PROJECT
Analyzing Cardiovascular Risk Factors with R
In this project, we’ll learn to analyze and understand different risk factors that may cause cardiovascular diseases using machine learning models.
You will learn to:
Load and process the data frame in R.
Preprocess, analyze, and explore datasets using R.
Apply GLM on datasets after preprocessing using R.
Evaluate GLM accuracy.
Skills
Data Cleaning
Data Visualization
Data Analysis
Prerequisites
Intermediate coding skills in R
Intermediate knowledge about plotting graphs in R
Basic understanding of statistical tools
Basic understanding of the general linear model
Technology
Rlang
Project Description
In this project, we’ll start with raw data, perform preprocessing, and then utilize various machine learning techniques to extract binary outcomes from the data, explaining which factors actually have a role to play in cardiac diseases and which don't.
We have a dataset consisting of around 70,000 records that contain various heart-related data for different individuals. This data includes indicators such as whether they are cardiac patients, their smoking status, activity level, blood pressure condition, and other relevant information.
We'll conduct an analysis of various habits among individuals based on the available data to determine whether they are cardiac patients or not. We'll apply chi-squared tests and develop new insights from the data to help us analyze better. We'll create a model for making a conclusive argument about our analysis.
Project Tasks
1
Data Preprocessing
Task 0: Description of the Dataset
Task 1: Import the Dataset and Libraries
Task 2: Process the Data for Analysis
Task 3: Calculate the BMI and MAP
2
Data Analysis
Task 4: The Effects of Alcohol Consumption on Cardiac Disease
Task 5: The Effects of Activity on Cardiac Disease
Task 6: Analysis of Smoking Habits and Cardiac Disease
Task 7: Create a Composite Plot
Task 8: The Effects of Gender on Cardiac Disease
Task 9: Effects of Cholesterol on Cardiac Disease
Task 10: Effects of Glucose on Cardiac Disease
Task 11: Composite Plot of Cholesterol and Glucose
Task 12: The Effects of Age on Cardiac Disease
Task 13: Interquartile Range Method
Task 14: Effects of BMI on Cardiac Disease
Task 15: Effects of MAP on Cardiac Disease
3
Evaluation of Model
Task 16: General Linear Model
Task 17: Accuracy of Model
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.