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PROJECT

# Cardiovascular Disease Risk Prediction with Random Forest

In this project, we’ll explore cardiovascular disease data through seaborn visualizations and predict disease risk using an sklearn-based random forest classifier considering diverse factors.

You will learn to:

Explore data using seaborn.

Work with the building blocks of a deep learning model.

Create a random forest classifier using sklearn.

Make predictions using a trained deep learning model.

Skills

Machine Learning

Data Visualisation

Data Analysis

Prerequisites

Intermediate knowledge of Python

Intermediate knowledge of seaborn

Intermediate knowledge of sklearn

Intermediate knowledge of machine learning models

Technologies

Python

seaborn

Matplotlib

Scikit-learn

Project Description

In this project, we’ll create a robust predictive model to assess the risk of cardiovascular disease (CVD) using the random forest algorithm. A well-structured pipeline will be used in our approach, including data exploration with seaborn and Matplotlib and data preprocessing with the Scikit-learn (sklearn) library.

We’ll carefully curate a relevant dataset that includes critical characteristics such as age, gender, blood pressure, cholesterol levels, and other risk factors associated with CVDs. We’ll visualize the dataset’s attributes and investigate potential relationships between variables and the target (CVD risk) using seaborn and Matplotlib. Next, we’ll use Scikit-learn’s powerful tools to perform data preprocessing tasks such as handling missing values, encoding categorical variables, and normalizing or scaling numerical attributes. This ensures that the dataset is ready for the machine learning model to be trained on.

Ultimately, our project aims to create a reliable and efficient CVD risk prediction model using the random forest algorithm. Data exploration insights and Scikit-learn capabilities will contribute to accurate risk assessments, potentially assisting healthcare professionals in making informed patient care and disease prevention decisions.

Project Tasks

1

Explore the Dataset

Task 0: Get Started

Task 1: Import Modules

Task 2: Load the Dataset

Task 3: Create the Pair Grid

Task 4: Plot the Distribution of Categorical Features

Task 5: Plot the Distribution of Numerical Features

Task 6: Plot the Relation of Factors with Diseases

Task 7: Transform the Categorical Columns

Task 8: Split the Training and Testing Dataset

2

Build the Model

Task 9: Build the Classifier

Task 10: Train the Classifier

Task 11: Get Predictions

Task 12: Print the Confusion Matrix and ROC Score

Congratulations!