Random Forest
CVD Risk Prediction with Random Forest
Jan 12, 2022
Project Description
The goal is to create a reliable and efficient cardiovascular disease (CVD) risk prediction model using the random forest algorithm. Data exploration insights using Seaborn and Matplotlib visualizations, along with data preprocessing capabilities from the Scikit-learn library, contribute to building an accurate risk assessment model.
This project takes a structured, end-to-end machine learning approach, including:
Exploratory Data Analysis: Gain insights into the dataset through visual exploration of cardiovascular risk factors using Seaborn and Matplotlib.
Data Preprocessing: Apply Scikit-learn preprocessing techniques to prepare the data for modeling, such as standardization, encoding categorical variables, etc.
Model Training: Build a random forest classifier model on the preprocessed data to predict CVD risk.
Model Evaluation: Evaluate model performance metrics like accuracy, precision, recall, etc. through cross-validation.
Insight Generation: Interpret model results and their potential real-world implications for healthcare and preventative medicine.
Robust CVD risk predictions from this model could assist medical professionals in making more informed patient care and disease prevention decisions.