WhizML-CVD_Analysis

This repository illustrates an application of the WhizML codebase for an analysis of cardiovascular disease risk.

About the Dataset

The dataset used is the Cardiovascular Diseases Risk Prediction dataset obtained from Kaggle.

Auto-EDA

Running the eda pipeline will launch the following Auto-EDA dashboard, allowing the users to observe the dataset.

Data Preprocessing

Users can implement custom functions to preprocess the data. In our case, the preprocessing codes can be found in Data_Preprocessing.ipynb, inside the notebooks directory.

ML Models Experimentation

The model_experimentation triggered the training of various Logistic Regression, Random Forest, and XGBoost models.

Model Explainability

Model explainability can be further explored using the model_explainability pipeline.

Bias Analysis

Bias analysis can be performed using the Aequitas web app, with the data provided by using the bias_analysis_data_prep pipeline.

Data Drift Analysis

As new data is obtained, drift detection can be performed using the data_drift_analysis pipeline.

Note: To create a hypothetical example, some rows were sampled from the original dataset and were assumed to be the new data.