Cardiovascular Disease XGBoost Classification

Jupyter Notebook that trains an XGBoost model to perform classification to detect the presence of cardiovascular disease in a person based on the given features. Trains an XGBoost model locally using SciKitLearn, performs hyperparameter tuning locally before finally training and deploying a SageMaker XGBoost model.

Original Data Source: https://www.kaggle.com/sulianova/cardiovascular-disease-dataset

Features:

• 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:

• Load the Dataset
• Perform Exploratory Data Analysis
• Perform Data Visualisation
• Create Training and Testing Datasets
• Train an XG-Boost classifier model (locally not using SageMaker)
• Perform GridSearch Hyperparameter Optimisation
• Train an XG-Boost classifier model (using Amazon SageMaker)
• Deploy trained model as an endpoint
• Assess trained model performance
• Delete the endpoint