Introduction to the Project

This is a introductory project of Machine Learning and Data Science concepts by exploring the problem of heart disease classification.

It is intended to be an end-to-end example of what a Data Science and Machine Learning proof of concept might look like.

we'll look at the following topics.

• Exploratory data analysis (EDA) - the process of reviewing a dataset and finding out more about it.
• Model training - create a model(s) to learn to predict a target variable based on other variables.
• Model evaluation - evaluating a models predictions using problem-specific evaluation metrics.
• Model comparison - comparing several different models to find the best one.
• Model fine-tuning - once we've found a good model, how can we improve it?
• Feature importance - since we're predicting the presence of heart disease, are there some more important things for prediction?
• Cross validation - if we build a good model, can we be sure it will work on unseen data?
• Reporting what we've found - if we had to present our work, what would we show someone?

To work through these topics, we'll use Pandas, Matplotlib, and NumPy for Data Analysis, as well as, Scikit-Learn for Machine Learning and modeling tasks.

Data Source

we've downloaded it in a formatted way from Kaggle. https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

Heart Disease Data Dictionary The following are the features we'll use to predict our target variable (heart disease or no heart disease).

1. age - age in years
2. sex - (1 = male; 0 = female)
3. cp - chest pain type
   • 0: Typical angina: chest pain related decrease blood supply to the heart
   • 1: Atypical angina: chest pain not related to heart
   • 2: Non-anginal pain: typically esophageal spasms (non heart related)
   • 3: Asymptomatic: chest pain not showing signs of disease
4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)
   • anything above 130-140 is typically cause for concern
5. chol - serum cholestoral in mg/dl
   • serum = LDL + HDL + .2 * triglycerides
   • above 200 is cause for concern
6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
   • '>126' mg/dL signals diabetes
7. restecg - resting electrocardiographic results 0. : Nothing to note
   1. : ST-T Wave abnormality
      • can range from mild symptoms to severe problems
      • signals non-normal heart beat
   2. : Possible or definite left ventricular hypertrophy
      • Enlarged heart's main pumping chamber
8. thalach - maximum heart rate achieved
9. exang - exercise induced angina (1 = yes; 0 = no)
10. oldpeak - ST depression induced by exercise relative to rest
    • looks at stress of heart during excercise
    • unhealthy heart will stress more
11. slope - the slope of the peak exercise ST segment 0. : Upsloping: better heart rate with excercise (uncommon)
    1. : Flatsloping: minimal change (typical healthy heart)
    2. : Downslopins: signs of unhealthy heart
12. ca - number of major vessels (0-3) colored by flourosopy
    • colored vessel means the doctor can see the blood passing through
    • the more blood movement the better (no clots)
13. thal - thalium stress result
    • 1,3: normal
    • 6: fixed defect: used to be defect but ok now
    • 7: reversable defect: no proper blood movement when excercising
14. target - have disease or not (1=yes, 0=no) (= the predicted attribute) Note: No personal identifiable information (PPI) can be found in the dataset.