binary classification for the presence of heart disease performed data explotoray analysis and data visualization using pandas and seaborn, model training using logistic regrission, decision tree, and random forest, hyperparameter tuning using grid search
This data set dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V. It contains 76 attributes, including the predicted attribute, but all published experiments refer to using a subset of 14 of them. The "target" field refers to the presence of heart disease in the patient. It is integer valued 0 = no disease and 1 = disease.
age: age in years
sex: sex
1 = male
0 = female
cp: chest pain type
Value 0: typical angina
Value 1: atypical angina
Value 2: non-anginal pain
Value 3: asymptomatic
trestbps: resting blood pressure (in mm Hg on admission to the hospital)
chol: serum cholestoral in mg/dl
fbs: (fasting blood sugar > 120 mg/dl)
1 = true;
0 = false
restecg: resting electrocardiographic results
Value 0: normal
Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
thalach: maximum heart rate achieved
exang: exercise induced angina
1 = yes
0 = no
oldpeak = ST depression induced by exercise relative to rest
slope: the slope of the peak exercise ST segment
Value 0: upsloping
Value 1: flat
Value 2: downsloping
ca: number of major vessels (0-3) colored by flourosopy
thal:
0 = error (in the original dataset 0 maps to NaN's)
1 = fixed defect
2 = normal
3 = reversable defect
target
0 = no disease,
1 = disease