Python Sklearn Support Vector Machine (SVM) Tutorial with Example

Python Sklearn Support Vector Machine (SVM) Tutorial with Example

iii) Details about Dataset

Our heart dataset has 303 rows and 14 columns. The significance of all the attributes present in the dataset is given below –

  • age: The person’s age in years
  • sex: The person’s sex (1 = male, 0 = female)
  • cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)
  • trestbps: The person’s resting blood pressure (mm Hg on admission to the hospital) chol: The person’s cholesterol measurement in mg/dl
  • fbs: The person’s fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)
  • restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes’ criteria)
  • thalach: The person’s maximum heart rate achieved
  • exang: Exercise induced angina (1 = yes; 0 = no)
  • oldpeak: ST depression induced by exercise relative to rest (‘ST’ relates to positions on the ECG plot. See more here)
  • slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)
  • ca: The number of major vessels (0-3)
  • thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversible defect)
  • target: Heart disease (0 = no, 1 = yes)

Out[5]:

vii) Fetching Best Hyperparameters

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