Build a classification model
While eye-balling differences is a useful way to gain an intuition for the data, let's see if you can operationalize things with a model. In this exercise, you will use each repetition as a datapoint, and each moment in time as a feature to fit a classifier that attempts to predict abnormal vs. normal heartbeats using only the raw data.
We've split the two DataFrames (normal and abnormal) into X_train, X_test, y_train, and y_test.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Exercise instructions
- Create an instance of the Linear SVC model and fit the model using the training data.
- Use the testing data to generate predictions with the model.
- Score the model using the provided code.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.svm import LinearSVC
# Initialize and fit the model
model = ____
model.____
# Generate predictions and score them manually
predictions = model.____
print(sum(predictions == y_test.squeeze()) / len(y_test))