All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
We will not working with 'Unnamed: 32' and 'id' variables.
Store the features in X and the target y.
Set the random_state parameter to a desired integer value for reproducibility.
Store the values in the variables in
X_train,X_test,y_trainandy_test.
Create an instance of the KNeighborsClassifier and store the model in knn. Use the argument for defect.
It's time to train the KNeighborsClassifier using the training dataset.
Use the trained model to make predictions on the test data. Store the prediction in y_pred.
Calculate the f1-score of the testing set and run the code in a Jupyter Notebook.
Store the results in the variable
f1_score_test.
