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
Set the random_state parameter to a desired integer value for reproducibility.
Store the values in the variables in
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
Calculate the f1-score of the testing set and run the code in a Jupyter Notebook.
Store the results in the variable