No, we did not.
Actually due to less time, we went on with Predictions.
We checked it now. So it was 77.8% on unseen data

Yup, that was the approach. Thank you for the notes.
we will consider them and try to improve the Model

knn_data[0] means features
knn_data[1] means labels.
It is just a representation and has nothing to do with K-Nearest Neighbours.
While preparing the dictionary we forgot using pandas, it can be used too.
We are Predicting according to that user like we take all the Articles read by the user as his/her history and predict the next one.
We used the window concept like me use when there is time-series data.
The loop there helps us to differentiate one user from another

Thank You so much Sir for your suggestions.
The evaluation results are high because they were taken on the training data itself.
Sir, we will check out this example and try to combine both Content-Based and Collaborative filtering