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.
Filter the legendary Pokémon from the original dataset and store them in a new variable called legendaries_df.
If we take a quick look at the data we can identify clear differences between Pokémon stats in their relation to legendary status.
What's the average points of Attack on Legendary Pokémons?
Legendary Pokémons are more solid than normal Pokémons. Can you validate that?
What is the average percentage of extra Defense points that Legendary Pokémons have compared to normal Pokémons?
What kind of value we expect to have after encoding labels with LabelEncoder?
A common mistake is to simply assign a numerical value to each category, ignoring any inherent order. Based on previous discussions, is it a correct decision to use the Label Encoder to transform the "Type 1" and "Type 2" variables?"
Now that you have predicted whether Pokémons are legendaries or not over the test data, use the accuracy_score function to check your model score.
The score you got is:
Having your second model ready, use the accuracy_score function to check your model score over the test data.
The score you got is:
