Train/Test split
To avoid overfitting, it's common practice in Machine Learning to split data into train and test datasets. This is done to ensure that the model is able to correctly predict new, unseen data.
Since we're working with time-series data, we cannot use random split methods, as that would allow the model to know the future.
A function to print the start and end of a DataFrame is available as show_start_end(), which takes a DataFrame as the only argument, and returns a string.
The data is available as environment.
Cet exercice fait partie du cours
Analyzing IoT Data in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Define the split day
limit_day = ____
# Split the data
train_env = ____[____]
test_env = ____[____]