Complete the code to calculate the accuracy of an agent's predictions.
accuracy = sum(predictions == [1]) / len(predictions)
The accuracy is calculated by comparing the agent's predictions to the true labels.
Complete the code to split data into training and testing sets.
train_data, test_data = data[:[1]], data[[1]:]
We split data at 70% length to create training and testing sets.
Fix the error in the code that calculates the mean squared error (MSE).
mse = sum((predictions - [1]) ** 2) / len(predictions)
MSE compares predictions to true labels to measure error.
Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.
{word: [1] for word in words if len(word) [2] 3}The dictionary maps each word to its length only if the word is longer than 3 letters.
Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.
result = { [1]: [2] for k, v in data.items() if v [3] 0 }This creates a dictionary with keys in uppercase and values only if they are greater than zero.