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Let us now implement the function which calculates the accuracy. This function takes as arguments the predicted labels and the actuals of the corresponding dataset.

We do this in 2 steps:

Using

`np.abs(Y_predicted - Y_actual)`

, we calculate the absolute difference between the actual labels and predicted labels.Then, we use

`np.mean()`

and calculate accuracy.

**Note**:

`np.abs`

gets the absolute value of each element in the input array.`np.mean`

returns the mean of the elements in the input array.

Let us assume

`y_actual`

and`y_predicted`

are the actual labels and predicted labels respectively. Copy the following code.`y_actual = np.array([1,1,1,0,1]) print("y_actual :", y_actual ) y_predicted = np.array([1,0,0,0,1]) print("y_predicted :", y_predicted )`

Get the absolute differences of the corresponding elements in

`y_actual`

and`y_predicted`

using`np.abs()`

, and store them in`c`

.`c = << your code comes here >>(y_actual - y_predicted)`

Store the mean of the elements of

`c`

in`c_mean`

using`np.mean`

.`c_mean = << your code goes here >>(c)`

Accuracy could be calculated as:

`accuracy = 100 - (c_mean * 100)`

This logic is written in the following

`get_accuracies`

function. Copy-paste the following`get_accuracies`

function.`def get_accuracies(Y_predicted, Y_actual): abs_diff = np.abs(Y_predicted - Y_actual) accuracy = 100 - np.mean(abs_diff) * 100 return accuracy`

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