<jupyter_start><jupyter_text>Accuracy In simple terms it is defined as the total number of correct predictions made by the model over all the predictions made.  Implementing the Accuracy<jupyter_code>from sklearn.datasets import load_breast_cancer

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

#get the data
data = load_breast_cancer()

x = data.data

y = data.target

#split the data
x_train, x_test, y_train, y_test = train_test_split(x, y)
model = LogisticRegression(max_iter=2000)

model.fit(x_train, y_train)
prediction = model.predict(x_test)
print(prediction)
#importing accuracy
from sklearn.metrics import accuracy_score
#getting our accuracy
accuracy = accuracy_score(prediction, y_test)

print("The Model's Accuracy is:")

print(accuracy)<jupyter_output>The Model's Accuracy is:
0.9440559440559441
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