Page 347 - AI Ver 3.0 Class 11
P. 347

# Calculate the accuracy of the model
                 accuracy = accuracy_score(y_test, y_pred)
                 print(f'Accuracy: {accuracy * 100:.2f}%')
                 Output:
                 First 5 rows of the dataset:
                       mean radius  mean texture  mean perimeter  mean area  mean smoothness  \
                 0        17.99         10.38          122.80       1001.0       0.11840
                 1        20.57         17.77          132.90       1326.0       0.08474
                 2        19.69         21.25          130.00       1203.0       0.10960
                 3        11.42         20.38           77.58        386.1       0.14250
                 4        20.29         14.34          135.10       1297.0       0.10030


                       mean compactness  mean concavity  mean concave points  mean symmetry  \
                 0           0.27760          0.3001           0.14710           0.2419
                 1           0.07864          0.0869           0.07017           0.1812
                 2           0.15990          0.1974           0.12790           0.2069
                 3           0.28390          0.2414           0.10520           0.2597
                 4           0.13280          0.1980           0.10430           0.1809


                       mean fractal dimension  ...  worst texture  worst perimeter  worst area  \
                 0             0.07871         ...      17.33           184.60        2019.0
                 1             0.05667         ...      23.41           158.80        1956.0
                 2             0.05999         ...      25.53           152.50        1709.0
                 3             0.09744         ...      26.50            98.87         567.7
                 4             0.05883         ...      16.67           152.20        1575.0


                       worst smoothness  worst compactness  worst concavity  worst concave points  \
                 0            0.1622           0.6656           0.7119            0.2654
                 1            0.1238           0.1866           0.2416            0.1860
                 2            0.1444           0.4245           0.4504            0.2430
                 3            0.2098           0.8663           0.6869            0.2575
                 4            0.1374           0.2050           0.4000            0.1625


                       worst symmetry  worst fractal dimension  target
                 0          0.4601            0.11890             0
                 1          0.2750            0.08902             0
                 2          0.3613            0.08758             0
                 3          0.6638            0.17300             0
                 4          0.2364            0.07678             0


                 [5 rows x 31 columns]
                 Accuracy: 94.15%
                 In the above program, the dataset is split into training and testing sets using train_test_split. A KNN classifier is
                 created with k=3 neighbors. This classifier is trained using the training data. The trained model is then used to predict
                 labels for the test set. Finally, the accuracy of the model is calculated and printed.




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