In the hands-on section, we applied the Support Vector Machine for classification. Following are some ideas to your understanding and develop other models on the same dataset:
You can apply a different classifier and compare the results on the same data:
Hints: DecisionTreeClassifier(criterion ='gini', random_state = 0)
In the Hands-On section, we did not apply cross-validation for checking the consistency of the models' performance. You can apply 5- or 10-fold cross-validation to find the average performance of the model.
We used the standardization technique for converting the data into a similar scale. Instead, you can try another approach- min-max scaling for normalizing the data and checking the performance.
Hints: from sklearn.preprocessing import MinMaxScaler