Explore the dataset to identify the features and the class attribute. Consider if there are any missing values and outliers. You also need to convert the Pandas DataFrames into numpy arrays that can be used by scikit-learn. Show your data after being preprocessed. If any technique described below is not able to achieve close to or above 90% accuracy, exam your data again to see if you can preprocess the data in a different way for that technique.
Step 2: Applying techniques
Apply the following techniques to your preprocessed dataset and see which one yields the highest accuracy as measured with 10-fold cross validation.
Decision tree
-Create a single train/test split of your data. Set aside 75% for training, and 25% for
testing. Use tree.DecisionTreeClassifier to create a model and fit it to your training data. Measure the accuracy of the resulting decision tree model using your test data. (Hint: you don’t have to visualize the tree and use sco
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