Solve using Python

In this problem you are required to apply various classification techniques on a benchmark dataset, spambase.data, from the UCI repository. This dataset contains 57 attributes, where the last one is the class: spam (1) or non-spam (0). For further details you may visit: https://archive.ics.uci.edu/ml/datasets/spambase Obtain 500 random splits of the dataset into training (80%) and test (20%) and for each split apply all these classification techniques: i. Decision trees ii. KNN iii. Support Vector Machines iv. Logistic Regression v. Nave Bayes Print a summarization table showing the average values of precision, recall, f1 score and accuracy, which are obtained from the 500 tests.