You are asked to apply different classification techniques to a given Spambase dataset, to potentially enhance the accuracy of the learnt models via selecting better parameters, and/or preprocessing etc., to compare the results, and to summarize your findings in a report. The Spambase Data Set can be retrieved from https://archive.ics.uci.edu/ml/datasets/Spambase or be directly downloaded from task resources. 

The classification algorithms to apply are: 

1. Logistic Regression 

2. Random Forest 

3. Support Vector Machines 

You must compare and interpret the results of using different approaches for the dataset. 

Other requirements are: 

• Classification models that achieve higher accuracies will get more points. 

• In your report after comparing the experimental results, write a paragraph or two trying to explain/speculate why, in your opinion one classification algorithm outperformed the others. 

• Include a brief discussion in your report, how you have selected the parameters of particular data mining algorithms. 

• Finally, at the end of your report provide a 1-2 paragraphs summary.