The goal of this task is to apply different classification approaches to a challenging dataset to compare the results, to enhance the accuracy of better parameters/preprocessing/using kernels/incorporating background knowledge and to summarize your findings in a report.

As far as classification algorithms are concerned we will use:
1. Neural Networks
2. Support Vector Machines

You will use 2 "variations" of each approach:
- As far SVMs are concerned, you should use 2 different kernels (any kernel is fine, you can use the linear kernel as one kenel)
- As far as NNs are concerned, you should use two of the following activation functions: 1. Logistic/sigmoid 2. Tanh or hyperbolic tangent activation function 3. Relu 4. Softplus

Accuracy of the four classification algorithms, you compare, should be measured using 10-fold cross validation. In your report after comparing the experimental results, write a paragraph or two tryıng to explain/speculate why, in your opınion one classıfication algorithm outperformed the other. Finally, at the end of your report provide a 1-2 paragraphs summary that summarizes the most important findıngs of this task.

Deliverables: Please submit both the report and the source code file

Suggestions You can use built-in functions in python and R. For python, it's preferable to use Scikit-Learn for both SVM and neural network. For R, we suggest the 'Neuralnet' and 'svm' functions