Objective
In this individual project, you are required to understand and compare several classi cation algorithms that are provided by the Python scikit-learn library (http://scikit-learn.org/stable/).
Requirements
(50 points) Write classi cation code by utilizing several scikit-learn classi ers: (i) perceptron, (ii) support vector machine (linear and non-linear using Radial Basis Function (RBF) kernel), (iii) decision tree, (iv) K-nearest neighbor, and (v) logistic regression.
(15 points) Each classi er needs to be tested using two datasets: (1) the digits dataset o ered by scikit-learn library, and (2) one dataset containing time-series instances. Example of the second dataset can be the REALDISP Activity Recognition Dataset (https://archive.ics.uci.edu/ml/datasets/ REALDISP+Activity+Recognition+Dataset).
(15 points) Properly analyze the classi ers' behavior by applying the knowledge that we discussed in class. Such analysis should include at least accuracy and running time.
(15 points) Understand the source code of DecisionTreeClassifier (You can follow the source link in http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier. html).
{ (5 points) Please denote two strategies that this classi er implements to pre-prune or post-prune the tree.
{ (10 points) For each strategy, please clearly identify the repository le and the lines of code that implement such strategies.
(5 points) Write a readme le readme.txt with the commands to run your code. Your Python code should be written for Python version 3.5.2 or higher.
Please properly organize your Python code (e.g., create proper classes, modules).
Submission instructions
In your github repository, create a project folder proj3.
Put all your les (Python code, readme le, report, etc.) in your project folder. Submit the link to your github repository folder through Canvas.
Grading criteria
The score allocation has already been put beside the questions.
Please make sure that you test your code thoroughly by considering all possible test cases. Your code may be tested using more datasets.
At least 5 points will be deducted if submitted les (including les types, le names, etc.) do not follow the instructions.
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