Python Assignment

Please number the question on the python notebook.

[Dataset: magic04.csv]

For this problem you will experiment with various classifiers provided as part of the scikit-learn (sklearn) machine learning module, as well as with some of its preprocessing and model evaluation capabilities.  The data is provided in a CSV formatted file with the first row containing the attribute names. Click “Data Folder”, and you can download the dataset to your PC by right-clicking and then selecting “save link as” the link. The description of the different fields in the data is provided at . Please try to read the document and understand the case and the dataset.

In this assignment, you need to use the scikit-learn package, the main machine learning package in python to develop an ipython notebook. Please take a look at the scikit-learn home page ( to get an overview of the package.

You want to make sure the scikit-learn package you are using is v20 or later versions. If you installed anaconda recently, you should have the version v23.2, which is fine though the latest version of sklearn is v24.1.

Please develop an ipython notebook titled 770_21_a1_yourlastname to finish the following tasks. You probably want to finish the tasks by modifying the German credit notebook I used in week 3 lecture

You are required to create an ipython notebook cell for each of the following tasks, where (C) indicates that you need to write code for the task, (O) indicates that you need to show output, and (A) that you need to type your answers using Markdown text.

At the beginning of each cell, you need to indicate which task the cell is about. For example, in the cell related to task 1, you should first type “# Task 1: Import data”. If you do not clearly label the cells, you will lose 1-2 points (out of 18 points).

1. You need to import data. (C) – completed

2. In this dataset, the dependent variable is class. It includes two categories: g and h. g represents gamma (signal), and h hadron (background). Please insert a cell and print the value count of each category. (C)(O) – completed

3. All the other variables are independent variables. Please insert a cell and print the histograms of the independent variables (C)(O). – completed

4. Insert a cell and print the basic stats of each independent variable using the describe() method (C)(O).   – completed.

5. Insert a cell and write code to split the dataset into training and validation sets (Please use 60%-40% split) (C).

6. Insert a cell and describe the uses of validation (at least 3 uses). (A). I will complete this portion.

7. Insert a cell. In this cell, you need to use scikit-learn’s logistic regression classifier ( and fit a model using the training dataset (C). Then you run the classifier on the validation set (C). Print the validation dataset classification report and Area Under the Receiver Operating Characteristic Curve (ROC AUC) for the validation set. (please google to find out how to get AUC using scikit-learn) (C)(O).

8. Insert a cell and use your own language to describe the SVM algorithm (with at most 8 sentences) (A). I will complete this portion.

9. Insert a new cell. In this cell, you use the same training and validation dataset you obtained in task 5 to fit SVM classifiers (Please use the SVC function in scikit-learn. You need to tune the SVM hyperparameter, C (default = 1.0), the Regularization parameter. You need to try each C in the list [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] – you must use a FOR loop. In each iteration, please print the validation set classification report and AUC. (C)(O).

10. Insert a new cell. In this cell, please first tell me which C in the list [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] gives you the optimal SVM classifier with respect to AUC (A). Then, please use your own language (with at most 4 sentences) to discuss what this hyperparameter C means (A).

11. Insert a cell and write code to fit a random forest classifier ( using the same training and validation dataset obtained in task 5 and print classification report and AUC. When you fit the random forest model, you can just use the default hyperparameters (C)(O).

Insert a new cell and use your own language (with at most 8 sentences) to describe the random forest algorithm (A).

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