One task is included in this assignment. The specification of the task starts in a separate page.
You must implement and run all your Python code in Jupyter Notebook. The deliverables include one Jupyter Notebook source file (with .ipybn extension) and one PDF document for each task.
Note: To generate a PDF file for a notebook source file, you can either (i) use the Web browser’s PDF printing function, or (ii) click “File” on top of the notebook, choose “Download as” and then “PDF via LaTex”.
All results of your implementation must be reproducible from your submitted Jupyter notebook source files. In addition, the submission must include all execution outputs as well as clear explanation of your implementation algorithms (e.g., in the Markdown format or as comments in your Python codes).
Submission must be done online by using the submission link associated with assignment 1 for this subject on MOODLE. The size limit for all submitted materials is 20MB. DO NOT submit a zip file.
Submissions made after the due time will be assessed as late submissions. Late submissions are counted in full day increments (i.e. 1 minute late counts as a 1 day late submission). There is a 25% penalty for each day after the due date including weekends. The submission site closes four days after the due date. No submission will be accepted after the submission site has closed.
This is a group assignment. Only one submission per group. State the names and student numbers of group members at the beginning of each submitted file.
Marking guidelines
Code: Your Python code will be assessed. The computers in the lab define the standard environment for code development and code execution. Note that the correctness, completeness, efficiency, and results of your executed code will be assessed. Thus, code that produces no useful outputs will receive zero marks. This also means that code that does not run on a computer in the lab would be awarded zero marks or code where none of the core functions produce correct results would be awarded zero marks.
Presentation and explanation: The correctness, completeness and clearness of your answers will be assessed.
CSCI316 (SIM) 2021 Session 3 – Group Assignment 1
The Task
(10 marks)
Dataset: The Covertype Data Set
(Source: https://archive.ics.uci.edu/ml/datasets/covertype )
This dataset contains a variety of cartographic variables (features) and the forest cover type (the class). The features are a mixture of categorical and numerical values. More information about this dataset is in the source webpage.
Objective
The objective of this task is to develop an end-to-end data mining project by using the machine learning library Scikit-Learn in Python. The output of the project is a classification model to predict the cover type.
Requirements
- Main steps of the project are (a) “discover and visualise the data”, (b) “prepare the data for machine learning algorithms”, (c) “select and train models”, (d) “fine-tune the models” and (e) “evaluate the outcomes”. You can structure the project in your own way. Some steps may be performed more than once.
- Clearly explain your findings at each step.
- In the steps (c) and (d), select and train at least 3 classifiers (from 3 different algorithms).
- Use 2/3 data for training and 1/3 for testing the models. Stratified sampling must be used.
- Define at least one new feature by using the User-Defined Transform functionality, which implements a parameter to use this new feature or not in the model fine-tuning step (i.e., step (d)).
Deliverables
- A Jupiter Notebook source file named <group_no>.ipybn which contains your implementation source code in Python
- A PDF document named <group_no>.pdf which is generated from your Jupiter Notebook source file, and presents clear and accurate explanation of your implementation and results. A poor presentation of results gains less marks in this task.
