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Cardiff School of Computer Science and Informatics Coursework Assessment Pro-forma Module Code: CMT307 Module Title: Applied Machine Learning Lecturer: Yuhua Li Assessment Title: Coursework 1 Assessment Number: 1 Date Set: 18 December 2020 Submission Date and Time: 5 February 2021 at 9:30am Return Date: 5 March 2021 This assignment is worth 50% of the total marks available for this module. If coursework is submitted late (and where there are no extenuating circumstances): 1 If the assessment is submitted no later than 24 hours after the deadline, the mark for the assessment will be capped at the minimum pass mark; 2 If the assessment is submitted more than 24 hours after the deadline, a mark of 0 will be given for the assessment. Your submission must include the official Coursework Submission Cover sheet, which can be found here: Submission Instructions Your submission must include: • A filled-in copy of the official Coursework Submission Cover sheet. • All Python source files submitted as a single zip file collection. • A typeset PDF report (see next section for details). Ensure that your student number is included on the report and as a comment at the top of each Python file that makes up your submission. You must submit to Learning Central three files (each named using your student number in the format of [student number]_CW1.pdf, e.g., C1234567_CW1.pdf) which contains the following files: Description Type Name Cover sheet Compulsory One PDF (.pdf) file [student number]_CW1.pdf Report Compulsory One PDF (.pdf) file containing your calculation for question 1 and your report for question 2. [student number]_CW1_report.pdf Assessment 2 Compulsory One ZIP (.zip) file containing the Python code [student number] Any code submitted will be run in Python 3 and must be submitted as stipulated in the instructions above. Any deviation from the submission instructions above (including the number and types of files submitted) will result in a mark of zero for the assessment or question part. You can submit multiple times on Learning Central. ONLY files contained in the last attempt will be marked, so make sure that you upload all files in the last attempt. Staff reserve the right to invite students to a meeting to discuss coursework submissions Assignment There are two questions in this coursework, marks for each part are in brackets. Question 1 Your algorithm gets the following results in a classification experiment, where in the table, ‘Id’ is the index number, ‘Prediction’ is the predicted results, ‘Target’ is the ground truth that the classifier aims to achieve. Please compute the precision, recall, f-measure and accuracy manually (without the help of your computer/Python, please provide all steps and formulas). Include the process to get to the final result. [10%] Id Prediction Target 1 True True 2 True True 3 False True 4 True True 5 True True 6 False True 7 True True 8 True True 9 True True 10 False False 11 False False 12 False False 13 True False 14 False False 15 True False 16 False False 17 False False 18 True False 19 True False 20 False False Question 2 In this question, you will develop machine learning models to predict e-commerce visitors’ purchasing intention. The given dataset Coursework_1_data.csv, which can be downloaded from Learning Central, contains shoppers’ online activity information including clickstream and session information data, where the last column Revenue represents visitors’ purchasing intention. Your tasks will include data exploration, data pre-processing, machine learning method selection and implementation and model performance evaluation. In addition to aforementioned tasks, you will write a concise report (around 1000 words, excluding tables and figures) to summarise your work and provide an analysis and discussion of the results. i) Data exploration [10%]: Conduct exploratory inspection of the dataset to provide a good understanding of data characteristics. ii) Data pre-processing [30%]: Carry out well thought pre-processing procedures to prepare the data into a form that is likely to lead to better performance. iii) Model implementation [30%]: Select three representative classification methods with a clear justification of your choice. Implement and optimise the classifiers for your chosen classification methods. iv) Performance evaluation [10%]: Organise the data in a suitable form to ensure the trained classifiers to provide reliable results. Evaluate models using suitable performance metrics. v) Result analysis and discussion [10%]: Provide an insightful analysis and comparison on results that you obtained from above steps, draw conclusions based on the results and analysis. Learning Outcomes Assessed Completion of this coursework allows students to demonstrate that they can: 1. Implement and evaluate machine learning methods to solve a given task 2. Explain the basic principles underlying common machine learning methods 3. Choose an appropriate machine learning method and data pre-processing strategy to address the needs of a given application setting 4. Reflect on the importance of data representation for the success of machine learning methods Criteria for assessment Credit will be awarded against the following criteria. Criteria Fail (0-49%) Pass (50-59%) Merit (60-69%) Distinction(>=70%) Data exploration [10%] No or arbitrary data exploration. Suitable but limited data exploration. Good data exploration but miss some insightful analysis. Thorough and insightful data exploration. Data pre- processing [30%] No or very little data pre- processing. Some necessary pre- processing is conducted. Adequate pre- processing to prepare the data for model development. Extensive pre- processing to deal with all aspects of non-ideal characteristics of the data with an aim to achieve a best classification performance. Model implementation [30%] Less than 3 classification methods are implemented. Classifiers are not correctly implemented and optimised. Three different classifiers are implemented but not properly/sufficiently trained. Three classification methods are selected to give a good representation of various classification techniques. All implemented classifiers are properly trained and optimised. Three classification methods are carefully selected with a good justification of your selection, which give a good representation of various classification techniques. All classifiers are excellently implemented, properly trained and systematically optimised. Performance evaluation [10%] Little performance evaluation. Performance evaluation using arbitrary metrics. Performance evaluation using metrics without considering data characteristics. Good performance evaluation with suitable metrics. Use most suitable performance metrics to assist the selection of the best model on characteristics of this dataset. Result analysis and discussion [10%] No or little meaningful result analysis and discussion. General result analysis and discussion. Vague conclusion. Good result analysis and discussion but lack of depth. Insightful result analysis and discussion, conclusion clearly drawn. Feedback and suggestion for future learning Feedback on your coursework will address the above criteria. Feedback and marks will be returned via Learning Central. There will be opportunity for individual feedback during an agreed time. Feedback for this assignment will be useful for subsequent skills development, such as data science, natural language processing and deep learning (which will be studied during the second semester). 欢迎咨询51作业君


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