Answer by using mathlap
Note: To be completed in groups of 3 students each 1.1 Developing the Machine Learning Model (20 points) Note: For each version, select only 5 features out of the available columns. Version 1: Heart Failure Prediction Dataset • Link: https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction • Train-Test Split: 70% – 30% • Output: Heart Disease Column (0 or 1) Version 2: Gender Classification Dataset • Link: https://www.kaggle.com/datasets/elakiricoder/gender-classification-dataset • Train-Test Split: 85% – 15% • Output: Gender (Male or Female) Version 3: Credit Card Fraud Detection Dataset • Link: https://www.kaggle.com/datasets/joebeachcapital/credit-card-fraud • Train-Test Split: 80% – 20% • Output: Output (1 or 0) Version 4: Milk Quality Dataset • Link: https://www.kaggle.com/datasets/cpluzshrijayan/milkquality • Train-Test Split: 85% – 15% • Output: Grade (Low, Medium, High) Version 5: Palmer Penguin Classification Dataset • Link: https://www.kaggle.com/datasets/martaarroyo/palmer-penguins-for-binary-classification • Train-Test Split: 75% – 25% • Output: Species (Adelie or Gentoo) Version 6: Airline Satisfaction Classification Dataset • Link: https://www.kaggle.com/datasets/binaryjoker/airline-passenger-satisfaction • Train-Test Split: 70% – 30% • Output: Satisfaction (Satisfied or Neutral/Dissatisfied) Version 7: Diabetes Prediction Dataset • Link: https://www.kaggle.com/datasets/vikasukani/diabetes-data-set • Train-Test Split: 80% – 20% • Output: Outcome (1 or 0) Version 8: Water Quality Prediction Dataset • Link: https://www.kaggle.com/datasets/adityakadiwal/water-potability • Train-Test Split: 85% – 15% • Output: Potability (1 or 0) Version 9: Smoke Detection Dataset • Link: https://www.kaggle.com/datasets/deepcontractor/smoke-detection-dataset • Train-Test Split: 70% – 30% • Output: Fire Alarm (1 or 0) According to the version assigned to you, train and compare 5 machine learning models for the specific application using the Matlab Classification Learner App or Python. Ensure to export the model with the highest accuracy. The following mark distribution will be followed, hence, make sure to include these in your report and code: 1. Introduction (3 points) 2. Database Loading, Pre-processing, and Train-test split (3 points) 3. Training process Screenshot and Training Results (3 points) 4. Testing, Confusion Matrix, accuracy, and saved model (3 points) 5. Explanations for each step (6 points) 6. Conclusion (2 points) 1.2 Developing the Application or Website (10 points) Using the model that you saved in Task 1, use Matlab App Designer (or any preferred interface software) to create a user interface application that satisfies the following requirements: a) Allows the user to upload test features. (2 points) c) Implements the model saved in Task 1 to determine the class of the features uploaded. (2 points) d) Provides an option to display the confusion matrix in Task 1 within a separate Axes. (2 points) e) Ensure creativity and use of unique widgets (2 points) f) Explanation of the app design and code (2 points) 1.3 Demonstration (10 points – 5 marks) Class demonstration: The team will be given 10 minutes to demonstrate their project to the class. There is no need to prepare PPT slides for this, you only need to show your app and code, and explain how it works. You will be marked as per the table below. Criteria Points Student Mark Answers to questions 5 Demonstration works as required 5 TOTAL 10 Submission Instructions: Submit a report which contains the following (30 points): • Assignment Cover Page – as per this document, include all names and student IDs of group members • Introduction paragraph – introduce what your dataset about, the number of classes, and number of features, and what the features are and why you chose them. (points included in Model development) • Model Development and results – include screenshots of the code in your report to explain them, but ensure to attach the original codes in the submission (20 points – spread between codes and report) o The points to be discussed is detailed in Section 1.1 • Application Design – include screenshots in your report to explain them, but ensure to attach the original codes in the submission (10 points – spread between codes and report) o The points to be discussed is detailed in Section 1.2 • Conclusion – conclude and summarize the assignment (points included in Model development) FAIR CONTRIBUTION MARK SHEET Fill in the contribution mark based on your individual contribution to the assignment. If all members contributed equally, write your contribution grade as 20/20. The total contribution score should equal to: Number of Team Members * 20. Example: Team Member Name Contribution Score A Signature Actual Report Mark Actual Mark Received 21/20 (contributed more 28/30 (21/20)*28 = 29.4/30 than assigned) B C TOTAL 20/20 (contributed as expected) 19/20 (contributed less than assigned) 60/60 28/30 (20/20)*28 = 28/30 28/30 (19/20)*28 = 26.6/30 Note: If all team members contributed equally, you can assign 20/20 contribution score for everyone. Fill in the following table for your Fair Contribution Scores: Team Member Name Contribution Score Signature Actual Report Mark Actual Received TOTAL Project – Prepared by: Dr. Abigail Copiaco Page 2 Mark
Collepals.com Plagiarism Free Papers
Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.
Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS
Why Hire Collepals.com writers to do your paper?
Quality- We are experienced and have access to ample research materials.
We write plagiarism Free Content
Confidential- We never share or sell your personal information to third parties.
Support-Chat with us today! We are always waiting to answer all your questions.
