Write a research paper about what UCI stands for in machine learning.Project 1: Implementation of Classification
Instructions:
In the project, you need to prepare an idea and a data set from real world, apply classification algorithms in supervised learning to analyze the data set and furthermore get some observations or conclusions.
Group work:
Both individual and group work are allowed in this project. Each group can include at most 2 students. All the names of group members should be indicated in the presentation slides.
About the data set:
You could find the data by your self or select from the following resources:
Kaggle
https://www.kaggle.com/Links to an external site.
UCI Machine Learning Repository
http://archive.ics.uci.edu/ml/index.phpLinks to an external site.
Stanford Large Network Dataset Collection
https://snap.stanford.edu/data/Links to an external site.
Dataverse Network
https://dataverse.org/Links to an external site.
Reddit Open Data
https://www.reddit.com/r/opendata/Links to an external site.
CDC Data
https://www.cdc.gov/nchs/tools/index.htm?
CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnchs%2Fdata_access%2Fdata_tools.htmLinks to an external site.
World Bank Catalog
https://datacatalog.worldbank.org/Links to an external site.
Metor Boston Data Common
https://datacommon.mapc.org/Links to an external site.
COVID-19 Data Repository by Johns Hopkins University
https://github.com/CSSEGISandData/COVID-19Links to an external site.
Project Requirements
In this project, you will conduct classification models on data sets using the concepts studied in class. At least TWO models are required to analyze the data with tuning hyperparameters. You also need to evaluate and compare the performance of different models and visualize your results.
Data cleaning is optional. If you select standard machine learning dataset from Kaggle or UCI ML Repository, data cleaning is not necessary.
Accordingly, please prepare a presentation for each team that includes the following. Please upload these presentations individually though you are working in a team. Also, please present these in class. Each team will get approx. 10 min for presentation, so please plan your talk accordingly.
Introduction of your project with goals – should be with reference to the data in this assignment
Description of your data set along with the classification target
Live demo / demo snapshots of execution of the classifiers with tuning hyperparameters, and performance evaluation of all the classifiers
Visualization to the results of the performance evaluation
Conclusions from all the above analysis
Submission:
A final submission should include all the source code, data set and slides for the presentation.
Rubric
CSIT456_557_final_project
CSIT456_557_final_project
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeIntroduction of your project with goals
3 pts
Full Marks
The goals of the project are described clearly with reference to the data in this assignment
2 pts
No reference
The goals of the project are described clearly but the reference to the data in this assignment is missing
1 pts
No description of the goals
The goals of the project are not described, but there is reference to the data in this assignment
0 pts
No Marks
The goals of the project and the reference to the data in this assignment are all missing
3 pts
This criterion is linked to a Learning Outcomedescription of the data set
The dataset with a description of the relevant data and the classification target.
2 pts
Full Marks
Description of your data set along with the classification target
1 pts
Half Marks
Data set along with the classification target are not described clearly
0 pts
No Marks
No description of your data set along with the classification target
2 pts
This criterion is linked to a Learning OutcomeExecution of the classifiers
15 pts
Full Marks
1. Data are well prepared with appropriate features/target, training/test sets 2. Correct ML models are selected and implemented 3. Techniques of tuning hyperparameters are correctly applied 4. Evaluation metrics are applied to compare different models. 5. Evaluation results are visualized.
12 pts
Partial Marks
4 out of 5 required items are satisfied.
9 pts
Partial Marks
3 out of 5 required items are satisfied.
6 pts
Partial Marks
2 out of 5 required items are satisfied.
3 pts
Partial Marks
1 out of 5 required items are satisfied.
0 pts
No Marks
No execution of the classifiers.
15 pts
This criterion is linked to a Learning OutcomeLive Demo / demo snapshots of the implementation
18 pts
Full Marks
1. The execution of the classifiers and evaluation are executed successfully. 2. Students can answer the questions about the source code.
14 pts
Partial marks
The execution of the classifiers and evaluation are executed successfully. But students cannot answer some questions about the source code and the implementation.
10 pts
Partial marks
The implementation is not finished. For example, performance evaluation or tuning hyperparameters are missing.
5 pts
Partial Marks
The implementation is not finished. Both performance evaluation and tuning hyperparameters are missing.
0 pts
No Marks
Students will get 0 points if: 1. No live demo /demo snapshots of the implementation. Or 2. Students cannot answer questions to most of the code.
18 pts
This criterion is linked to a Learning OutcomeConclusions
Conclusions from the above analysis
2 pts
Full Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomePresentation
Presenters are well-prepared and logically organized
8 pts
Full Marks
Presenters are well-prepared. Slides should present materials in an informative manner. The presentation is logically organized and presenters appear to be fluid.
6 pts
Partial Marks
The slides present materials in an informative manner. The presentation is logically organized. However, 1. the presentation is not fluid, or 2. there is no good balance between high-level motivational material and technical details, or 3. Presenters cannot respond well to questions from audiences.
4 pts
Partial Marks
The slides present materials in an informative manner. The presentation is logically organized. However, 2 out of the 3 following items are meet: 1. the presentation is not fluid, or 2. there is no good balance between high-level motivational material and technical details, or 3. Presenters cannot respond well to questions from audiences.
2 pts
Partial Marks
Presentation is not logically organized. There is no balance between high-level motivational material and technical detail.
0 pts
No Marks
No presentation
8 pts
This criterion is linked to a Learning OutcomeData Preprocessing
Data preprocessing such as normalization or dealing with categorical features is applied in the project.
2 pts
Full Marks
0 pts
No Marks
2 pts
Total Points: 50
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BELOW I ATTACHED A SCREEN SHOT OF ^^^^ THE TEXT INSTRUCTIONS ABOVE BUT VISUALLY ALSO ATTACHED CLASS NOTES THAT MAY HELP MAYBE WITH THIS PROJECT BUT PLEASE FOLLOW ALL INSTRUCTIONS PLEASE MAKE IT EASY TO EXPLAIN NOT COMPLICATED EVERYTHING LABLED AND DETAIL
ALSO FOR MYSELF CAN YOU MAKE A SEPERATE WORD DOC EXPLAINING IN A SHORT SUMMARY OF WHAT TO SAY BECAUSE I DONT KNOW ANYTHING DONT WANT TO JUST READ OFF THE SLIDE WHILE PRESENTING IT
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