csci946-a2

Social Network Misinformation Detection

Tasks

Process the data considering different data types and properties.
Apply core models/algorithms
- regression
- association rules
- clustering
- classification
Justify the choice of models/algorithms used.

Report: A comprehensive report summarizing Tasks 1-4. The report should be well-organized, include citations, and be submitted in PDF format.
Code: Python code files that can recreate all results. The code should be clean, annotated, and executable.
Submission Format: A single ZIP file named "A2.zip" containing the report and code files. The ZIP file should not exceed 200 MB.

Group Work: The assignment is to be completed in groups, maintaining the same group members as Assignment 1. Each member must contribute, and contributions should be documented.
Penalties: Zero marks for plagiarism or failure to contribute. Ensure all work is original and properly documented.
Submission Details: Submit via the Moodle site, ensuring the code runs successfully in the lab environment. Marks will be deducted for untidy or non-functional code.

Name		Name	Last commit message	Last commit date
Latest commit History 78 Commits
.gitignore		.gitignore
.nojekyll		.nojekyll
A2_Code.html		A2_Code.html
A2_Code.ipynb		A2_Code.ipynb
A2_Code.py		A2_Code.py
README.md		README.md
a2.html		a2.html
a2.ipynb		a2.ipynb
a2.py		a2.py
amend.py		amend.py
association_rules.py		association_rules.py
classification.html		classification.html
classification.ipynb		classification.ipynb
classification.py		classification.py
clustering.py		clustering.py
clustering_A2 -final.py		clustering_A2 -final.py
eda_pp.py		eda_pp.py
eda_pp_dream.html		eda_pp_dream.html
eda_pp_dream.ipynb		eda_pp_dream.ipynb
eda_pp_dream.py		eda_pp_dream.py
eda_pp_kye.html		eda_pp_kye.html
eda_pp_kye.ipynb		eda_pp_kye.ipynb
eda_pp_kye.py		eda_pp_kye.py
index.html		index.html
regression.html		regression.html
regression.ipynb		regression.ipynb
regression.py		regression.py
twitter_user_data.csv		twitter_user_data.csv