This project is part of INFO 526 (Fall 2024) and is a team effort by the Visual Voyagers. Our goal is to study and visualise data about cancer to find patterns, trends, and factors that could improve understanding of this disease. We will use machine learning methods to uncover useful insights.
The basic outline of this project is to use the dataset 'Breast Cancer Diagnostic Data' to train various classification models and then compare their performance.
1) Exploratory Data Analysis (EDA): Looking for fundamental discrepancy in the dataset.
2) Model Analysis: After the EDA, we have decided to use Logistic Regression, Support Vector Machine and Naive Bayes classifier
3) Visualization: We will be working on visualising the models performance on various criteria and use the found insights to represent them visually using scatter plot, bar chart plot etc.
project-2-visual-voyagers/
├── data/
│ ├── cancer_dataset.csv
| └── Readme.md
├── plots/
│ └── Readme.md
├── docs/
│ └── Readme.md
├── about.qmd
├── proposal.html
├── proposal.qmd
└── README.md