https://github.com/sunami09/cartresearch

https://github.com/sunami09/cartresearch

Last synced: 4 days ago
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• Host: GitHub
• URL: https://github.com/sunami09/cartresearch
• Owner: sunami09
• Created: 2024-04-07T10:12:36.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2024-04-07T21:35:21.000Z (7 months ago)
• Last Synced: 2024-04-07T22:29:35.177Z (7 months ago)
• Language: Jupyter Notebook
• Size: 5.18 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### README for GitHub Project: Breast Cancer Detection with CART

#### Overview

This repository hosts a research project focused on the detection of breast cancer utilizing the Classification and Regression Trees (CART) algorithm. The project aims to shed light on the capabilities of decision trees in the realm of medical diagnostics, specifically emphasizing aspects such as feature selection, the impact of class imbalance, and an evaluation of model performance. Included within this repository are a Jupyter Notebook (`Main.ipynb`), which details the project's implementation, a PDF document of the research paper (`Sunami Dasgupta Research Paper.pdf`) outlining the study's findings, and the dataset used for analysis (`data.csv`).

#### Getting Started

##### Prerequisites

- [Jupyter Notebook](https://jupyter.org/install) or [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html) installation.

- An active [Kaggle](https://www.kaggle.com/) account for dataset access and notebook execution, if you choose to run the notebook on Kaggle.

##### Running the Notebook Locally

1. **Clone the Repository**:

   - Clone this repository to your local machine or download it as a ZIP file and extract it.

2. **Open the Notebook**:

   - Launch Jupyter Notebook or JupyterLab and open `Main.ipynb` from the repository directory.

3. **Run the Notebook**:

   - Execute each cell in the notebook sequentially to explore the CART algorithm's application in breast cancer detection. The dataset `data.csv` will be automatically loaded as part of the notebook execution process.

##### Running the Notebook on Kaggle

1. **Upload the Notebook and Dataset to Kaggle**:

   - Log in to your Kaggle account.

   - Upload `Main.ipynb` to a new Kaggle notebook and `data.csv` to Kaggle Datasets.

2. **Run the Notebook**:

   - Execute the notebook cells in sequence on Kaggle to perform the analysis.

#### Reading the Research Paper

To delve into the research findings, download and read the `Sunami Dasgupta Research Paper.pdf` available in this repository. The paper provides a detailed exploration of the methodology, results, and conclusions drawn from the study.