https://github.com/echosingh/cancer_prediction
An ML-based project for predicting cancer using Logistic Regression and visualizing performance metrics.
https://github.com/echosingh/cancer_prediction
cancer-data cancer-prediction jupyter-notebook ml visualization
Last synced: 3 months ago
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An ML-based project for predicting cancer using Logistic Regression and visualizing performance metrics.
- Host: GitHub
- URL: https://github.com/echosingh/cancer_prediction
- Owner: EchoSingh
- License: mit
- Created: 2024-12-28T11:44:17.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-12-28T12:11:34.000Z (9 months ago)
- Last Synced: 2025-02-24T23:24:42.100Z (7 months ago)
- Topics: cancer-data, cancer-prediction, jupyter-notebook, ml, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 2.08 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🩺 Cancer Prediction
🚀 **An ML-based project for predicting cancer using Logistic Regression and visualizing performance metrics.**
---
## Project Structure
The repository includes the following files:
### Data
- **Cancer.csv**: The dataset used for cancer prediction.
### Reports and Documentation
- **G17_Harnessing AI for Breakthroughs in Computational Biology.pdf**: A detailed report discussing the significance of AI in computational biology and this project's contribution.
### Code and Notebooks
- **Cancer_Prediction.ipynb**: A Jupyter Notebook containing the code for data preprocessing, training, and evaluation of the cancer prediction model.
- **Graphs_Plot.ipynb**: A Jupyter Notebook dedicated to visualizing data and results using graphs.
### Architecture and Visuals
- **Cancer_Prediction_Architecture.png**: A graphical representation of the architecture of the cancer prediction model.
### Configurations
- **.gitignore**: Specifies files and directories to be ignored by Git.
### Licensing
- **LICENSE**: Contains the license for the project.
### README
- **README.md**: Documentation about the project.
## Requirements
To run this project, ensure you have the following installed:
- Python 3.7+
- Jupyter Notebook
- Required libraries (specified in the notebooks or requirements file):
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
## Usage
1. Clone this repository:
```bash
git clone https://github.com/EchoSingh/Cancer_Prediction.git
```
2. Navigate to the project directory:
```bash
cd Cancer_Prediction
```
3. Open Jupyter Notebook:
```bash
jupyter notebook
```
4. Run the notebooks:
- Open `Cancer_Prediction.ipynb` to train and evaluate the model.
- Open `Graphs_Plot.ipynb` to visualize the data and results.
## Results
- Graphs and architecture visualizations provide insights into the data and model workings.
## License
This project is licensed under the terms specified in the `LICENSE` file.