🩺 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:

   git clone https://github.com/EchoSingh/Cancer_Prediction.git

2. Navigate to the project directory:

   cd Cancer_Prediction

3. Open Jupyter Notebook:

   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.