This project uses machine learning to predict breast cancer based on features of cell nuclei present in the breast cancer dataset. It is based on a guided project from Coursera.
The goal of this project is to build a logistic regression classifier that can accurately predict whether a tumor is benign or malignant based on certain features. The dataset used is the Breast Cancer Wisconsin (Diagnostic) Dataset.
The dataset contains 569 instances of tumors, with 30 features each. The features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.
breast_cancer_prediction.ipynb: Jupyter notebook containing the code for the project.
The project requires the following Python packages:
- pandas
- numpy
- scikit-learn
- matplotlib (optional, for visualizations)
You can install these using pip:
pip install pandas numpy scikit-learn matplotlib- Clone the repository:
git clone https://github.com/harshita2234/Breast-Cancer-Prediction.git- Navigate to the project directory:
cd Breast-Cancer-Prediction- Open the Jupyter notebook:
jupyter notebook breast_cancer_prediction.ipynb- Run the cells in the notebook to see the analysis and results.
This project is licensed under the MIT License - see the LICENSE file for details.
- Coursera for the guided project
- Wisconsin Diagnostic Breast Cancer (WDBC) dataset