Breast Cancer Classification with Python using Logistic Regression

🔖 This project uses logistic regression to classify breast cancer tumors as benign or malignant. The data was collected from the Breast Cancer Wisconsin (Diagnostic) dataset. The data was preprocessed using data cleaning, feature selection, and standardization. A logistic regression model was then trained on the preprocessed data. The model achieved an accuracy of 98.25% on the test data.

🔖 The project was implemented in Python using JupyterLab. The code is well-documented and easy to follow.

🔖 Highlights:

• Uses logistic regression to classify breast cancer tumors
• Achieves an accuracy of 98.25% on the test data
• Implements data cleaning, feature selection, and standardization
• Uses JupyterLab for code execution and visualization

To use:

1. Clone the repository to your local machine.
2. Open the breast-cancer-classification.ipynb file in JupyterLab.
3. Run the cells in the notebook to train and evaluate the model.

Contributions are welcome!

If you find this project useful, please consider contributing to it. You can do this by submitting bug reports, feature requests, or pull requests.

Thank you for your interest in this project! Author Nilesh Kumar Yadav