This project demonstrates a portfolio-ready implementation of breast cancer diagnosis prediction using Logistic Regression and Decision Tree classifiers in Python.
The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, containing features extracted from breast cancer biopsy images.
- Explore and visualize the dataset.
- Implement single-feature and multi-feature Logistic Regression classifiers.
- Implement a Decision Tree classifier.
- Compare classifier performance using accuracy, precision, recall, and confusion matrices.
- radius_mean
- area_mean
- concavity_mean
- symmetry_mean
- Python 3.10+
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
- Clone this repository.
- Ensure the dataset
cancer.csvis in the same directory. - Install the required packages listed in
requirements.txt. - Run the notebook
Logistic_Regression_Bc.ipynb.
- Feature vs Diagnosis stripplots.
- Confusion matrix heatmap.
- Classifier comparison bar chart.
MIT License