This project leverages machine learning algorithms to classify breast cancer as benign or malignant using a dataset with 569 instances and 30 features. We aim to enhance early and accurate diagnosis for effective treatment. All of the machine-learning algorithms in this project are written scratch and no ML libraries are used.
- Data visualization and preprocessing included removing non-informative features and normalizing the dataset to ensure uniformity in feature scales.
- Different plots and graphs are used to give information about the dataset.
- Logistic Regression
- Multi-Layer Perceptron
- Decision Tree
- Random Forest
- Our results highlighted the effectiveness of Multi-Layer Perceptrons, showcasing high accuracy and precision in classification, indicating its potential as a reliable diagnostic tool.
- Plans for future work include exploring more complex models and features to improve the classification accuracy further and possibly deploying the model for real-world testing.
- Ömer Tuğrul
- Selin Ataş