🧠 Breast Cancer Detection using Machine Learning This project aims to predict whether a breast tumor is benign or malignant using various machine learning algorithms. The implementation is done in Python using Jupyter Notebook, with powerful libraries like scikit-learn, pandas, and matplotlib.
📌 Table of Contents Overview
Dataset
Technologies Used
Model Evaluation
Results
How to Run
Future Improvements
License
✅ Overview Breast cancer is one of the most common types of cancer among women worldwide. Early diagnosis can significantly improve the chances of recovery. This project leverages machine learning to assist in the classification of tumors into benign or malignant categories.
📂 Dataset The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, which contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.
🔗 UCI ML Repository – Breast Cancer Wisconsin Dataset
🛠 Technologies Used Python 3.x
Jupyter Notebook
Pandas
NumPy
Matplotlib & Seaborn
Scikit-learn (Logistic Regression, SVM, KNN, Random Forest, etc.)
📈 Model Evaluation The following metrics were used to evaluate model performance:
Accuracy
Confusion Matrix
Precision, Recall, F1 Score
ROC-AUC Curve
🎯 Results The model achieved high accuracy (e.g., 97%+ depending on the algorithm). Random Forest and SVM performed especially well, making this a strong candidate for assisting medical diagnostics.
bash Copy Edit git clone https://github.com/yourusername/breast-cancer-detection.git cd breast-cancer-detection Install dependencies
bash Copy Edit pip install -r requirements.txt Run the notebook Open breast_cancer_detection.ipynb in Jupyter and run all cells.
🚀 Future Improvements Add deep learning models (e.g., using TensorFlow/Keras)
Deploy as a web app with Streamlit or Flask
Integrate cross-validation and hyperparameter tuning
📜 License This project is open source and available under the MIT License.