Machine Learning project for Breast Cancer Classification using Machine Learning (Logistic Regression, SVM, Random Forest) with model evaluation and performance analysis.
Dataset
Breast Cancer Wisconsin Dataset Features: Cell nucleus measurements Target: 0 → Malignant 1 → Benign
Algorithms Used Logistic Regression Support Vector Machine (SVM) Random Forest K-Nearest Neighbors (KNN)
Evaluation Metrics Accuracy Precision Recall F1 Score Confusion Matrix
Technologies Python, Pandas, NumPy, Scikit-learn, Matplotlib
Outcome The model successfully classifies tumors with high accuracy and demonstrates the use of ML in medical diagnosis.
Project Structure Cancer-Cell-Classification/ │ ├── notebook.ipynb └── README.md
How to Run the Project Clone the repository
Install required libraries: pip install -r requirements.txt
Run the notebook or Python file
Future Improvements Hyperparameter tuning Deep Learning model implementation Deployment using Flask or Streamlit