codinglover-noman / breast-cancer-diagnostic-classification-using-svm

Breast-Cancer-Diagnostic-Classification-using-Supervised Learning

This project focuses on Breast Cancer Diagnostic Classification utilizing Supervised machine learning. The primary objective is to achieve accurate and reliable diagnostic outcomes for breast cancer cases.

Overview

In this project, various classification models have been employed to evaluate their performance:

✅ K-nearest neighbors (K-NN) algorithm. ✅ Support Vector Classifier ✅ Logistic Regression ✅ ExtraTree-decesion ✅ Random Forest ✅ Keras

Prerequisites

Make sure you have the following prerequisites installed:

• Python (>=3.6)
• Pip (Python Package Installer)
• Jupyter Notebook (for exploring the project interactively)

Explore the Project: Open and run the main script or Jupyter notebook to observe the classification results

Models Explored

1. K-nearest neighbors (K-NN) Algorithm Description: K-NN is a versatile classification algorithm that classifies data points based on the majority class of their k-nearest neighbors. Usage: Explored for its potential in breast cancer diagnostic classification.
2. Support Vector Classifier Description: Support Vector Classifier (SVC) is a powerful machine learning algorithm used for classification tasks. It works by finding the hyperplane that best separates different classes. Usage: Applied to breast cancer diagnostic classification for comparison with other models.
3. Logistic Regression Description: Logistic Regression is a popular algorithm for binary classification. It models the probability that a given instance belongs to a particular category. Usage: Investigated for its effectiveness in breast cancer diagnosis.
4. ExtraTree-decision Description: ExtraTree-decision is an ensemble learning method based on constructing multiple decision trees during training and outputting the class that is the mode of the classes. Usage: Explored as part of the ensemble models for diagnostic classification.
5. Random Forest Description: Random Forest is an ensemble learning method that operates by constructing a multitude of decision trees during training and outputs the class that is the mode of the classes. Usage: Utilized to harness the power of ensemble learning in breast cancer diagnosis.
6. Keras Description: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It enables fast experimentation with deep neural networks. Usage: Explored for its capabilities in the context of neural network-based diagnostic classification.

Acknowledgments

Special thanks to Selene Reyes from Kaggle where she teached a very well about this work. I greatly appreciated from her work and got a inspired for work.

For more details on the project, explore the code and documentation. Happy coding!