Breast Cancer Classification

This project aims to classify breast cancer cases as benign or malignant using machine learning models. It includes data preprocessing, model training, and evaluation.

introduction

Breast cancer is a common form of cancer, and early diagnosis is crucial for effective treatment. This project focuses on developing machine learning models to classify breast cancer cases based on clinical features as either benign or malignant. The dataset used for this project is sourced from [Kaggle Datasets] and contains various attributes related to breast cancer diagnosis.

Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

Python (>=3.6)

Git (optional)

Usage

Run the breast_cancer_classification.py script to perform the following tasks:

Load and preprocess the breast cancer dataset.

Train machine learning models (Logistic Regression, SVM, Decision Tree, and Random Forest).

Evaluate the models using cross-validation.

Save the best-performing model (SVM) as svm_model.pkl.

Use the saved SVM model to make predictions on new breast cancer cases.

Data

The dataset used in this project contains information related to breast cancer diagnosis. It includes features like radius, texture, area, and more. The target variable is binary, with "0" representing benign cases and "1" representing malignant cases.

Models

This project uses the following machine learning models for classification:

Logistic Regression

Support Vector Machine (SVM)

Decision Tree

Random Forest Classifier

Each model is fine-tuned using grid search to find the best hyperparameters.

Results

The results of the model evaluation indicate that the SVM model outperforms the other models, achieving a testing accuracy of approximately 95%. The confusion matrix and classification report for each model can be found in the code and documentation.

Conclusion

This project demonstrates the effectiveness of the SVM model for breast cancer classification based on clinical features. The saved SVM model (svm_model.pkl) can be used to make predictions on new breast cancer cases, potentially aiding in early diagnosis and treatment.

Bonus!!

this project also has done by trying different types of dimension reduction ways, PCA, Kruskal Wallis test and Chi-Square test