This repository contains code and resources for building and evaluating various machine learning models for breast cancer classification. Breast cancer is one of the most common cancers among women worldwide, making early detection crucial for effective treatment. Machine learning algorithms can aid in the classification of breast cancer based on various features extracted from medical imaging data.
The dataset used in this project is the Breast Cancer Wisconsin (Origina) dataset, which is publicly available on the UCI Machine Learning Repository. This dataset consists of features computed from digitized images of fine needle aspirate (FNA) of breast masses and aims to predict whether a breast mass is malignant or benign.
The following classification models have been implemented and evaluated in this project:
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive bayes
- Kernel SVM
##Data Import!!!
- Download the csv file and upload in runtime OR
- Mount google Drive( from google.colab import drive
drive.mount('/content/drive')
The performance of each model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results are summarized and compared to identify the most effective model for breast cancer classification.
In this project, we explored various machine learning models for breast cancer classification and evaluated their performance using the Breast Cancer Wisconsin dataset. The results demonstrate the potential of machine learning algorithms in aiding the diagnosis of breast cancer, which can ultimately contribute to improving patient outcomes and reducing mortality rates.
Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.
- Breast Cancer Wisconsin (Diagnostic) dataset: UCI Machine Learning Repository
- Inspired by similar projects and tutorials available in the machine learning community.