https://github.com/s3bu7i/ml-breast-cancer-classifier
https://github.com/s3bu7i/ml-breast-cancer-classifier
algorithms-and-data-structures ml ml-engineering model
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/s3bu7i/ml-breast-cancer-classifier
- Owner: s3bu7i
- License: mit
- Created: 2024-12-08T23:09:09.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-01-07T20:57:15.000Z (11 months ago)
- Last Synced: 2025-01-07T21:39:11.930Z (11 months ago)
- Topics: algorithms-and-data-structures, ml, ml-engineering, model
- Language: Python
- Homepage:
- Size: 2.91 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
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README
# Breast Cancer Classifier Using Machine Learning
This project is a Machine Learning-based breast cancer classifier that predicts whether a tumor is malignant or benign using key clinical data. The model is built with Python and leverages popular ML libraries.
---
## Features
- **Data Preprocessing**: Handles missing values, normalizes data, and prepares it for model training.
- **Model Training**: Uses supervised learning algorithms, including Logistic Regression, Support Vector Machines, and Random Forests.
- **Evaluation Metrics**: Includes accuracy, precision, recall, and F1 score for model performance evaluation.
---
## Dataset
The dataset used in this project is sourced from the [Breast Cancer Wisconsin (Diagnostic) dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)). It includes the following features:
- Mean radius, texture, perimeter, area, and more.
- Diagnosis: `M` (Malignant) or `B` (Benign).
---
## Installation
1. Clone the repository:
```bash
git clone https://github.com/s3bu7i/ML-Breast-Cancer-Classifier.git
```
2. Navigate to the project directory:
```bash
cd ML-Breast-Cancer-Classifier
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
---
## Usage
1. Run the preprocessing script:
```bash
python preprocess.py
```
2. Train the model:
```bash
python train.py
```
3. Evaluate the model:
```bash
python evaluate.py
```
4. Predict new samples:
```bash
python predict.py
```
---
## Model Performance
The classifier achieves high accuracy and reliability in distinguishing between malignant and benign cases. Below are the results of key evaluation metrics:
- **Accuracy**: 97%
- **Precision**: 96%
- **Recall**: 95%
- **F1 Score**: 95%
---
## Project Structure
```
ML-Breast-Cancer-Classifier/
├── data/ # Dataset and preprocessing scripts
├── models/ # Saved models
├── notebooks/ # Jupyter notebooks for exploratory data analysis
├── scripts/ # Training and evaluation scripts
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
---
## Future Enhancements
- Implement deep learning models for improved performance.
- Explore feature selection and optimization techniques.
- Build a web application for real-time classification.
---