prometheussx / breast-cancer-rf-classification

A project that uses Random Forest for descriptive breast cancer diagnosis, classifying breast tumors as malignant or benign.

Breast Cancer Classification Using Random Forest

Breast cancer is a prevalent form of cancer among women. Early detection and accurate classification of breast tumors are crucial for effective treatment. This project aims to classify breast tumors as malignant (cancerous) or benign (non-cancerous) by using the Random Forest algorithm.

Project Overview

In this project, we perform breast cancer classification using a dataset that contains features extracted from breast mass images. The Random Forest classification algorithm is employed for its ability to make accurate predictions and handle complex data.

Dataset

The dataset used in this project is available in the Cancer_Data.csv file. It includes the following features:

• radius_mean: Mean of distances from the center to points on the perimeter.
• texture_mean: Standard deviation of gray-scale values.
• ... (other feature descriptions)

Project Structure

The project is organized as follows:

• Cancer_Data.csv: The dataset used for classification.
• Breast-Cancer-RF-Classification.py: Jupyter Notebook containing the Python code for data analysis, model training, and evaluation.

Requirements

To run the project, you need the following Python libraries:

• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• Seaborn (for visualization)

You can install these libraries using pip:

pip install numpy 
pip install pandas 
pip install matplotlib 
pip install scikit-learn 
pip install seaborn

Usage

To execute the breast cancer classification code:

1. Clone this repository to your local machine:

git clone https://github.com/Prometheussx/Breast-Cancer-RF-Classification.git

2. Navigate to the project folder:

cd Breast-Cancer-RF-Classification

3. Open and run the Breast-Cancer-RF-Classification.py Jupyter Notebook.

4. Follow the code cells to load the dataset, preprocess the data, train the Random Forest classifier, and evaluate the model's performance.

Model Evaluation

We evaluate the model using the Random Forest classifier and calculate the accuracy of predictions on the test dataset.

Results

The project provides a breast cancer classification model with high accuracy, making it a valuable tool for early diagnosis.

Contact Information

For any questions, feedback or requests to contribute to the project, you can contact the contact information below:

• LinkedIn: [https://www.linkedin.com/in/erdem-taha-sokullu/]
• Email: [[email protected]]
• Kaggle: [https://www.kaggle.com/erdemtaha]

Contributing

If you'd like to contribute to this project or report any issues, please create a GitHub issue or send a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.