Breast Cancer Classifier

Welcome to the Breast Cancer Classifier repository! This project aims to analyze breast cancer test data and build a model to classify whether a person has malignant (M) or benign (B) breast cancer.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Model
• Results

Introduction

Breast cancer classification is a critical task in the medical field, helping in early detection and treatment planning. This repository provides a comprehensive workflow to classify breast cancer using machine learning techniques. The goal is to build a model that accurately classifies breast cancer as malignant (M) or benign (B) based on various features from breast cancer test data.

Dataset

The dataset used in this project contains features extracted from breast cancer test results, such as:

• Radius
• Texture
• Perimeter
• Area
• Smoothness
• Compactness
• Concavity
• Concave points
• Symmetry
• Fractal dimension
• And more...

The dataset is available in the data directory.

Installation

To get started with the project, clone this repository and install the required dependencies.

git clone https://github.com/TarunBhatia11/breast-cancer-classifier.git
cd breast-cancer-classifier
pip install -r requirements.txt

Ensure you have Python 3.7 or higher installed.

Usage

To run the analysis and model training, follow these steps:

1. Data Preprocessing: Preprocess the dataset to handle missing values, encode categorical features, and normalize numerical features.

   python preprocess.py

2. Model Training: Train the model using the preprocessed data.

   python train.py

3. Prediction: Use the trained model to make predictions on new data.

   python predict.py --input new_data.csv --output predictions.csv

Model

We explore several machine learning models, including:

• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)
• Gradient Boosting

The best-performing model is selected based on evaluation metrics such as accuracy, precision, recall, and F1-score.

Results

The results of the model training and evaluation are stored in the results directory. This includes:

• Evaluation metrics
• Confusion matrix
• ROC curve
• Feature importance

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. Follow these steps to contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature-name)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/your-feature-name)
5. Create a new Pull Request

Disclaimer

This project is generally educational purposes.

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Thank you for checking out the Breast Cancer Classifier project! If you have any questions or need further assistance, feel free to reach out.