Breast Cancer Detection

A machine learning-powered web application for detecting breast cancer (Malignant or Benign) based on cell nucleus features. Built with Python, Scikit-Learn, and Streamlit.

Overview

This project utilizes a Support Vector Classifier (SVC) model to predict whether a breast mass is malignant or benign. The model is trained on the Breast Cancer Wisconsin (Diagnostic) Dataset. Users can input specific features of the cell nucleus through a user-friendly web interface and get instant predictions.

Features

• Interactive Web Interface: easy-to-use form for inputting data points using Streamlit.
• Real-time Prediction: Instant classification results.
• Machine Learning Integration: Uses a pre-trained SVC model for accurate predictions.

key Features Used for Prediction

The model uses the following features (Mean, Standard Error, and Worst):

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

Installation

1. Clone the repository:

   git clone <repository_url>
   cd cancerdetection/CANCER-DETECTION

2. Install dependencies: It is recommended to use a virtual environment.

   pip install -r requirements.txt

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Access the app: Open your browser and navigate to the local URL provided in the terminal (usually http://localhost:8501).

3. Make a prediction: Enter the values for the various features in the input fields and click the predict button.

Files

• app.py: The main Streamlit application script.
• SVC.joblib: The pre-trained Support Vector Classifier model.
• requirements.txt: List of Python dependencies.