Breast-Cancer-Detection-Using-Machine-Learning

This project aims to assist in the early detection of breast cancer using Machine Learning (ML) techniques. Early diagnosis is crucial for improving survival rates, and this project provides a simple yet effective web-based prediction app that can classify whether a breast tumor is malignant or benign based on input medical data.

💖 Breast Cancer Detection using Machine Learning

Empowering early detection through Machine Learning and Awareness 🌸

This project demonstrates how Machine Learning can assist in the early diagnosis of breast cancer by predicting whether a tumor is Benign (non-cancerous) or Malignant (cancerous) using medical data.

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🧠 Overview

Breast cancer is one of the most common cancers worldwide, and early detection can save lives. This project leverages the Wisconsin Breast Cancer Diagnostic (WBCD) dataset and builds a Random Forest Classifier model that classifies tumors based on several key medical features extracted from cell nuclei.

The model is deployed using Streamlit, providing an interactive and visually appealing web interface where users can enter parameters and get instant predictions.

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📊 Dataset

Dataset Used: Wisconsin Breast Cancer Diagnostic Dataset (WBCD)

It contains 569 records with 30 numerical features describing cell characteristics such as:

• Mean Radius
• Mean Texture
• Mean Perimeter
• Mean Area
• Mean Smoothness
• Mean Concavity
• Mean Symmetry

Each record is labeled as:

• M → Malignant (Cancerous)
• B → Benign (Non-Cancerous)

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⚙️ Tech Stack

• 🐍 Python 3
• 💻 Streamlit — Web App Framework
• 🤖 Scikit-learn — Machine Learning Library
• 📦 Pickle — Model Serialization
• 📈 Pandas & NumPy — Data Preprocessing

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🚀 How It Works

1. The dataset is preprocessed and split into training and testing data.
2. A Random Forest Classifier is trained and saved as model.pkl.
3. The Streamlit app (app.py) loads this model.
4. Users enter feature values (mean radius, smoothness, concavity, etc.).
5. The app predicts whether the tumor is Benign or Malignant in real time.

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💡 Objective

To create an easy-to-use and visually engaging app that promotes breast cancer awareness and showcases the potential of Machine Learning in Healthcare.

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🖥️ Run the Project Locally

# Clone the repository
git clone https://github.com/yourusername/breast-cancer-detection.git

# Navigate to the project folder
cd breast-cancer-detection

# Install dependencies
pip install -r requirements.txt

# Run the Streamlit app
streamlit run app.py

Created by Rishu Raj