🩺 Breast Cancer Diagnosis Using Machine Learning

Machine learning-based breast cancer diagnosis using the Breast Cancer Wisconsin dataset — predicting whether a tumor is benign or malignant using deep learning and traditional ML techniques.

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📘 Project Overview

Breast cancer is one of the most common cancers affecting women worldwide.
This project builds a neural network model that classifies tumors as benign (non-cancerous) or malignant (cancerous) using features computed from a digitized image of a breast mass.

The model achieves ~98% accuracy, demonstrating how machine learning can assist early diagnosis and clinical decision-making.

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📂 Dataset

The dataset used in this project is data.csv stored in the dataset/ folder.

Dataset Description

• Features: 30 numerical features from the Wisconsin Breast Cancer dataset
• Target: Diagnosis (Benign or Malignant)

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🧠 Model Architecture

flowchart LR
    A["📥 Data Collection"] --> B["🧹 Data Cleaning"]
    B --> C["⚙️ Feature Engineering"]
    C --> D["🧠 Model Training"]
    D --> E["📊 Model Evaluation"]
    E --> F["🚀 Deployment / Prediction"]

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🔍 Project Pipeline

flowchart TD
    A["📥 Dataset - Breast Cancer Wisconsin"] --> B["🧹 Data Preprocessing"]
    B --> C["📊 Train-Test Split"]
    C --> D["⚙️ Feature Scaling - StandardScaler"]
    D --> E["🧠 Model Building - Neural Network (Keras)"]
    E --> F["📈 Model Training"]
    F --> G["📊 Evaluation - Accuracy & Loss Curves"]
    G --> H["🎯 Predictions on Test Data"]
    H --> I["✅ Result - Benign or Malignant"]

    %% Node Colors
    style A fill:#FFB347,stroke:#333,stroke-width:2px,color:#000
    style B fill:#FFD700,stroke:#333,stroke-width:2px,color:#000
    style C fill:#FFA07A,stroke:#333,stroke-width:2px,color:#000
    style D fill:#87CEFA,stroke:#333,stroke-width:2px,color:#000
    style E fill:#9370DB,stroke:#333,stroke-width:2px,color:#fff
    style F fill:#40E0D0,stroke:#333,stroke-width:2px,color:#000
    style G fill:#FF69B4,stroke:#333,stroke-width:2px,color:#000
    style H fill:#98FB98,stroke:#333,stroke-width:2px,color:#000
    style I fill:#32CD32,stroke:#333,stroke-width:2px,color:#fff

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⚙️ Tech Stack

Category	Tools / Libraries
Programming Language	Python 3.x
Machine Learning	TensorFlow / Keras, Scikit-learn
Data Processing	Pandas, NumPy
Visualization	Matplotlib, Seaborn
Environment	Jupyter Notebook / Google Colab

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🧩 Model Performance

Metric	Value
Training Accuracy	~99%
Validation Accuracy	~98%
Loss Function	Binary Crossentropy
Optimizer	Adam

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🚀 Future Improvements

• Deploy as a Streamlit web app for interactive diagnosis
• Experiment with Deep Neural Networks (DNN) and CNNs
• Integrate with medical data APIs for real-world use

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📜 License

This project is released under the MIT License — feel free to use and modify for research and learning purposes.