Breast Cancer Classifier Using Machine Learning

This project is a Machine Learning-based breast cancer classifier that predicts whether a tumor is malignant or benign using key clinical data. The model is built with Python and leverages popular ML libraries.

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Features

• Data Preprocessing: Handles missing values, normalizes data, and prepares it for model training.
• Model Training: Uses supervised learning algorithms, including Logistic Regression, Support Vector Machines, and Random Forests.
• Evaluation Metrics: Includes accuracy, precision, recall, and F1 score for model performance evaluation.

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Dataset

The dataset used in this project is sourced from the Breast Cancer Wisconsin (Diagnostic) dataset. It includes the following features:

• Mean radius, texture, perimeter, area, and more.
• Diagnosis: M (Malignant) or B (Benign).

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Installation

1. Clone the repository:

   git clone https://github.com/s3bu7i/ML-Breast-Cancer-Classifier.git

2. Navigate to the project directory:

   cd ML-Breast-Cancer-Classifier

3. Install the required dependencies:

   pip install -r requirements.txt

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Usage

1. Run the preprocessing script:

   python preprocess.py

2. Train the model:

   python train.py

3. Evaluate the model:

   python evaluate.py

4. Predict new samples:

   python predict.py

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Model Performance

The classifier achieves high accuracy and reliability in distinguishing between malignant and benign cases. Below are the results of key evaluation metrics:

• Accuracy: 97%
• Precision: 96%
• Recall: 95%
• F1 Score: 95%

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Project Structure

ML-Breast-Cancer-Classifier/
├── data/                 # Dataset and preprocessing scripts
├── models/               # Saved models
├── notebooks/            # Jupyter notebooks for exploratory data analysis
├── scripts/              # Training and evaluation scripts
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

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Future Enhancements

• Implement deep learning models for improved performance.
• Explore feature selection and optimization techniques.
• Build a web application for real-time classification.

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