https://github.com/abhishekyadav915/breast_cancer_classification_using_deep_learning

This project demonstrates Breast Cancer Classification using a deep learning model built with TensorFlow and Keras. The model is trained on the Breast Cancer Wisconsin Dataset and predicts whether a tumor is Benign (1) or Malignant (0).
https://github.com/abhishekyadav915/breast_cancer_classification_using_deep_learning

deeplearning keras-tensorflow matplotlib-pyplot numpy-library pandas-library sklearn-library standardization tensorflow

Last synced: 8 months ago
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This project demonstrates Breast Cancer Classification using a deep learning model built with TensorFlow and Keras. The model is trained on the Breast Cancer Wisconsin Dataset and predicts whether a tumor is Benign (1) or Malignant (0).

• Host: GitHub
• URL: https://github.com/abhishekyadav915/breast_cancer_classification_using_deep_learning
• Owner: AbhishekYadav915
• Created: 2025-02-04T09:51:21.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-02-04T10:12:34.000Z (10 months ago)
• Last Synced: 2025-02-04T11:22:49.290Z (9 months ago)
• Topics: deeplearning, keras-tensorflow, matplotlib-pyplot, numpy-library, pandas-library, sklearn-library, standardization, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 77.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Breast Cancer Classification using Deep Learning

## Overview

This project demonstrates **Breast Cancer Classification** using a deep learning model built with **TensorFlow and Keras**. The model is trained on the **Breast Cancer Wisconsin Dataset** and predicts whether a tumor is **Benign (1) or Malignant (0).**

## Features

- Loads the dataset from `sklearn.datasets`.

- Performs **data preprocessing** (handling missing values, standardization).

- Builds a **Neural Network** for classification.

- Trains the model using **Adam optimizer & sparse categorical crossentropy loss**.

- Evaluates the model using **accuracy & loss visualization plots**.

- Implements a **predictive system** to classify new data.

## Installation

Clone the repository:

```bash

git clone https://github.com/yourusername/Breast_Cancer_Classification.git

cd Breast_Cancer_Classification

```

Install dependencies:

```bash

pip install -r requirements.txt

```

## Usage

Run the Jupyter Notebook:

```bash

jupyter notebook Breast_Cancer_Classification.ipynb

```

## Dataset

The dataset used is the **Breast Cancer Wisconsin Dataset**, available in the `sklearn.datasets` module.

## Model Architecture

- **Input Layer:** Takes standardized feature data.

- **Hidden Layers:** Fully connected layers with ReLU activation.

- **Output Layer:** A softmax layer for binary classification.

## Results

### Accuracy & Loss Plots:

![Accuracy Plot](path/to/accuracy_plot.png)

![Loss Plot](path/to/loss_plot.png)

### Example Prediction:

| Features | Prediction |

|----------|------------|

| [15.3, 20.4, ...] | Malignant (0) |

| [12.1, 18.5, ...] | Benign (1) |

## Next Steps

- Apply **hyperparameter tuning** to improve accuracy.

- Deploy the model as a **Flask or FastAPI web app**.

- Convert the notebook into a **Python script for automation**.

## Contributing

Feel free to open an issue or submit a pull request if you find any improvements!

## License

This project is licensed under the MIT License.