Cancer-Detection

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🩺 Breast Cancer Detection using Logistic Regression

This project builds a Machine Learning model to predict whether a tumor is malignant or benign using the Breast Cancer Wisconsin Dataset. It also includes a Plotly-based interactive dashboard for data exploration and visual insights.

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📊 Demo Dashboard

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🚀 Project Highlights

• 🔍 Exploratory Data Analysis (EDA)
• 🧪 Logistic Regression Classifier
• 📈 ROC Curve, Confusion Matrix, and Classification Report
• 🖼️ Plotly/Dash Dashboard for interactive visualization
• 💾 Dataset: Breast Cancer Wisconsin (Diagnostic) on Kaggle

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📁 Folder Structure

Cancer-Detection/
│
├── app.py                   # Plotly Dashboard (Dash App)
├── playground.ipynb        # Colab/EDA Notebook
├── model.pkl               # Trained ML model
├── cancer\_data.csv         # Dataset file
├── requirements.txt        # All dependencies
└── README.md               # Project info

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🧠 ML Pipeline

1. Data Preprocessing
   • Handling missing values
   • Feature scaling (StandardScaler)
2. Model Training
   • Logistic Regression
3. Evaluation
   • Accuracy, Confusion Matrix, ROC-AUC
4. Dashboard
   • Visualize predictions interactively

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📊 Sample Visuals

• Confusion Matrix with Heatmap
• ROC Curve
• Scatter Matrix & Feature Correlations
• Live Prediction Dashboard

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📦 Requirements

Install all required libraries:

pip install -r requirements.txt

Or manually install:

pip install pandas scikit-learn matplotlib seaborn plotly dash

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▶️ How to Run

🔬 Jupyter Notebook (EDA + Model)

jupyter notebook playground.ipynb

🌐 Dash App

python app.py

It will start a server on http://127.0.0.1:8050.

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✍️ Author

Siddhant 📧 [shaurya.sid1729@gmail.com] 🔗 GitHub

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⭐ Give a Star

If you like this project, consider ⭐ starring the repo!

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Would you like me to:
- Include the **Streamlit** version?
- Add a **badge section** (like GitHub stars, license, etc.)?
- Generate a `requirements.txt` for you?

Let me know!