This repository contains code for preprocessing the Winconsin cancer dataset, training models, and assessing their performance using metrics like roc auc curve and confusion matrix.
import seaborn as sns
Tasks:
- Train models to see which classifier performs best on the Winconsin breast cancer dataset
- Show performance metrics to user
Models
- Logistic regression
- Gaussian Naive bayes
- KNN
- SVM
- Decision Tree
- Random Forest
- Gradient Boosting
Metrics
- confusion matrix
- roc auc curve
Tweaks
- Hyperparameter tuning
Clone the repository
~ git clone https://github.com/rizanB/breast-cancer.git .
Take a look at the notebook
~ my.ipynb
Running with Docker
- Build Docker image
docker build -t breast-cancer .
- Run a container with the image
docker run -p 5000:5000 breast-cancer
Running without Docker
- Install required packages
pip install -r requirements.txt
- Start Flask server
python app.py
Accessing the application Open a browser and visit http://localhost:5000.