A simple Flask application that can serve predictions from a scikit-learn model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict endpoint. You can also use the /train endpoint to train/retrain the model. Any sklearn model can be used for prediction.
Read more in this blog post.
- scikit-learn
- Flask
- pandas
- numpy
if you're not on the Bell network you can run the following on Git Bash
pip install -r requirements.txt
python main.py <port>
Returns an array of predictions given a JSON object representing independent variables. Here's a sample input:
[
{'Age': 85, 'Sex': 'male', 'Embarked': 'S'},
{'Age': 24, 'Sex': 'female', 'Embarked': 'C'},
{'Age': 3, 'Sex': 'male', 'Embarked': 'C'},
{'Age': 21, 'Sex': 'male', 'Embarked': 'S'}
]
and sample output:
{'prediction': [0, 1, 1, 0]}
Trains the model. This is currently hard-coded to be a random forest model that is run on a subset of columns of the titanic dataset.
Removes the trained model.