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
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.
