Goal of the project is to build a machine learning model using the xgboost algorithm to predict the flowers, using the iris dataset. In addition to the model, a user would be able to submit requests with new data and get a prediction as a response.
Follow "Build from the source code - advanced method" https://xgboost.readthedocs.io/en/latest/build.html. I needed to install cmake for that to work and at the end install using python setup.py install
If you run the notebook under notebooks/iris-xgboost.ipynb, it trains an xgboost model and saves it in app/model.pkl
Run python app.py to start the server
Sample request: curl -X GET "http://0.0.0.0:5000/api" -H "Content-Type: application/json" --data '{"sepal length (cm)": "6.7","sepal width (cm)": "3.1","petal length (cm)":"4.4", "petal width (cm)":"1.4"}'
This is expected to return {"PREDICTION": "versicolor"}