Created
February 23, 2018 16:49
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# This is a sample model to demonstrate how a Machine Learning model | |
# can be implemented in Production as a REST API and how it can be consumed | |
# Import libraries and packages | |
from sklearn import svm, datasets | |
import pickle | |
import numpy as np | |
# Load Sample data | |
iris = datasets.load_iris() | |
# Split loaded data into independent and target features | |
X = iris.data | |
y = iris.target | |
# Train Support Vector Machine (SVM) model with all data | |
svmModel = svm.SVC(kernel='poly', degree=3, C=1.0).fit(X, y) | |
# Persist model so that it can be used by different consumers | |
svmFile = open('SVMModel.pckl', 'wb') | |
pickle.dump(svmModel, svmFile) | |
svmFile.close() |
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