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model.py
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model.py
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#!/usr/bin/env python
# coding: utf-8
import pickle
from sklearn import datasets
iris=datasets.load_iris()
x=iris.data
y=iris.target
#labels for iris dataset
labels ={
0: "setosa",
1: "versicolor",
2: "virginica"
}
#split the data set
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.60)
#Using decision tree algorithm
from sklearn import tree
classifier=tree.DecisionTreeClassifier()
classifier.fit(x_train,y_train)
predictions=classifier.predict(x_test)
#export the model
pickle.dump(classifier, open('model.pkl','wb'))
#load the model and test with a custom input
model = pickle.load( open('model.pkl','rb'))
x = [[6.7, 3.3, 5.7, 2.1]]
predict = model.predict(x)
print(predict)
print("Hello Worlds")
print(labels[predict[0]])
#print(labels[predict[0]])