classification_iris_Data_Set.py

#!/usr/bin/env python
# coding: utf-8

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# Importing the required libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn import datasets
from sklearn import svm
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB

# import the iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# splitting X and y into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
	X, y, test_size=0.3, random_state=1)

# GAUSSIAN NAIVE BAYES
gnb = GaussianNB()
# train the model
gnb.fit(X_train, y_train)
# make predictions
gnb_pred = gnb.predict(X_test)
# print the accuracy
print("Accuracy of Gaussian Naive Bayes: ",
	accuracy_score(y_test, gnb_pred))

# DECISION TREE CLASSIFIER
dt = DecisionTreeClassifier(random_state=0)
# train the model
dt.fit(X_train, y_train)
# make predictions
dt_pred = dt.predict(X_test)
# print the accuracy
print("Accuracy of Decision Tree Classifier: ",
	accuracy_score(y_test, dt_pred))

# SUPPORT VECTOR MACHINE
svm_clf = svm.SVC(kernel='linear') # Linear Kernel
# train the model
svm_clf.fit(X_train, y_train)
# make predictions
svm_clf_pred = svm_clf.predict(X_test)
# print the accuracy
print("Accuracy of Support Vector Machine: ",
	accuracy_score(y_test, svm_clf_pred))


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