IRIS ML Project

Using the IRIS dataset to estimate the class of the IRIS using different ML Algorithms.

	import pandas
	from pandas.plotting import scatter_matrix
	import matplotlib.pyplot as plt
	from sklearn import model_selection
	from sklearn.metrics import classification_report
	from sklearn.metrics import confusion_matrix
	from sklearn.metrics import accuracy_score
	from sklearn.linear_model import LogisticRegression
	from sklearn.tree import DecisionTreeClassifier
	from sklearn.neighbors import KNeighborsClassifier
	from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
	from sklearn.naive_bayes import GaussianNB
	from sklearn.svm import SVC
	url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
	names = ['sepal length','sepal width','petal length','petal width','class']
	dataset = pandas.read_csv(url,names=names)
	print(dataset.shape) #print rows and cols
	print(dataset.head(10)) #print first 10 rows
	print(dataset.describe()) #prints values or ranges of the dataset
	print(dataset.groupby('class').size()) #prints the class object attributes and size of it
	#plots
	dataset.plot(kind = 'box', subplots = True, layout=(2,2),sharex=False,sharey=False)
	plt.title('MY IRIS PLOT')
	plt.show()
	dataset.hist()
	plt.show()
	scatter_matrix(dataset)
	plt.show()
	array = dataset.values
	X = array[:,0:4]
	Y = array[:,4]
	validation_size = 0.20
	seed = 6
	X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X,Y, test_size=validation_size, random_state = seed)
	seed =6
	scoring = 'accuracy'
	#spot check algorithms
	models= []
	models.append(('LR', LogisticRegression()))
	models.append(('CART', DecisionTreeClassifier()))
	models.append(('KNN', KNeighborsClassifier()))
	models.append(('LDA', LinearDiscriminantAnalysis()))
	models.append(('NB', GaussianNB()))
	models.append(('SVM', SVC()))
	#evaluate each model in turn
	results= []
	names = []
	for name,model in models:
	kfold = model_selection.KFold(n_splits=10,random_state =seed)
	cv_results = model_selection.cross_val_score(model, X_train,Y_train, cv=kfold, scoring=scoring)
	results.append(cv_results)
	names.append(name)
	msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
	print(msg)