main.py

# Load dependencies
from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
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

# Load the dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv(url, names=names)

# Summarize the Dataset

# Shape
#print(dataset.shape)

# Head
#print (dataset.head(20))

# Descriptions
#print(dataset.describe())

# Class distribution
#print (dataset.groupby('class').size())

# Box and whisker plots
#dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)

# Histograms
#dataset.hist()

# scatter plot_matrix
#scatter_matrix(dataset)

#pyplot.show()

# split-out validation dataset
array = dataset.values
X = array[:,0:4]
Y = array[:,4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=0.20, random_state=1)

models = []

models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma='auto')))


results = []
names = []

for name, model in models:
    kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)
    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
    results.append(cv_results)
    names.append(name)
    print('%s: %f (%F)' % (name, cv_results.mean(), cv_results.std()))


#pyplot.boxplot(results, labels=names)
#pyplot.title('Algorithm Comparison')
#pyplot.show()

# Make prediction
mdel = SVC(gamma='auto')
model.fit(X_train, Y_train)
predictions = model.predict(X_validation)

# Evaluate prediction
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))