iris.py

# Load libraries
from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot as plt
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 dataset
filename = "iris.csv"
names = ["sepal-length", "sepal-width", "petal-length", "petal-width", "class"]

dataset = read_csv(filename, names=names)

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

print(dataset.describe())

print(dataset.groupby("class").std())

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

# histograms
# dataset.hist()
# plt.show()

# scatter plot matrix
# scatter_matrix(dataset)
# plt.show()

# Split original dataset/create validation dataset
data_array = dataset.values
X = data_array[:, 0:4]
y = data_array[:, 4]
(
    X_train, X_validation, Y_train, Y_validation
) = train_test_split(X, y, test_size=0.20, random_state=1)

# Spot Check Algorithms
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')))

# evaluate each model in turn
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(f'{name}: {cv_results.mean():f} ({cv_results.std():f})')

# Compare Algorithms
plt.boxplot(results, labels=names)
plt.title('Algorithm Comparison')
plt.show()

# Make predictions on validation dataset
model = SVC(gamma='auto')
model.fit(X_train, Y_train)
predictions = model.predict(X_validation)

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