4_make_predictions.py

# 1. Load Libraries
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

# 2. Load the Data
names=['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv('iris.csv', names=names)

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=0.2, random_state=1)

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

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