I'm doing the "Hello world" in machine learning, using the Iris dataset. I already have an acceptable result for the entry of this model, I am using 80% of the information to train it and the remaining 20% to do the validation. I am using 6 prediction algorithms, which work well.
but I have a problem, how can I insert new information so that it is analyzed? How do I insert the characteristics of a flower and tell me the type of iris it is? Either: Iris-setosa, Iris-versicolor or Iris-virginica?
# Load libraries import pandas from pandas.plotting import scatter_matrix 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 # Load dataset url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv" names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class'] dataset = pandas.read_csv(url, names=names) #######Evaluate Some Algorithms######## #Create a Validation Dataset # Split-out validation dataset array = dataset.values X = array[:,0:4] Y = array[:,4] validation_size = 0.20 seed = 7 X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed) ########Build Models######## # 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 = 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) ########Make Predictions######## print('######## Make Predictions ########') # Make predictions on validation dataset knn = KNeighborsClassifier() knn.fit(X_train, Y_train) predictions = knn.predict(X_validation) print(accuracy_score(Y_validation, predictions)) print(confusion_matrix(Y_validation, predictions)) print(classification_report(Y_validation, predictions)) 
