lineaPy5.py

#RandomeForestClassifer
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
model.score(X, y)
acc_random_forest = round(model.score(X, y) * 100, 2)
acc_random_forest

# Gaussian Naive Bayes
from sklearn.naive_bayes import GaussianNB
gaussian = GaussianNB()
gaussian.fit(X, y)
Y_pred = gaussian.predict(X_test)
acc_gaussian = round(gaussian.score(X, y) * 100, 2)
acc_gaussian

# Support Vector Machines
from sklearn.svm import SVC, LinearSVC
svc = SVC()
svc.fit(X, y)
Y_pred = svc.predict(X_test)
acc_svc = round(svc.score(X, y) * 100, 2)
acc_svc

# Decision Tree
from sklearn.tree import DecisionTreeClassifier
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X, y)
Y_pred = decision_tree.predict(X_test)
acc_decision_tree = round(decision_tree.score(X, y) * 100, 2)
acc_decision_tree

# Stochastic Gradient Descent
from sklearn.linear_model import SGDClassifier
sgd = SGDClassifier()
sgd.fit(X, y)
Y_pred = sgd.predict(X_test)
acc_sgd = round(sgd.score(X, y) * 100, 2)
acc_sgd