def main():
# read in the training data and test data
trainset = csv_function.read_csv("train_14.csv")
testset = csv_function.read_csv("test_14.csv")
# the first column of the training set will be the target for the classifier
target = [x[17] for x in trainset]
train = [x[3:16] for x in trainset]
test = [x[3:16] for x in testset]
# create and train the logistic regression
model = LogisticRegression()
model.fit(train, target)
print(model)
# make predictions
expected = target
predicted = model.predict(train)
# summarize the fit of the model
print(metrics.classification_report(expected, predicted))
print(metrics.confusion_matrix(expected, predicted))
# make predictions
pred_test = model.predict(test)
csv_function.write_csv("submission_lr.csv", list(pred_test))
def main():
# read in the training data and test data
trainset = csv_function.read_csv("train_14.csv")
testset = csv_function.read_csv("test_14.csv")
# the first column of the training set will be the target for the classifier
target = [x[17] for x in trainset]
train = [x[3:16] for x in trainset]
test = [x[3:16] for x in testset]
# create and fit the adaboost model
model = AdaBoostClassifier(DecisionTreeClassifier(), algorithm="SAMME", n_estimators=200)
model.fit(train, target)
# predicted = model.predict(train)
# expected = target
#
# # summarize the fit of the model
# print(metrics.classification_report(expected, predicted))
# print(metrics.confusion_matrix(expected, predicted))
predict_test = model.predict(test)
csv_function.write_csv("submission_ada.csv", list(predict_test))