def save():
data = get_data()
model = KNN()
model.fit(data)
pickle.dump(model, open('model.pkl', 'wb'))
import numpy as np
import matplotlib.pyplot as plt
import pydotplus
from io import StringIO
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from data_preprocessor import get_data
def run_random_forest(x_train, x_test, y_train, y_test):
clf = RandomForestClassifier(n_estimators=10)
clf.fit(x_train, y_train)
scores = cross_val_score(clf, x_test, y_test, cv=5)
print("random_forest: %.15f" % scores.mean())
if __name__ == '__main__':
x_train, x_test, y_train, y_test = get_data()
run_random_forest(x_train, x_test, y_train, y_test)
import pickle
import os.path
import sys
import json
from data_preprocessor import get_data
from sklearn.model_selection import train_test_split
import save_model
data = get_data()
if os.path.isfile("model.pkl"):
model = pickle.load(open('model.pkl', 'rb'))
else:
save_model.save()
model = pickle.load(open('model.pkl', 'rb'))
if len(sys.argv) > 1:
ID = int(sys.argv[1]) - 1
X_test = data.loc[ID].to_frame().T
preds = model.predict(X_test)
values = [[i + 1 for i in row] for row in preds]
feedback = json.dumps(values)
print(feedback)
from decision_tree import run_decision_tree
from k_nearest_neighbour import run_k_nearest_neighbour
from logistic_regression import run_logistic_regression
from naive_bayes import run_naive_bayes
from neural_network import run_neural_network
from perceptron import run_perceptron
from random_forest import run_random_forest
from svm import run_svm
from xg_boost import run_xg_boost
from pca import run_pca
from voting import run_voting
from data_preprocessor import get_data
if __name__ == '__main__':
x_train, x_test, y_train, y_test = get_data(True)
print(
"\n-------------------------------------\nAccuracies with top 5 features:\n-------------------------------------"
)
run_decision_tree(x_train, x_test, y_train, y_test)
run_k_nearest_neighbour(x_train, x_test, y_train, y_test)
run_logistic_regression(x_train, x_test, y_train, y_test)
run_naive_bayes(x_train, x_test, y_train, y_test)
run_neural_network(x_train, x_test, y_train, y_test)
run_perceptron(x_train, x_test, y_train, y_test)
run_random_forest(x_train, x_test, y_train, y_test)
run_svm(x_train, x_test, y_train, y_test)
run_xg_boost(x_train, x_test, y_train, y_test)
print(
