Baseline.csv : 97.72% , full.csv : 86.36%

mlp_signature.py

import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.neural_network import MLPClassifier

df = pd.read_csv('Baseline.csv') #Baseline.csv dengan full.csv
maxi = 0
max_i = 0
max_j = 0
for i in range(10):
	data = df.sample(frac=0.7, random_state=i)
	data_t = df.drop(data.index)

	train_in = data.drop('Features',1);
	train_out = data['Features'];

	test_in = data_t.drop('Features',1);
	test_out = data_t['Features'];

	for j in range(10):
		clf = MLPClassifier(max_iter = 500, solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(100), random_state=j)

		clf = clf.fit(train_in,train_out)
		predicted = clf.predict(test_in)

		if maxi < accuracy_score(test_out,predicted):
			maxi = accuracy_score(test_out,predicted)
			max_i = i
			max_j = j
			print(str(maxi*100)+"% "+str(max_i)+" "+str(max_j))

print("The best is : "+str(maxi*100)+"% "+str(max_i)+" "+str(max_j))