import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler

Malware = pd.read_csv("Malware.csv")
Clean_Files = pd.read_csv("Clean_Files.csv")

y = dataset['Clean_Files']
X = dataset.drop('Clean_Files',axis = 1)
X = np.asarray(X)
y = np.asarray(y)


X_train,X_test,y_train,y_test = train_test_split(X,y,test_size =0.3,random_state=0)

scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

MLP = MLPClassifier(hidden_layer_sizes=(12,12,12,12,12,12))

MLP.fit(X_train,y_train)
Predictions = MLP.predict(X_test)

TN, FP, FN TP = confusion_matrix(y_test,predictions).ravel()

print (“True Positive:” , TP)
print (“True Negative:” , TN)
print (“False Positive:” , FP)
print (“False Negative:” , FN)





