Logistic Regression

#Import Libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

#Import Dataset

dataset = pd.read_csv('bratsfinal1.csv',names = ['firstaxis_1','firstaxis_2','firstaxis_3','secondaxis_1','secondaxis_2','secondaxis_3','thirdaxis_1','thirdaxis_2','thirdaxis_3','eigen1','eigen2','eigen3','firstaxis_len','secondaxis_len','thirdaxis_len','c1','c2','c3','mer_ecc','eq_ecc','age','survival'])

datset2 = pd.read_csv('validation_data.csv',names = ['firstaxis_1','firstaxis_2','firstaxis_3','secondaxis_1','secondaxis_2','secondaxis_3','thirdaxis_1','thirdaxis_2','thirdaxis_3','eigen1','eigen2','eigen3','firstaxis_len','secondaxis_len','thirdaxis_len','c1','c2','c3','mer_ecc','eq_ecc','age'])

X = dataset.iloc[:, 0:21].values

y = dataset.iloc[:, 21:22].values

#Split Training Set and Testing Set

# from sklearn.cross_validation import train_test_split

# x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.25)

#Feature Scaling

from sklearn.preprocessing import StandardScaler

sc_X=StandardScaler()

x_train=sc_X.fit_transform(X)

x_test=sc_X.transform(datset2)

#Training the Logistic Model

from sklearn.linear_model import LogisticRegression

classifier = LogisticRegression()

classifier.fit(x_train, y)

#Predicting the Test Set Result

y_pred = classifier.predict(x_test)

print y_pred

#Create Confusion Matrix for Evaluation

# from sklearn.metrics import confusion_matrix

# cm = confusion_matrix(y_test, y_pred)