# Data Preprocessing Template

# Importing the libraries
import numpy as np
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('breast_cancer.txt',header=None)
X = dataset.iloc[:, 2:].values
y= dataset.iloc[:,1].values

from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()
y = labelencoder_X.fit_transform(y)

#y = X[:,0]
y= y.astype(np.float)
#y_data = pd.DataFrame(Y)
#y=y_data.iloc[:,0].values
#X=dataset.iloc[:,2:].values

# Splitting the dataset into the Training set and Test 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, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train,y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import confusion_matrix
cm= confusion_matrix(y_test,y_pred)
