# Classification template

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('diabetes.csv')
X = dataset.iloc[:, 0:8].values
y = dataset.iloc[:, 8].values

# Feature Selection
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
X = SelectKBest(chi2, k=4).fit_transform(X, y)

# Managing Missing Values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values=0 , strategy="mean",axis=0)
X[:,0:5] = imputer.fit_transform(X[:,0:5])

# 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)

# Fitting classifier to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5,metric = 'minkowski',p=2)
classifier.fit(X_train,y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
