Naive Bayes in Machine Learning

Dataset of patients who had undergone surgery for breast cancer.
Features of dataset:

• Age - Age of patient at time of operation.
• Year - Patient's year of operation (year - 1900).

• Nodes - Number of positive axillary nodes detected.
• Class(Survived):
  1 - the patient survived 5 years or longer
  2 - the patient died within 5 year
• Given the details of the patient we need to predict whether the patient survived or not.  
  

  Import required libraries

  
  # For mathematical calculation
  import numpy as np
  
  # For handling datasets
  import pandas as pd
  
  # For plotting graphs
  from matplotlib import pyplot as plt
  
  # Import the sklearn library for Naive bayes
  from sklearn.naive_bayes import GaussianNB
  

  Import dataset

  
  # Import the csv file
  df = pd.read_csv('data.csv')
  
  print df.head()
  '''
  Output:
     Age  Year  Nodes  Survived
  0   30    64      1         1
  1   30    62      3         1
  2   30    65      0         1
  3   31    59      2         1
  4   31    65      4         1
  '''
  

  Plot the classes against features.

  
  # We plot the data to see dependency of any 
  # feature on the class
  plt.xlabel('Feature')
  plt.ylabel('Survived') 
  
  X = df.loc[:,'Age']
  Y = df.loc[:,'Survived']
  plt.scatter(X, Y,color='blue',label='Age')
  
  X = df.loc[:,'Year']
  Y = df.loc[:,'Survived']
  plt.scatter(X, Y,color='green',label='Year')
  
  X = df.loc[:,'Nodes']
  Y = df.loc[:,'Survived']
  plt.scatter(X, Y,color='red',label='Nodes')
  
  plt.legend(loc=4, prop={'size': 7})
  plt.show()
  

  

   Prepare data for training

  
  # Prepare the training set
  X = df.loc[:,'Age':'Nodes']
  Y = df.loc[:,'Survived']
  

  Train the model

  
  clf = GaussianNB()
  
  # Train the model
  clf.fit(X,Y)
  

  Test the model

  
  # Test the model(returns the class)
  prediction = clf.predict([[12,70,12],
                            [13,20,13]])
  
  print prediction
  '''
  Output:
  [1 2]
  '''