# New Technology

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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

'''
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]
'''``````