KavithaShanmugam

## ner.py
"""
This is an simple implementation of a Named Entity Recognition (NER) from given text.
Here I have implemented a method to extract the names using NLTK (Natural Language Toolkit) instead of using Regular expression.
This is the very effective method to extract the names from the given text.

Steps:
******

1) Install the nltk package. Here is the instruction link

## iter_sample.py
"""

Programming task
================

Implement the method iter_sample below to make the Unit test pass. iter_sample
is supposed to peek at the first n elements of an iterator, and determine the
minimum and maximum values (using their comparison operators) found in that
sample. To make it more interesting, the method is supposed to return an
iterator which will return the same exact elements that the original one would

## ner.py
"""

This is an simple implementation of a Named Entity Recognition (NER) from given text.
Here I have implemented a method to extract the names using NLTK (Natural Language Toolkit) instead of using Regular expression.
This is the very effective method to extract the names from the given text.

Steps:
******

1) Install the nltk package. Here is the instruction link
	"""
	This is an simple implementation of a Named Entity Recognition (NER) from given text.
	Here I have implemented a method to extract the names using NLTK (Natural Language Toolkit) instead of using Regular expression.
	This is the very effective method to extract the names from the given text.

	Steps:
	******

	1) Install the nltk package. Here is the instruction link
	"""

	Programming task
	================

	Implement the method iter_sample below to make the Unit test pass. iter_sample
	is supposed to peek at the first n elements of an iterator, and determine the
	minimum and maximum values (using their comparison operators) found in that
	sample. To make it more interesting, the method is supposed to return an
	iterator which will return the same exact elements that the original one would