The first step is to **import** the `nltk` library into our Python environment. This is your gateway to a plethora of text processing tools and techniques that NLTK offers.

Once `nltk` is successfully imported, we will take an important step in our project - defining the text that we will analyze and process. For this purpose, we'll be using a variable named `story`. This variable will **hold the textual data** that you wish to work on. It could be any piece of text - a short story, an excerpt from a book, a paragraph of your choice, or even a collection of sentences that you're interested in analyzing. 

> **Note**
>
> The first task's tests are tailored to function with the initial text provided. Therefore, if you choose to use a different text for analysis, it's okay to disregard any errors that might arise from the initial test.

> **Note**
>
> If you want to use `nltk` on your computer, you need to install it first using the following command:
>```
>pip install nltk
>```

In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data. Our focus will encompass several core areas of natural language processing: tokenization, stemming, tagging and parsing. These NLTK features will form the backbone of our text processing and analysis tasks, making it an essential tool in our project for handling and extracting meaningful insights from language data.

In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data.

Identifying the Most Frequent Words in Text

Import NLTK