Authorship Classification using NLTK

This readme file which explains how to setup environment and run the code by installing prerequisite.

Libraries used

This Program uses a number of Python libraries:

• NumPy - NumPy is the fundamental package for scientific computing with Python
• NLTK - Natural Language Toolkit
• scikit-learn - Simple and efficient tools for data mining and data analysis
• Pandas - pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
• Matplotlib - Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.

And of course this project itself is open source with a public repository on GitHub.

Installation and How to run

• It is recommended to use Anaconda as the python environment running python 3
• Download and install Python
• Download and install Anaconda from this link
• Use Spyder IDE from Anaconda Naviagtor in order to run the code.
• This program requires NLTK BOOKS to run.
• Install the required books before running code. Run the Python interpreter and type the commands:

>>> import nltk
>>> nltk.download()

System requirements

anaconda website

• Operating system: Windows 7 or newer, 64-bit macOS 10.10+, or Linux, including Ubuntu, RedHat, CentOS 6+, and others.
• If your operating system is older than what is currently supported, you can find older versions of the Anaconda installers in our archive that might work for you. Check our FAQ for version recommendations.
• System architecture: Windows- 64-bit x86, 32-bit x86; MacOS- 64-bit x86; Linux- 64-bit x86, 32-bit x86, 64-bit Power8/Power9.
• Minimum 5 GB disk space to download and install.

On Windows, macOS, and Linux, it is best to install Anaconda for the local user, which does not require administrator permissions and is the most robust type of installation. However, if you need to, you can install Anaconda system wide, which does require administrator permissions.

System used to develop this program

• Macbook Pro running macOS 10.14 Mojave
• 16 Gb RAM
• i7 Processor
• Anaconda navigator with Python 3.7.1

License

Free