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Predict the future words efficiently with the "Next Word Prediction Using Markov Model" project. Built in Python and powered by the `msvcrt` module, this academic initiative explores the Markov chain model to anticipate the most likely next word based on a given sequence.

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Next Word Prediction Using Markov Chain Model 🧮

The "Next Word Prediction Using Markov Model" project is an academic initiative centered around the Markov chain model concept. Developed entirely in Python and utilizing the msvcrt module, this project aims to create a practical next word prediction system. The main objective is to leverage the predictive capabilities of Markov models to anticipate the most likely word to come after a given sequence of words. The implementation in Python, with the support of the msvcrt module, ensures a smooth and efficient execution, making it a valuable exploration of language modeling and prediction within the framework of Markov chains.

Next Word Prediction Next Word Prediction

Next Word Prediction

Next Word Prediction

🚀 Know More

Setup Guide ⚙️

Follow these steps to set up and run the project locally:

  1. Clone the Repository:

    git clone https://github.com/doubtcrack/Next-Word-Prediction.git
    cd Next-Word-Prediction
  2. Setup the markov_chain.txt file

  3. Starting the Project

    Begin the application with the following command in the directory:

    python markov_chain.py
    

    Accessing the Application

    You're all set!

Contribute and Support ❤️

🌟If you find this project useful and would like to contribute or show your support, we welcome your involvement. Feel free to submit pull requests, report issues, or share your feedback. Your contributions make this project even better!🌟

And also, please consider giving it a star ⭐ on GitHub! Your support and feedback are greatly appreciated as they help us improve and grow the platform. Thank you 💖

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Predict the future words efficiently with the "Next Word Prediction Using Markov Model" project. Built in Python and powered by the `msvcrt` module, this academic initiative explores the Markov chain model to anticipate the most likely next word based on a given sequence.

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