Next Word Prediction using NLTK

Project Overview

This project focuses on building a Next Word Prediction model using NLTK and machine learning techniques. The model processes text data, constructs n-grams (bigrams and trigrams), and predicts the most probable next word based on context.

Features

• Tokenizes sentences into words
• Generates bigrams and trigrams
• Predicts the next word using probability distributions
• Implements machine learning techniques for text prediction

Requirements

Ensure you have the following dependencies installed:

pip install nltk numpy pandas

Step-by-Step Process

1. Basic Setup

• Import necessary libraries such as nltk, numpy, and pandas.
• Load the dataset containing textual data.

2. Loading the Dataset

• The dataset is structured as a list of sentences, where each sentence is a list of words.

3. Creating N-grams

• Generate unigrams, bigrams, and trigrams from the dataset.

  Example:

  • Sentence: "This is a Data Science Course"
  • Bigrams: "This is", "is a", "a Data", "Data Science", "Science Course"
  • Trigrams: "This is a", "is a Data", "a Data Science", "Data Science Course"

4. Building the Prediction Model

• Use probability distributions to analyze n-grams and predict the most likely next word.

5. Model Evaluation

• Evaluate the model based on accuracy, perplexity, and fluency.

How to Run the Project

1. Clone this repository:

   git clone <repository_url>
   cd Next-Word-Prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook:

   jupyter notebook Next_Word_Prediction.ipynb

Future Enhancements

• Integrate Transformers (e.g., GPT-2) for more advanced predictions.
• Implement GPTTokenizer for better text preprocessing.
• Improve accuracy using deep learning techniques.

Contributing

Feel free to fork this repository and improve the model. Contributions are welcome!

License

This project is licensed under the MIT License.