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.
- Tokenizes sentences into words
- Generates bigrams and trigrams
- Predicts the next word using probability distributions
- Implements machine learning techniques for text prediction
Ensure you have the following dependencies installed:
pip install nltk numpy pandas
- Import necessary libraries such as
nltk
,numpy
, andpandas
. - Load the dataset containing textual data.
- The dataset is structured as a list of sentences, where each sentence is a list of words.
-
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"
- Use probability distributions to analyze n-grams and predict the most likely next word.
- Evaluate the model based on accuracy, perplexity, and fluency.
- Clone this repository:
git clone <repository_url> cd Next-Word-Prediction
- Install dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook:
jupyter notebook Next_Word_Prediction.ipynb
- Integrate Transformers (e.g., GPT-2) for more advanced predictions.
- Implement GPTTokenizer for better text preprocessing.
- Improve accuracy using deep learning techniques.
Feel free to fork this repository and improve the model. Contributions are welcome!
This project is licensed under the MIT License.