This project focuses on building a deep learning model that predicts the next possible word in a given text sequence. It uses a Long Short-Term Memory (LSTM) network to understand the sequence patterns and context in text data.
Overview
The model learns language structure by training on a collection of text data. Once trained, it can generate text predictions similar to how a human would complete a sentence. This project demonstrates the use of Natural Language Processing (NLP) and Recurrent Neural Networks (RNN) in text generation tasks.
Technologies Used
Python, TensorFlow / Keras, NumPy, Pandas, Matplotlib, Natural Language Processing (NLP)
How to Run
Clone the repository: git clone https://github.com/maliparag11/Next-Word-Prediction.git
Navigate to the project folder: cd Next-Word-Prediction
Open and run the notebook: jupyter notebook Next_Word_Prediction.ipynb
Results: The model successfully predicts the next word in a given sentence with reasonable accuracy. You can experiment with different datasets or adjust hyperparameters to improve the prediction quality.
Future Scope: Use a larger and more diverse dataset. Fine-tune using pre-trained language models like GPT-2 or BERT. Develop a web interface for real-time text prediction.