This project demonstrates a basic implementation of a next word prediction model using LSTM (Long Short-Term Memory) neural networks. The model is trained on a text corpus, and given a sequence of words, it predicts the most likely next word.

• next_word_prediction.ipynb: Jupyter Notebook containing the code.
• 1661-0.txt: Sample text file used for training (Project Gutenberg's "The Adventures of Sherlock Holmes" by Arthur Conan Doyle).
• keras_next_word_model.h5: Trained LSTM model saved in the Hierarchical Data Format (HDF5).
• history.p: Pickle file storing training history for visualization.

1. Open the Jupyter Notebook next_word_prediction.ipynb.
2. Execute each cell sequentially.
3. The model will be trained on the provided text file (1661-0.txt).
4. The notebook demonstrates text preprocessing, model creation, training, and prediction.

• numpy
• nltk
• keras (TensorFlow backend)
• matplotlib

The training process includes two epochs, and the model's performance can be observed through the accuracy and loss metrics.

Connect with me on LinkedIn:

• The provided text file (1661-0.txt) is a sample, and you can replace it with your own corpus for training.
• The model is saved as keras_next_word_model.h5 for future use.
• Experiment with different sequences to observe varied predictions.

Feel free to enhance and experiment with the model for more accurate predictions.