Next Word Predictor: Shakespeare's Hamlet with LSTM

This project utilizes a Long Short-Term Memory (LSTM) model to predict the next word in a sequence from Shakespeare's Hamlet. The model is trained on the complete text of Hamlet and can generate contextually relevant word predictions based on a given input.

📁 Project Structure

• code.ipynb: Jupyter Notebook containing the full implementation of the LSTM model, including data preprocessing, model training, and evaluation.
• shakespeare-hamlet.txt: Raw text file containing the complete text of Shakespeare's Hamlet, used for training the model.
• tokenizer.pkl: Serialized tokenizer object used to convert text into sequences of integers for model input.
• nextword_model.h5: Saved Keras model containing the trained LSTM network for word prediction.
• Training_Loss_And_Accuracy_Plot.png: Visual plot showing the training loss and accuracy over epochs.

⚙️ Requirements

To set up the environment for this project, install the necessary dependencies using the provided requirements.txt file.

🧑‍💻 Usage

1. Clone the repository:

   git clone https://github.com/TulsiBasetti/Next_Word_Predictor-_Shakeshpeare_Hamlet_LSTM.git
   cd Next_Word_Predictor-_Shakeshpeare_Hamlet_LSTM

2. Install the required packages:

   pip install -r requirements.txt

3. Open and run the Jupyter Notebook:

   jupyter notebook code.ipynb
   

📈 Training and Evaluation

The model's performance is visualized through the Training_Loss_And_Accuracy_Plot.png, which shows the loss and accuracy metrics over the training epochs.