In this repository, I explore and implement a next word prediction model using two prominent techniques: Long Short-Term Memory (LSTM) and Support Vector Machine (SVM). Through rigorous experimentation, I have determined that the LSTM model outperforms SVM in predicting the next word in a given sequence of text.
Key Features:
LSTM Dominance: Dive into the detailed analysis showcasing the superior performance of the LSTM model in next word prediction tasks. The code demonstrates the implementation of LSTM networks for sequence modeling and prediction.
SVM Benchmark: While SVM is a powerful machine learning algorithm, compare its performance against LSTM in the specific context of next word prediction. Understand the nuances and limitations of both approaches.
Web Scraping for Textual Data: Explore the methods employed for web scraping to gather diverse and rich textual data. The scraped data serves as the foundation for training and evaluating the predictive models.
Code Structure: Navigate through a well-organized codebase with clear documentation. The structure allows for easy understanding and modification of the models and data processing steps.
Whether you are interested in natural language processing, machine learning, or are simply curious about next word prediction, this repository provides a comprehensive exploration of the topic. Feel free to fork, experiment, and contribute to further advancements in language modeling.
Happy Coding!