This project uses Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on a corpus of documents. The aim of the project is to identify latent topics in the corpus and explore the distribution of topics across the documents.

• Python 3.x
• pip package manager

• Install all the dependencies given in requirements.txt file.

1. Clone the repository
2. Install the dependencies
3. Open topic_modeling.ipynb in Jupyter Notebook
4. Run the notebook to preprocess the data, train the LDA model, and generate topic visualizations.

• topic_modeling.ipynb: Jupyter Notebook containing the code for data preprocessing, model training, and visualization.
• data: Data used in this project is kaggle news category dataset.

• The top words for each topic are displayed using word clouds.

• Naman Mistry(https://github.com/namanmistry)

• https://www.kaggle.com/datasets/rmisra/news-category-dataset