ArXiv Dataset Analysis

This project aims to analyze the ArXiv dataset to extract insights and trends in scientific papers. It involves preprocessing the data, applying dimensionality reduction techniques, and using machine learning algorithms for classification and clustering.

Project Steps

1. Data Acquisition: Obtain the ArXiv dataset, which includes metadata and abstracts of scientific papers.
2. Data Preprocessing: Clean the data by removing irrelevant information and performing necessary text preprocessing tasks.
3. Dimensionality Reduction: Reduce the dimensionality of the feature space using techniques like LDA.
4. Classification and Clustering: Apply machine learning algorithms to categorize the papers into subject areas and group them based on similarities.
5. Data Visualization: Visualize the results in wordcloud to present the trends and insights obtained from the analysis.

Dependencies

• Python 3.x
• Pandas
• NumPy
• scikit-learn
• Matplotlib

Getting Started

1. Clone the project repository: git clone https://github.com/your-username/arxiv-dataset-analysis.git
2. Obtain the ArXiv dataset and place it in the appropriate directory.
3. Run the provided scripts or notebooks to execute the different steps of the project.

Conclusion

This project provides a basic framework for analyzing the ArXiv dataset and extracting trends in scientific papers. It demonstrates the application of data preprocessing, dimensionality reduction, and machine learning techniques to gain insights from the data.

For detailed information and code implementation, please refer to the provided scripts and documentation.