Enhancing the Computing Efficiency Through Data Compression for Machine Learning

As machine learning data sizes continue to rise, this work offers a unique solution to improve computing efficiency by means of data compression. This starts by going over the problem’s history and emphasizing the difficulties brought on by growing data complexity. Recurrent Neural Networks (RNNs) are used in this technique in conjunction with the BIRCH Algorithm to compress data. Which is show via extensive experimentation that this method is efficient in decreasing data size greatly while maintaining important information. The findings show significant gains in overall performance, resource efficiency, and model training speed. By tackling important computational issues, this research helps to address contemporary machine learning applications. Finally, The results highlights the significance of effective data compression methods for managing growing datasets, opening the door for improvements in computing effectiveness and model performance.