Paper Contents
Abstract
The explosion of data in healthcare, finance, and social media has created a huge need for tools that can analyze information quickly and at scale. Machine learning (ML) plays a key role here, helping us spot patterns, make predictions, and guide smarter decisions. Python has become the go-to language for ML because its simple and backed by powerful libraries like scikit-learn, TensorFlow, and PyTorch.But Python on its own struggles with todays massive datasets since it typically runs on a single machine. Thats where big data platforms like Hadoop and Apache Spark come in. Hadoop offers reliable storage and batch processing, while Spark speeds things up with in-memory computing and specialized tools for ML and streaming. Combining Python with these platforms unlocks both flexibility and scalability.Approaches like PySpark, Hadoop Streaming, or distributed deep learning frameworks (TensorFlow on Spark, Horovod) make this integration possible. Studies show Spark can cut training times by up to fivefold compared to standalone Python. This has real-world impact: from disease prediction in healthcare, to fraud detection in finance, to analyzing billions of social media posts. Looking ahead, cloud-native ML, Python frameworks like Ray and Dask, and federated learning will push these integrations even further.
Copyright
Copyright © 2025 Hariprakash K. This is an open access article distributed under the Creative Commons Attribution License.
