In the real world, the size of datasets is huge which comes as a challenge for every data science programmer. Working on it takes a lot of time, so there is a need for a technique that can increase the algorithm’s speed. Most of us are familiar with the term parallelization that allows for the distribution of work across all available CPU cores. Python offers two built-in libraries for optimization of this process, multiprocessing and multithreading.

**Multi-Processing: **Multiprocessing refers to the ability of a system to support more than one processor at the same time. It works in parallel and doesn’t share memory resources.

**Threading: **Threads are components of a process, which can run sequentially. Memory is shared between the CPU core.

In this article, we will discuss how much time it takes to solve a problem using a traditional approach. Further, we will research parallelization techniques like multiprocessing and multithreading that can reduce the training time of large dataset for a data science problem. While dealing with larger implementations of machine learning, the time complexity is a major concern. Through this article, we will learn how to address the concern of time complexity. n.

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