A Guide to Real-World Data Collection for Machine Learning

Whether you’re brand new to data science or the Chief Data Scientist at a large organization, you’ve probably played with perfectly crafted data sets to solve toy machine learning problems. Maybe you’ve used K-Means clustering to predict flower species in the <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set" rel="noopener ugc nofollow" target="_blank">Iris</a> data set. Or maybe you’ve tried out a logistic regression model to predict which passengers survived the <a href="https://www.kaggle.com/competitions/titanic" rel="noopener ugc nofollow" target="_blank">Titanic</a> voyage. While these data sets are great for practicing the basics of machine learning, they don’t mirror the real-world data you’ll come across on the job. In reality, your data can have quality issues, might not be perfect for the task at hand, or may not exist yet. This means Data Scientists often need to roll up their sleeves and gather data — a challenge often not covered in today’s data science curriculum. For new Data Scientists, collecting extensive amounts of data before diving into the problem at hand can feel extremely daunting since this stage lays the foundation for the entire machine learning project. However, with the right strategies, this process can become much more manageable. Throughout my 10+ years as a Data Scientist, I’ve encountered a wide variety of data collection strategies, and in this article, I’ll share five of my favorite tips to optimize your data collection process and set you on the path to creating a successful machine learning product. <a href="https://towardsdatascience.com/a-guide-to-real-world-data-collection-for-machine-learning-a232c436ac19">Visit Now</a>