How To Split Data Effectively for Your Data Science Project

Data is one of the most important resources for any data science project. But what good is abundant data if you can’t use it effectively? After all, your success as a data scientist hinges on how adeptly you can manipulate and analyze data to produce actionable insights from it.

One of the common steps in your data science project after collecting the data is to split the data into train and test. Although this step might seem simple, if it is done in an effective way, it may affect your results and lead to unrealistic models…