Dataset

Techniques on what to clean and how. Before fitting a machine learning or statistical model, we always have to clean the data. No models create meaningful results with messy data. Data cleaning or cleansing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. What a long definition! It is certainly not fun and very time-consuming. Source: kdnuggets.com To make it easier, we created this new complete step-by-step guide in Python. You’ll learn…