What are the best practices for cleaning messy data?

Cleaning messy data is an essential part of any data analysis process. Without proper data cleaning, insights drawn can be misleading, resulting in incorrect conclusions and poor decision-making. The first step is to understand the nature of the dataset and identify inconsistencies. These may include missing values, duplicate entries, inconsistent formats, and outlines. Addressing these issues begins with a thorough data audit, which helps to pinpoint areas requiring correction. https://www.sevenmentor.com/da....ta-science-course-in

One of the core practices in data cleaning is dealing with missing data. Depending on the context and the extent of missing values, various strategies can be employed, such as imputing missing values ​​using statistical methods, or simply removing incomplete records if their absence does not compromise the overall dataset. Data normalization is another key practice, ensuring that the data maintains a consistent format, which is crucial for downstream analysis. This involves standardizing units of measurement, aligning date formats, and unifying categorical data entries.