Introduction: Why Inconsistent Data is a Hidden Risk In the world of data analytics, data quality is everything. Imagine you’re making critical business decisions based on data, but that data has missing values or inconsistencies. The result? Poor decisions, wasted resources, and missed opportunities. Handling missing and inconsistent data isn’t just a technical...

Missing data is one of the most common challenges faced in data science projects. Whether it comes from human error, system glitches, or incomplete data sources, missing values can significantly impact the quality and accuracy of your models. Addressing them thoughtfully is critical for producing reliable insights and making informed decisions. If you're looking to master real-world dataset...