Why is data cleaning important in data analysis?

Why is data cleaning important in data analysis?

Data cleaning is crucial because it ensures the accuracy and quality of your data, leading to more reliable insights.

What are some common data cleaning techniques?

Common techniques include removing duplicates, handling missing values, and correcting inconsistencies in data formats.

How does data cleaning affect machine learning models?

Data cleaning improves the performance of machine learning models by providing high-quality data, which leads to better predictions.

Can you give an example of a data cleaning process?

Sure! An example process might involve identifying and removing outliers, filling in missing values, and standardizing data formats.