Why is data quality crucial in machine learning?

Why is data quality crucial in machine learning?

Data quality is essential in machine learning because it directly impacts the accuracy and reliability of the models. Poor quality data can lead to incorrect predictions and insights.

What are some common issues with data quality?

Common issues include missing values, duplicates, inconsistencies, and irrelevant features. These can skew results and reduce model performance.

How can I improve data quality for my ML projects?

You can improve data quality by implementing data cleaning processes, validating data sources, and using techniques like normalization and standardization.

What tools can help with data quality assessment?

Tools like Talend, Apache Nifi, and OpenRefine can help assess and improve data quality by providing functionalities for data cleaning and transformation.