What are the differences between EDA tools and machine learning?

What are the differences between EDA tools and machine learning?

EDA tools, or Exploratory Data Analysis tools, are used to analyze data sets to summarize their main characteristics, often using visual methods. Machine learning, on the other hand, involves algorithms that allow computers to learn from and make predictions based on data.

Can you give examples of EDA tools?

Sure! Some popular EDA tools include Pandas, Matplotlib, Seaborn, and Tableau. These tools help in visualizing and understanding data.

How do EDA tools support machine learning?

EDA tools help in the preprocessing stage of machine learning by identifying patterns, detecting outliers, and understanding the data distribution, which is crucial for building effective models.

Is it necessary to use EDA before applying machine learning?

While it's not strictly necessary, performing EDA is highly recommended as it can significantly improve the quality of your machine learning models by providing insights into the data.