A company collected new data and then created a correlation matrix, computed summary statistics, and produced visualizations to understand the data. Which stage of the machine learning workflow is this?

This scenario describes Exploratory Data Analysis (EDA). EDA involves using statistical summaries and visualizations (like correlation matrices and summary statistics) to understand the dataset's characteristics, identify patterns, detect anomalies, and test hypotheses. This stage helps in gaining insights before formal modeling. Data preprocessing involves cleaning, transforming, and preparing data for modeling, such as handling missing values or scaling features. Feature engineering is the process of creating new features from existing ones to improve model performance. Hyperparameter tuning is the process of optimizing the model's configuration settings to achieve better performance after the model has been selected.