A data analyst at an e-commerce company needs to group customers into distinct behavioral segments based on features such as annual spending, visit frequency, and average order value. The dataset does not contain any historical segment labels or predefined target categories. Which model type should the analyst specify in the CREATE MODEL statement using BigQuery ML?

K-means clustering (`KMEANS`): ### **What K-means clustering is:** **K-means clustering** is an **unsupervised machine learning algorithm** built directly into BigQuery ML. It partitions a dataset into a specified number ($k$) of distinct, non-overlapping clusters or segments based on the mathematical similarity and distance between feature values of individual data points. ### **How it meets the requirements:** - **Unsupervised segmentation (✓):** The scenario explicitly states that the customer dataset does not have predefined target categories or historical labels. K-means is specifically designed for unlabeled data exploration and pattern discovery. - **Customer profiling (✓):** K-means identifies natural groupings based on multidimensional feature vectors, such as visit frequency, annual spending, and order value. - **SQL native execution (✓):** In BigQuery ML, you define `MODEL_TYPE='KMEANS'` inside the `OPTIONS` clause of a `CREATE MODEL` SQL statement without needing separate data export or external training infrastructure. ### **Key advantages and capabilities:** - **No labeled data required:** Training does not require training/evaluation data splits or predefined target labels (`input_label_cols`). - **Automatic feature preprocessing:** BigQuery ML automatically standardizes numerical features and handles one-hot encoding for categorical variables during model training. - **Built-in cluster inspection:** Once trained, you can inspect cluster centroids and analyze customer segment attributes using functions like `ML.CLUSTERS` and `ML.PREDICT`. - **Scalable in-place training:** Operates directly over large BigQuery tables using standard SQL queries and managed cloud compute resources. ### **Why this is optimal:** K-means clustering is the industry-standard algorithm within BigQuery ML for data segmentation and customer grouping tasks where ground-truth outcome labels are unavailable.