How would a data scientist use scatterplots to identify clusters and outliers @ disneyland
To identify clusters and outliers at Disneyland, a data scientist could use scatterplots as follows:
1. Clustering: The data scientist could create scatterplots with two relevant variables related to visitor behavior at Disneyland, such as time spent in the park and money spent on tickets and merchandise. By analyzing the scatterplot, the data scientist could identify groups of visitors who exhibit similar patterns, indicating potential clusters of visitor behavior.
2. Outliers: The data scientist could look for data points that deviate significantly from the general pattern in the scatterplot. For example, an outlier could be a visitor who spends an unusually large amount of money on merchandise compared to the average visitor. By identifying outliers, the data scientist can further investigate these unusual data points to understand the reasons behind their behavior.
Overall, scatterplots can help a data scientist visually identify clusters and outliers in the data related to visitor behavior at Disneyland, providing valuable insights for optimizing operations and enhancing the visitor experience.