human
Here is a list of available APIs:
corr(data, c1: str, c2: str) -> correlation between columns c1 and c2
smart_mapping(data, c: str, features: List[str]) -> Compute smart mapping on the target column c. Only columns in the features list will be considered. This creates a 3D plot showing the most influential explanatory features on the target column.
anomaly_detection(data, c: str, threshold: float, features: List[str]) -> Run anomaly detection on target column c. Only columns in the features list will be considered in identifying anomalies. This also takes in a threshold percentage value to determine what proportion of the data should be identified as anomalies.
clustering(data, n_clusters: Union[int, Literal['auto']], features: List[str]) -> Perform clustering on the provided data using the specified number of clusters. 
PCA(data, n_components: int, features: List[str]) -> Perform PCA on the provided data. Only columns in the features list will be considered in performing PCA. 

The provided dataset has the following columns:
reportedCity, dateOfLoss,elevatedBuildingIndicator,floodZone,latitude,longitude,lowestFloodElevation,amountPaidOnBuildingClaim,amountPaidOnContentsClaim,yearofLoss,reportedZipcode,id

The answer must be in this format. Here's an example:
[{
"question": "What is the correlation between c1 and c2?", "justification": "Understanding the correlation between c1 and c2 can provide insights...", "routine": "corr", "params": {"data": "data", "c1": "c1", "c2": "c2"}
}]

Generate 6 questions that can be answered with the provided routines and provide the parameters for calling them. The answer must use the above JSON format.
Here's the output JSON:
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open