how would a data scientist analyze taxi service trip pricing? how could they use a scatterplot to see clusters and outlines?

Answer 1

A data scientist could analyze taxi service trip pricing by collecting and examining data such as the distance of the trip, the time of day, the location of pick-up and drop-off, any additional charges such as tolls or surge pricing, and the final fare charged to the customer. They could then use statistical analysis techniques such as regression analysis or clustering algorithms to identify patterns, correlations, or clusters within the data that could help predict or explain pricing variations.

To use a scatterplot to see clusters and outlines in the data, the data scientist could create a scatterplot with the x-axis representing one variable (e.g., trip distance) and the y-axis representing another variable (e.g., trip fare). By plotting each data point on the scatterplot, clusters or patterns may emerge that indicate relationships between the variables. For example, the data points may form distinct clusters based on different pricing tiers or may show outliers that represent unusually high or low fares. By visually examining the scatterplot, the data scientist can gain insights into the underlying structure of the data and identify potential factors that influence trip pricing.