Before Adjusting a Fee Rate, Identify Which Transactions Will Be Affected

Thu, 22 Oct 2026 00:00:00 +0000

If a payment fee rate is reduced from 14 basis points to 1 basis point, how much can a merchant save in a month?

While this appears to be a multiplication problem, the actual challenge lies before the multiplication: determining which transactions are affected. This analysis recalculates two “hard” problems from DABstep, with a focus on defining the scope and performing independent verification.

Hotel Data in Practice: Why Brand Rankings Change

Tue, 20 Oct 2026 00:00:00 +0000

Why doesn’t a brand with an average 5-star rating always rank first on the hotel brand leaderboard?

This walkthrough follows the complete pipeline using public benchmark questions: Data → Question → Solution → Validation. The focus is not merely on calculating the answer, but on understanding how qualification thresholds and denominator selection alter conclusions.

Data and Questions

The dataset is InfiAgent-DABench, which evaluates the ability to truly execute analysis and deliver results starting from CSV files, natural language questions, and constraints. This fixed question set contains 257 questions referencing 52 CSV files; here, we use only hotel_data.csv, comprising 1,057 rows and 9 columns, covering 7 cities and 103 non-null brands, with each row representing a hotel record.

Data Analysis Practice on DataAgentX

Before Adjusting a Fee Rate, Identify Which Transactions Will Be Affected

Hotel Data in Practice: Why Brand Rankings Change

Data and Questions