Complete the task described in TASK.md. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it.
Careful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis.
Data deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification.
Simple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure.
Uncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words.
Reproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation.
Finally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. When a task cannot be completed as specified, saying so, and saying why, is the correct outcome.