When you think of political persuasion, you may think of the effortsthat political campaigns undertake to persuade you that their candidate is betterthan the other candidate. In truth, campaigns are less about persuading people to change their minds and more about persuading those who agree with you to actually go out and vote. Predictive analytics now plays a big role in this effort, but in 2004, it was a new arrival in the political toolbox.
Predictive Analytics Arrives in US Politics 

In January of 2004, candidates in the US presidential campaign were competing in the Iowa caucuses, part of a lengthy state-by-state primary campaign that culminates in the selection of the Republican and Democratic candidates for president. Among the Democrats, Howard Dean was leading in national polls.
The Iowa caucuses, however, are a complex and intensive process attracting only the most committed and interested voters. Those participating are not a representative sample of voters nationwide. Surveys of those planning to take part showed a close race between Dean and three other candidates, including John Kerry.
Kerry ended up winning by a surprisingly large margin, and the better than expected performance was due to his campaign’s innovative and successful use of predictive analytics to learn more about the likely actions of individual voters.
This allowed the campaign to target voters in such a way as to optimize performance in the caucuses. For example, once the model showed sufficient support in a precinct to win that precinct’s delegate to the caucus, money and time could be redirected to other precincts where the race was closer.

1. Overall, how well did the flyer do in moving voters in a Democratic direction? (Look at the target attribute among those who got the flyer, compared with those who did not.)
2. Explore the data to learn more about the relationships between the predictor attributes and MOVED_AD (visualization can be helpful). Which of the predictors seem to have good predictive potential? Show supporting charts and/or tables.
3. Partition the data using the partition attribute that is in the dataset, make decisions about predictor inclusion, and fit three predictive models accordingly. For each model, give sufficient detail about the method used, its parameters, and the predictors used, so that your results can be replicated.
4. Among your three models, choose the best one in terms of predictive power. Which one is it? Why did you choose it?
5. Using your chosen model, report the propensities for the first three records in the validation set.
6. Create a derived attribute that is the opposite of Flyer, say, Flyer-reversed.
Using your chosen model, re-score the holdout data using the Flyerreversed attribute as a predictor, instead of Flyer. Report the propensities for the first three records in the validation set.
7. For each record, uplift is computed based on the following difference:
P(success | Flyer = 1) − P(success | Flyer = 0)
Compute the uplift for each of the voters in the validation set,and report the uplift for the first three records.
8. If a campaign has the resources to mail the flyer only to 10% of the voters, what uplift threshold should be used?