The task in this case is to develop an uplift model that predicts the uplift for each voter. Uplift is defined as the increase in propensity to move one's opinion in a Democratic direction. First, review the variables in Voter-Persuasion.csv and understand which data source they are probably coming from. Then, answer the following questions and perform the tasks indicated:

Overall, how well did the flyer do in moving voters in a Democratic direction? (Look at the target variable among those who got the flyer, compared to those who did not.)
Explore the data to learn more about the relationships between the predictor variables and MOVED_AD using data visualization. Which of the predictors seem to have good predictive potential? Show supporting charts and/or tables.
Partition the data using the partition variable 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.
Among your three models, choose the best one in terms of predictive power. Which one is it? Why did you choose it?
Using your chosen model, report the propensities for the first three records in the validation set.
Generate a derived variable that is the opposite of Flyer. Call it Flyer-reversed. Using your chosen model, re-score the validation data using the Flyer-reversed variable as a predictor, instead of Flyer. Report the propensities for the first three records in the validation set.
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.

If a campaign has the resources to mail the flyer only to 10% of the voters, what uplift cutoff should be used?
Dataset can be found here: https://buckeyemailosu-my.sharepoint.com/:x:/g/personal/paxton_80_osu_edu/ESuUZ-88UtxOt_Rayoq9N3ABnK8abWKo2-miyrsAFGQTUQ?e=iCCcqr