Case: Analysis of Voting Behavior

The third case study we deploy regards a data set of voting records in the US House of Representatives. Given 16 key votes for each one of the 435 members of the House, we focused on extracting patterns explaining the voting behavior, according to the party each one of the representatives belongs to.

The key votes (attributes) are presented in Table 1 that follows.

Table 1: Key votes titles (attibutes)

Table 2 presents further information on the target variable, namely the party each of the Representatives belongs to.

Table 2: Target attribute (part) details

Table 3 provides the votes of most informational value for judging the party of the Representative, as occurred by extensive experimentation with proper filtering algorithms.

Table 3: Key votes (attributes) of importance

With inputs as such, a bunch of advanced machine learning algorithms were run to finally return some critical insights on how voting turned out to be related with the party. Some of the rules extracted are presented below, while you may find an extended set of them in the full analysis report which is available for download at the end of this post.

Rule 01: If crime=yes & religious-groups-in-schools=no & aid-to-Nicaraguan-contras=no then vote=democrat

For example, Rule 01 introduces that Representatives who voted for the crime resolution, but were against to the resolutions about religious groups in schools and the aid to the Nicaraguan contras are expected to be Democrats, with 97% rate of success.

Rule 02: If immigration=yes & adoption-of-the-budget-resolution=no then vote=republican

Rule 02 suggests with a 76% of accuracy that Representatives who voted for the immigration resolution but against the adoption of the budget belong to the republican party.

Rule 03: If physician-fee-freeze=yes & adoption-of-the-budget-resolution=no & El-Salvador-aid=yes then vote=republican

Finally, Rule 03 deploys that those who voted for the physician fee freeze and El Salvador aid regulations, while were against the adoption of the budget resolution, were republicans with an accuracy of 87%.

The full report, which you may download here (.pdf), contributes more than 30 such rules and patterns, fully representative of our services. And, please take into account that, focusing on the party of each representative was one of the options, rules and patterns extracted could also focus on the expected votes on a specific resolution, taking the voter’s party into consideration, it goes further.

Consider also the power of such an analysis when the given data set regards, for example, voters and their characteristics. If you are used to think some bars and pies, relating one attribute with the vote, as razor-sharp survey analysis, get ready to be impressed with the extent and value of the results and insights you’ll gain from a mineknowledge analysis.

Still considering it? Learn more about our services¬†here, we’re awaiting for your data, starting from now.

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