Hillary Clinton was a sure favorite to win the 2016 presidential election. Despite losing the popular vote, however, Donald Trump squeezed enough electoral college votes to win the presidency and shock the U.S. political system giving the Republicans control of the house, senate, and presidency.

The 2016 election was not the first time that presidential polls have been inaccurate. In fact, the average presidential polling error was 2.0 between the 1968 and 2012 elections according to FiveThirtyEight, a data journalism website, meaning that Trump was indeed a normal polling error behind Clinton.

Democrats lose in key battleground states

Even the most conservative forecasts gave Clinton over a 70% chance of winning the general election ranging from sites such as FiveThirtyEight (71.4%) to the New York Times (85%). The majority of models had Clinton winning divided states including Florida, North Carolina, Pennsylvania, and Virginia. As we know, Clinton only managed to win Virginia taking major defeats in the other three states.

In this analysis, we will take a closer look at a Siena Poll sampling voters across the former three battleground states: Pennsylvania, North Carolina, and Florida. The Democrats had hoped that Clinton would take at least two of these states and never truly thought that she would lose all three.

We will use a decision tree, random forest, and naive Bayes models to predict the probability that Clinton takes the majority of votes in these three states. For reference, Naïve Bayes is a classifier (based on Bayes Theorem). The algorithm assumes that each observation (in our case voters) is independent from each other. Random forest and decision trees are an ensemble learning method for classification and regression.

As predictors, we used the voter’s gender, vote likelihood (0-10), party affiliation, educational background, race, and favorability of both Democratic and Republican parties to create a probability that the voter would cast a ballot for Clinton or Trump.

On average, Clinton held a 54% to 46% edge over Trump across Pennsylvania, North Carolina, and Florida. Among all female supporters Clinton’s vote probability jumped to 58.7% but fell to 48.6% among all male voters.

Gender White Black Male 38.3 90.9 Female 46.7 97.5

Figure 1: Percent of people that voted for Hillary Clinton by gender and race.

Source: Siena Poll (Siena College, New York)

As the above chart shows, Clinton’s support takes a big hit among white male voters dropping to 38.3%. Compared to their male counterparts, white females were more likely to vote for Clinton (46.7%) but still had a higher probability of backing Trump according to the poll.

On the flip side, the vast majority of democratic black males supported Clinton (90.9%). Clinton’s vote count was even higher among black females (97.5%). However, the poll also suggested that blacks were on average less likely to vote than whites, which likely hurt Clinton’s chances in these states.

Figure 2: Vote likelihood in the 2016 Presidential Election (0-10 scale). White voters were more likely to vote than blacks or Asians.

Source: Siena Poll

Despite Trump’s white voter base in Pennsylvania, North Carolina, and Florida, our three models gave Clinton an average 52.5% probability of taking the majority vote in these states. On paper this forecast seemed to be within reason the day before the election but soon fell apart as battleground states swung to Trump into the late night. With the looming 2018 midterms, Democrats are in desperate need of gaining seats in the house and senate to combat Trump’s policies and create initiatives that prop-up healthcare, affordable housing, and environmental protection going into 2019 and 2020 before the next general election.

Github: https://github.com/Fremont28/battleground_politics-

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