Analysis of US Presidential

Elections, 2016

Table of Contents

Overview of Dataset Objectives Tools Used Methodology Analysis & Findings Assumptions Prediction Conclusions Bibliography

Overview of Dataset

Dataset was obtained from Kaggle website The dataset contains relevant data for the 2016 US Presidential

Elections, including results of primary elections The dataset consisted of 4 files in csv and zip format, namely,

County_facts- demographic data on counties from US census County_facts_dictionary- description of columns of County_facts Primary_results- File containing data about votes and number of

votes received by each candidate in different counties.

Objectives Understanding the primary elections and key terms Number of candidates who took part in the primary elections from each party Most popular candidate for each party by different state and with respect to

different types of people Differentiation in number of votes with respect to party and candidate by each

state Analysing the Non-Swing states (Looking previous 5 election year trends) Understanding the general elections and key terms Calculating the number of electoral votes for final presidential nominees Prediction of the next President of the United States of America Predictions and models Popularity of each candidate on the basis of twitter sentiment analysis Performance comparison of the various tools utilized

Tools Used

RStudio MS-Excel SAS SQL with RStudio Tableau Anaconda

Methodology

Obtain dataset from Kaggle.com

Explore the data to find what its all about

Understand the US primary elections Defining objectives

Modifying, cleaning and transformation of Data in RStudio

Writing the modified dataset into a csv file

Carrying out different type of analysis on the modified data to draw insights using different tools and visualizations

Understand the US general elections

Make certain Assumptions in order to predict the next president

Do qualitative & quantitative analysis keeping in mind the assumptions made to find out the next president

Supporting our answer with the help of certain mathematical models

Twitter Sentiment Analysis to find the popularity of final presidential nominees

Comparison of performance of tools used for analysis

Drawing conclusions

Analysis & Findings

Understanding the US Primary Election and key terms

Key Terms National Conventions Primary

Closed primary Open primary New Hampshire Primary

Caucus Iowa Caucuses

Delegates Pledged Delegates Super Delegates

Number of candidates who took part in the primary elections from each partyBased on dataset, a total number of 14 candidates together from both the parties took part in the primary elections, who are as follows:

Democratic Party

Hillary Clinton

Bernie Sanders

Martin O’ Malley Re

publ

ican

Pa

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Ben Carson

Carly Fiorina

Chris Christie

Donald Trump

Jeb Bush

John Kasich

Marco Rubio

Mike Huckabee

Rand Paul

Ted Cruz

Rick Santorum

Most popular candidate by each party

Republican Party

Democratic Party

Top most popular candidates for each party by different types of people

Most popular candidate from both the parties, by different types of person

Non-Swing states, looking at the previous election

2012 ELECTIONS TO SEE THE NON-SWING STATES AND COMPARE IT WITH THIS YEAR ELECTIONS

Understanding the general election and key terms

Key terms Electoral College Electors Swing states

Calculating the number of electors Number of electors differ for each state

The number of electors are calculated on the basis of number of districts in each state along with the senate members, which are two for all states

The more the number of districts in each state, the more the number of electors

Electors are the persons who choose the president of the United States

The electors vote in the favour of the nominee who was popular across each state

California 53

districts2 senate members

55 electors

Prediction of the next president of United States

As the data pertaining to general elections was not available certain presumptions were made, which are as follows: The conditions and the number of votes to be cast during the upcoming

general elections would be similar to the conditions during primary elections Therefore, the same data of primary elections was analysed to draw

prediction insights Qualitative analysis and current affairs were used to make predictions Two different predictions were made, one on the basis of party and other on

the basis of final presidential nominee The predictions are supported by different mathematical models defined by

distinguished professors in their fields Assumption on the division of votes of the candidates who quit or suspended

their campaign

Predictions and models

On the basis of party, the most number of electoral votes went to republican party, leading to the win of Donald Trump

If we take only the candidates solely, and forget the parties then there can be two phases as follows, 1st Phase- Winner Hillary Clinton 2nd Phase- Winner Donald Trump

Mathematical models to support our answer include different econometric models such as, DeSart Model (Jay DeSart), Fair Model (Ray Fair), Primary Model (Helmut Norpoth), and Electoral Cycle Model (Helmut Norpoth) among others.

Twitter sentiment analysis

All candidates Hillary Clinton Donald Trump

Performance of various tools utilized

We have carried out similar analysis on both R and Python and based on our data and skills we came to the following conclusions:

Parameter R PythonNumber of lines of code (average) 145 85

RAM Usage 88% 66%Average Processing Time (minutes) 8-10 4-7

Ease of coding Easy ModerateNumber of Packages used 22-25 4-6

Conclusion As per our analysis the prediction is mainly dependent on the

casting of votes in swing states along with division of votes of Ted Cruz of Republican party as he has declined to endorse his republican counterpart Donald Trump.