EPILOGUE: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2Fa5eefdb31428bd5ce99cdf76fa484a63%2Fmap.jpg?generation=1733007717460285&alt=media" alt="">

FINAL UPDATE: It's election night, and the results are coming in. The final update includes the latest poll data from 538, which is from two days ago. Thanks all for following the development of this dataset.

OCTOBER UPDATE: The past month has been typical of the final weeks before the election - rallies, interviews, and advertising. This update includes a transcript of the VP debate between Walz and Vance, and the latest poll summaries.

SEPTEMBER UPDATE: Trump and Harris had their first debate. This update includes the transcript and recent poll results. Also, there was a second attempt to kill former President Trump! No shots fired though on this one. You'll see aerial diagrams of both attempts in the dataset.

https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftse4.mm.bing.net%2Fth%3Fid%3DOIF.edyLiGntLZbwC9fBkg8TsQ%26pid%3DApi&f=1&ipt=a1096b37cf3eced7dff70d362a2c76f8876422f53c47856cadf09f9fa18b367e&ipo=images" alt="debate">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2F0ecedf88421c303e0112734a30de9e29%2Frouth.jpg?generation=1726701011377683&alt=media">

LATE AUGUST UPDATE: The Democratic Party replaced President Biden with his VP, Kamala Harris. It's now Trump v Harris along with one nominee from each of the smaller factions.

https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fmedia.cnn.com%2Fapi%2Fv1%2Fimages%2Fstellar%2Fprod%2F240122181719-trump-kamala-vpx-split-2.jpg%3Fc%3D16x9%26q%3Dw_850%2Cc_fill&f=1&nofb=1&ipt=984b6cf55cf55e1539003ca1c1beaa359625f6e5b08b511b3b018c9d2c959ae5&ipo=imagesg">

https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Chase_Oliver%2C_Jill_Stein_%26_Randall_Terry_%2853866448015%29.jpg/1280px-Chase_Oliver%2C_Jill_Stein_%26_Randall_Terry_%2853866448015%29.jpg">

AUGUST UPDATE: This election season just gets crazier and crazier. You'll see new data related to the assassination attempt on former President Trump. There are transcripts of Secret Service hearings and an annotated image of the rally area.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2F75dd20a00c2ac6d81c6d6e1f83cbd941%2Fdonald-trump-rally-shooting-2024-113.webp?generation=1722800392288670&alt=media">

JULY UPDATE: Added the transcript of the debate between Trump and Biden.

MAY UPDATE: Added some new polls and also a meta-poll assessing the quality of select pollsters.

APRIL UPDATE : The dataset now contains approval ratings for sitting presidents, which includes Biden and Trump.

MARCH UPDATE: As of last week, the presumptive nominees are Joe Biden(D) and Donald Trump(R). They also ran against each other in 2020. Robert F Kennedy Jr is running as an independent.

Presidential elections occur quadrennially in years evenly divisible by 4, on the first Tuesday after November 1. Presidential candidates from the major political parties usually declare their intentions to run as early as the spring of the previous calendar year before the election. The two major parties each nominate one candidate through a process of primary elections and nominating conventions during the election year. (source: Wikipedia)

This dataset contains data on candidates, primary/caucus results, polls, and debate transcripts. Updates and additional data will be added as the landscape develops.

Note: Version 3 of this dataset contains previous coverage of the 2022 Congressional Mid-term Elections.

Context

This data comes from the Associated Press - the AP has been tracking vote counts in US elections since 1848 and their data is widely considered to be accurate.

Content

The variables in this dataset are: - state: State to which the vote count corresponds - state_abr: Two-letter abbreviation of state name - trump_pct: Percentage of the vote won by Donald Trump - biden_pct: Percentage of the vote won by Joe Biden - trump_win: Binary variable denoting whether Donald Trump won the vote in a state - biden_win: Binary variable denoting whether Joe Biden won the vote in a state

Please, If you enjoyed this dataset, don't forget to upvote it.

US Election 2020

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3244747%2F5173f21bc8eaea8335539cc942338b4d%2Fheader_win.png?generation=1605608056355359&alt=media" alt="">

Context

For this year, was the 59th quadrennial presidential election held on Tuesday, November 3, 2020. To win the election, the candidate needs 270 out of 538 electoral votes. A good sign, that show if a candidate is doing well, is if they win states that aren't expcted to go their way.

Content

This dataset contains county-level data from 2020 US Election.

Acknowledgements

• US election 2020: A really simple guide

This file shows all election related data by state and county (i.e. total votes, Republican votes, Democratic votes, Republican voting percentage, Democratic voting percentage) for both the 2020 and 2024 U.S. Presidential Elections.

Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

U.S. Election Tweets 2020

Data includes tweets leading up to the U.S. 2020 election with hashtags as follows: #trump,. #biden, #2020election, #presidentialelection, #electionissues.

Over 600k tweets from Oct. 14 - Nov. 4 2020.

The County Presidential Election Returns 2000-2020 dataset describes the results of the various United States Presidential elections, broken out on a per county basis. It provides the details on the votes cast per candidate, the political party to whom the candidate belongs, and the mode by which the votes were cast. The data was collected by the MIT Election Data and Science Lab, and is housed on the Harvard Dataverse.

References: 1. https://electionlab.mit.edu/ 2. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ

Context

I like history, so figured I would compile a dataset on all US Presidential elections throughout history, and how they went.

Content

Inside are datasets for the winners, candidates, and turnout for each presidential election.

Acknowledgements

Banner Image By Gilbert Stuart https://www.clarkart.edu/artpiece/detail/george-washington, Public Domain, https://commons.wikimedia.org/w/index.php?curid=591229

U.S. President 1976–2020

This data file contains constituency (state-level) returns for elections to the U.S. presidency from 1976 to 2020.

For more information please refer : https://guides.lib.jjay.cuny.edu/c.php?g=992251&p=7179356

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This dataset is collected from 1824 to 2020: 1. Year: Description: The year in which the U.S. election took place. Type: Numeric (Integer) Example: 1824, 1860, 1920, 2020

1. Candidate: Description: The name of the candidate participating in the election. Type: String (Candidate's name) Example: John Adams, Abraham Lincoln, Franklin D. Roosevelt, Joe Biden

2. Party: Description: The political party affiliation of the candidate. Type: String (Party name or abbreviation) Example: Democratic, Republican, Whig, Libertarian

3. Popular Vote: Description: The total number of votes that the candidate received in the popular vote. Type: Numeric (Integer) Example: 500,000, 5,000,000, 70,000,000

4. Result: Description: The outcome of the election for the specified candidate. Type: String (e.g., "Winner," "Runner-up," "Withdrew") Example: Winner, Runner-up, Withdrew, Conceded

5. Percentage: Description: The percentage of the total popular vote received by the candidate. Type: Numeric (Float) Example: 25.3%, 49.8%, 60.5%

This dataset appears to capture essential information about U.S. elections over time, including details about the candidates, their political party affiliations, the number of popular votes they received, the outcome of the election, and the percentage of the total popular vote they secured. This comprehensive dataset allows for the analysis of historical U.S. election trends and outcomes.

Dataset

This dataset was created by Jason G Yoon

Released under CC0: Public Domain

Contents

Context

The goal of this dataset is to provide a tidy way to access to the transcripts of speeches given by various US politicians in the context of the 2020 US Presidential Election. Transcripts have been scraped from rev.com. Some other information, such as location and type of speech, have been manually added to the dataset.

Content

The dataset has the following columns:

speaker: Who gave the speech

title: a title or a description of speech

text: the transcript of the speech

location: the location or the platform where the speech was give

type: type of speech (e.g., campaign speech, interview or debate)

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

Dataset

This dataset was created by AllenEdgarPoe

Contents

This dataset contains US Presidential Election results broken down by congressional district. It shows each party's vote percentage and overall number of votes.

Data through 2020 was scraped from the maps found here. Credit to Kiernan Park-Egan at Western Washington University. I filled in a handful of missing at-large district results so the dataset is complete.

2024 data was compiled by Drew Savicki and other contributors.

Rows per election: - 1952 — 433 (NM and ND have two at-large districts each.) - 1956 — 433 (NM and ND have two at-large districts each.) - 1960 — 435 (NM and ND have two at-large districts each. New seats in AK and HI.) - 1964 — 434 (DC gets to vote. NM and HI have two at-large districts each.) - 1968 — 435 (HI has two at-large districts.) - 1972 to present — 436

Missing data: - 1960 — 141 rows are missing Total Vote value. - 1964 — All rows are missing Total Vote value.

Dataset

This dataset was created by Hue Dinh

Contents

[READ THIS FIRST! DATASETS FOR Academic/Learning/Non-commercial purpose]

Context

US Election 2020 is very interesting to look into as it is an election in the middle of a pandemic. Me and my teammate created a twitter crawler using Twitter API and Tweepy for my Artificial Intelligence coursework. We chose Donald Trump as a subject of interest as President Trump was known for his twitter interaction.

I decided to deploy my crawler on post-voting day to conduct a sentiment analysis.

Tweet text in this datasets is suitable for Sentiment Analysis usage.

Content

This raw datasets is crawled using Tweepy library and Twitter API. 2500 tweets were gathered per 15 minutes. There are total of 247,500 row of entries and 13 columns, with the total of 3,217,500 cells of data. Data cleaning is needed to perform before doing any analysis.

Datasets date range: 4th November 2020 - 11th November 2020 Tweets with "Trump", "DonalTrump", "realDonalTrump" were capture.

(The User = user of the particular row) username: Twitter User handle accDesc: Description of the user on profile location: Location of the tweet following: Total number of account the user is following followers: Total number of followers of the user totaltweets: Total tweets created of the user usercreated: Date of the user registered his/her Twitter account tweetcreated: Date of the tweet created favouritecount: tweet <3 count (equivalent to like on Facebook) retweetcount: Total tweet's retweet (equivalent to share on Facebook) text: Text body of the tweet tweetsource: Device used to create this tweet hashtags: hashtag of the tweet in JSON format

Acknowledgements and Disclaimers

Banner and thumbnail courtesy of > visuals < from unsplash.com

Much thanks to my teammate Jiacheng Loh and ChenZhen Li for the efforts.

Please do not use this datasets for any malicious attempts, any damage done is not under the responsible of me.

This datasets were gathered for the purpose of learning and not for commercial purposes.

Data were public in the public domain, therefore i assume these data is open for all.

Limitations

Datasets are gathered with at least 15 minutes interval, therefore datecreated distribution is not equal and may not include all tweets created within the date range.

The Elections Performance Index (EPI) 2020 dataset offers a comprehensive, state-level evaluation of how well U.S. states administered elections in 2020. Developed by the MIT Election Data and Science Lab, this dataset provides crucial indicators to assess the performance, accessibility, and integrity of electoral processes across states. It includes a range of metrics designed to track changes and trends in election administration over time, supporting transparency, policy improvement, and academic research. The dataset, provided in CSV format, allows for replication of findings based on the most up-to-date data as of 2022. Citation (APA format):

MIT Election Data and Science Lab. (2022). Elections Performance Index 2020 (Version 3.0) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/MRQWAE License:

Creative Commons CC0 1.0 Universal (Public Domain Dedication)

Context

As you all know, America's Presidential election 2020 will going to happen in November. The result of which will going to be a big thing not just only for American citizens but other countries people too and this will ultimately effect the relations with other countries. So, I have scraped tweet replies of Twitter handle of both the final runners i.e., Donald Trump and Joe Biden. So, the idea is to predict result of this upcoming election and get some insights on how to make predictions with better accuracy.

Content

• This dataset is collected through scraping Twitter handles of Donald Trump and Joe Biden separately for a week using python script and retrieving replies on tweets done by both of them. The idea is to look for the sentiments of people towards their representative and how they are reacting to the tweets done by both the leaders. So that one can predict results in advance by analysing people sentiments using data science skills. Below I have attached two csv files, one is for Donald Trump and another is for Joe Biden. Both of the csv files are having two common columns: 1st column user contains user name who have replied to the tweets. 2nd column text contains text of replies on each tweet.

• To get more insights on it and how to start with the project, you can refer to my article below. This was a small fun project to get insights thorugh this dataset where I am not training any model as such. I am simply analysing people's sentiment using NLP library TextBlob. So, you can refer to this article to get started with the dataset on how to clean it, analyze it and visualize it. Feel free to use some other algorithms to achieve greater accuracy. Predicting US Presidential Election Result Using Twitter Sentiment Analysis with Python

Inspiration

This dataset actually gives immense opportunities to revise your data science skills from fundamentals. - Data Cleaning as this dataset includes lots of noise which will be going to be a barrier in making good model. - Exploratory Data Analysis - Data Analysis - Data Visualization

• It will be great if you can perform some analysis on this text and get insights or pre-poll survey like thing for the upcoming elections and predict result in advance.

US election season is here, which means it's time to analyze some polling data! Less than four years ago, Donald Trump was elected president of the United States, and on November 2nd, 2020, he will run for re-election against a Democratic challenger. Kernels may answer a variety of questions, including: "How accurate were the polls in 2016?", "Which Democratic challenger will fare best against Trump in 2020 according to the polls?", "Which states are anybody's game?", and of course the ultimate question, "Will Trump win again in 2020?" Then, after November 2nd, we can ask ourselves what happened in 2020!

The two datasets included here include:

• US 2016 General Election Results by State and County (from Data World)
• US 2020 General Election Polling Data (from FiveThirtyEight)

This data should be used in conjunction with the 2016 General Election Polling Data from this link: https://www.kaggle.com/fivethirtyeight/2016-election-polls

The 2020 polling data will be updated regularly until the election on November 2nd, 2020. Then, I will upload a dataset of 2020 general election final results. As time goes on and we get closer to the election, we will acquire higher quantities of data and data that is more representative of what might happen on November 2nd. Have fun!