The 2016 U.S. presidential election was contested by Donald J. Trump of the Republican Party, and Hillary Rodham Clinton of the Democratic Party. Clinton had been viewed by many as the most likely to succeed President Obama in the years leading up to the election, after losing the Democratic nomination to him in 2008, and entered the primaries as the firm favorite. Independent Senator Bernie Sanders soon emerged as Clinton's closest rival, and the popularity margins decreased going into the primaries. A few other candidates had put their name forward for the Democratic nomination, however all except Clinton and Sanders had dropped out by the New Hampshire primary. Following a hotly contested race, Clinton arrived at the Democratic National Convention with 54 percent of pledged delegates, while Sanders had 46 percent. Controversy emerged when it was revealed that Clinton received the support of 78 percent of Democratic superdelegates, while Sanders received just seven percent. With her victory, Hillary Clinton became the first female candidate nominated by a major party for the presidency. With seventeen potential presidential nominees, the Republican primary field was the largest in US history. Similarly to the Democratic race however, the number of candidates thinned out by the time of the New Hampshire primary, with Donald Trump and Ted Cruz as the frontrunners. As the primaries progressed, Trump pulled ahead while the remainder of the candidates withdrew from the race, and he was named as the Republican candidate in May 2016. Much of Trump's success has been attributed to the free media attention he received due to his outspoken and controversial behavior, with a 2018 study claiming that Trump received approximately two billion dollars worth of free coverage during the primaries alone. Campaign The 2016 presidential election was preceded by, arguably, the most internationally covered and scandal-driven campaign in U.S. history. Clinton campaigned on the improvement and expansion of President Obama's more popular policies, while Trump's campaign was based on his personality and charisma, and took a different direction than the traditional conservative, Republican approach. In the months before the election, Trump came to represent a change in how the U.S. government worked, using catchy slogans such as "drain the swamp" to show how he would fix what many viewed to be a broken establishment; painting Clinton as the embodiment of this establishment, due to her experience as First Lady, Senator and Secretary of State. The candidates also had fraught relationships with the press, although the Trump campaign was seen to have benefitted more from this publicity than Clinton's. Controversies Trump's off the cuff and controversial remarks gained him many followers throughout the campaign, however, just one month before the election, a 2005 video emerged of Trump making derogatory comments about grabbing women "by the pussy". The media and public's reaction caused many high-profile Republicans to condemn the comments (for which he apologized), with many calling for his withdrawal from the race. This controversy was soon overshadowed when it emerged that the FBI was investigating Hillary Clinton for using a private email server while handling classified information, furthering Trump's narrative that the Washington establishment was corrupt. Two days before the election, the FBI concluded that Clinton had not done anything wrong; however the investigation had already damaged the public's perception of Clinton's trustworthiness, and deflected many undecided voters towards Trump. Results Against the majority of predictions, Donald Trump won the 2016 election, and became the 45th President of the United States. Clinton won almost three million more votes than her opponent, however Trump's strong performance in swing states gave him a 57 percent share of the electoral votes, while Clinton took just 42 percent. The unpopularity of both candidates also contributed to much voter abstention, and almost six percent of the popular vote went to third party candidates (despite their poor approval ratings). An unprecedented number of faithless electors also refused to give their electoral votes to the two main candidates, instead giving them to five non-candidates. In December, it emerged that the Russian government may have interfered in this election, and the 2019 Mueller Report concluded that Russian interference in the U.S. election contributed to Clinton's defeat and the victory of Donald Trump. In total, 26 Russian citizens and three Russian organizations were indicted, and the investigation led to the indictment and conviction of many top-level officials in the Trump campaign; however Trump and the Russian government both strenuously deny these claims, and Trump's attempts to frame the Ukrainian government for Russia's invol...

2016 General Election: Trump vs. Clinton | RealClearPolling

Kaggle’s March Machine Learning Mania competition challenged data scientists to predict winners and losers of the men's 2016 NCAA basketball tournament. This dataset contains the 1070 selected predictions of all Kaggle participants. These predictions were collected and locked in prior to the start of the tournament.

How can this data be used? You can pivot it to look at both Kaggle and NCAA teams alike. You can look at who will win games, which games will be close, which games are hardest to forecast, or which Kaggle teams are gambling vs. sticking to the data.

The NCAA tournament is a single-elimination tournament that begins with 68 teams. There are four games, usually called the “play-in round,” before the traditional bracket action starts. Due to competition timing, these games are included in the prediction files but should not be used in analysis, as it’s possible that the prediction was submitted after the play-in round games were over.

Data Description

Each Kaggle team could submit up to two prediction files. The prediction files in the dataset are in the 'predictions' folder and named according to:

TeamName_TeamId_SubmissionId.csv

The file format contains a probability prediction for every possible game between the 68 teams. This is necessary to cover every possible tournament outcome. Each team has a unique numerical Id (given in Teams.csv). Each game has a unique Id column created by concatenating the year and the two team Ids. The format is the following:

Id,Pred
2016_1112_1114,0.6
2016_1112_1122,0
...

The team with the lower numerical Id is always listed first. “Pred” represents the probability that the team with the lower Id beats the team with the higher Id. For example, "2016_1112_1114,0.6" indicates team 1112 has a 0.6 probability of beating team 1114.

For convenience, we have included the data files from the 2016 March Mania competition dataset in the Scripts environment (you may find TourneySlots.csv and TourneySeeds.csv useful for determining matchups, see the documentation). However, the focus of this dataset is on Kagglers' predictions.

This statistic shows the projected global growth rate of per person health spending between 2016 and 2030, by source. Global health spending funded by governments is predicted to increase **** percent within that time.

This figure shows the predictions about future breakfast food trends in Ireland in 2016. Gluten free bread is expected to gain popularity, according to ** percent of respondents. On the other hand, ** percent of people surveyed predicted that jam will become less popular.

The ANES is a nationally representative, cross-sectional survey used extensively in political science. This is a dataset from the 2016 pilot study, consisting of responses from 1200 voting-age U.S. citizens.

Votes for Republican presidential candidate by alternative measures of predicted internet, 2012–2016.

Of the 10.2 billion U.S. dollars that sources projected will be spent in the 2016 election season, by far the most popular destination for these funds was broadcast television, which was expected to attract 6.06 billion U.S. dollars in advertising spending. This is five and a half times more than the next most popular advertising channel, cable television, and over seven times more than the spend on radio or newspaper advertising. Political advertising spending

The amounts spent on political advertising have been increasing each election season, with around 400 million more spent in 2012 than 2016, and total spending for the 2018 election season predicted to be higher again than in 2016 (especially for broadcast television and online/digital advertising)1. While most attention is paid to political advertising spending at the federal level, which includes both presidential and congressional elections, a significant amount is also spent on advertising for local and state elections.

Online political advertising

The role played by online and digital advertising in U.S. elections has been growing in importance since the mid-2000s. Many commentators have credited President Obama’s use of social media as a key factor in his 2008 electoral victory, after which having a strong online presence was an essential component of all election advertising strategies. This growing importance is clearly evidenced by the advertising expenditure figures, with around 128 times more money predicted to be spent online on the 2020 presidential election season than in 2008.

For every category, we applied lasso regression to predict the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift.

This dataset provides values for 2016 reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Freshwater salinization is an emerging water quality issue for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA region. A model was developed to predict specific conductance (SC; a proxy for salinity) conditions across the CBW and departures from background SC. Discrete observations of SC from 1999-2016 were acquired from a published SC data inventory and explanatory variables describing sources of SC were compiled from several sources. Random forests modeling was conducted to predict SC at four time periods (1999-2001, 2004-2006, 2009-2011, and 2014-2016) at all non-tidal National Hydrography Dataset Plus Version 2.1 (NHDPlusV2.1; 1:100K scale) stream reaches. These predictions were then compared to a national background SC dataset to determine relative departures from background SC for each NHDPlusV2.1 reach ID. This data release contains model input data, model output data, and predictions of SC. This data release contains the following three files: 1."Model_input.csv": Contains SC observations, explanatory variables, and additional columns relevant to the model application. 2. "Model_output.csv": Contains predicted SC values for the reaches contained in either the testing or training datasets, as well as the feature contributions for each explanatory variable. 3. "Model_predictions.csv": Contains predicted SC, predicted/expected (or P/E) ratios, and departure categories for all non-tidal reach IDs in the CBW for the four time periods.

The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as environmental epidemiology, environmental justice, health equity, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, U.S. Census Bureau American CommUnity Survey (ACS), and Area Deprivation Index (ADI). The data are particularly useful for studies on rural populations who are under-represented due to the lack of air monitoring sites in rural areas. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by lowering the key barrier to participation in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.

This dataset provides values for 2016 reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Predicted internet, 1996.

Actually, I prepare this dataset for students on my Deep Learning and NLP course.

But I am also very happy to see kagglers play around with it.

Have fun!

Description:

There are two channels of data provided in this dataset:

1. News data: I crawled historical news headlines from Reddit WorldNews Channel (/r/worldnews). They are ranked by reddit users' votes, and only the top 25 headlines are considered for a single date. (Range: 2008-06-08 to 2016-07-01)

2. Stock data: Dow Jones Industrial Average (DJIA) is used to "prove the concept". (Range: 2008-08-08 to 2016-07-01)

I provided three data files in .csv format:

1. RedditNews.csv: two columns The first column is the "date", and second column is the "news headlines". All news are ranked from top to bottom based on how hot they are. Hence, there are 25 lines for each date.

2. DJIA_table.csv: Downloaded directly from Yahoo Finance: check out the web page for more info.

3. Combined_News_DJIA.csv: To make things easier for my students, I provide this combined dataset with 27 columns. The first column is "Date", the second is "Label", and the following ones are news headlines ranging from "Top1" to "Top25".

=========================================

To my students:

I made this a binary classification task. Hence, there are only two labels:

"1" when DJIA Adj Close value rose or stayed as the same;

"0" when DJIA Adj Close value decreased.

For task evaluation, please use data from 2008-08-08 to 2014-12-31 as Training Set, and Test Set is then the following two years data (from 2015-01-02 to 2016-07-01). This is roughly a 80%/20% split.

And, of course, use AUC as the evaluation metric.

=========================================

+++++++++++++++++++++++++++++++++++++++++

To all kagglers:

Please upvote this dataset if you like this idea for market prediction.

If you think you coded an amazing trading algorithm,

friendly advice

do play safe with your own money :)

+++++++++++++++++++++++++++++++++++++++++

Feel free to contact me if there is any question~

And, remember me when you become a millionaire :P

Note: If you'd like to cite this dataset in your publications, please use:

Sun, J. (2016, August). Daily News for Stock Market Prediction, Version 1. Retrieved [Date You Retrieved This Data] from https://www.kaggle.com/aaron7sun/stocknews.

The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains estimates of ozone concentrations at a high resolution spatially (1-km grid cells) and temporally (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically-weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages. In version 1.10, we have enhanced the completeness of daily O3 predictions by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, we used inverse distance weighting interpolation to fill the missing grid cells. Other missing daily O3 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily 8-hour maximum and annual O3 predictions allow public health researchers to respectively estimate the short- and long-term effects of O3 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for O3. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.

BackgroundOver 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015–2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission.Methodology/Principal FindingsWe combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015–2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions.SignificanceGiven the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak.

AI-powered price forecasts for 2016.HK stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Pheno Forecast maps predict key life cycle stages in a range of species to improve conservation and management outcomes. For insect pest species, Pheno Forecasts are based on published growing degree day (GDD) thresholds for key points in species life cycles. These key points typically represent life cycle stages when management actions are most effective. These maps are updated daily and available 6 days in the future. We forecast adult emergence in emerald ash borer (Agrilus planipennis) at 450-1500F growing degree days (base 50F, start date: January 1, double sine GDD method). This forecast was generated from February 2016 through March 2023, as a formatted version of the USA-NPN Accumulated Growing Degree Day product, and made available via the Visualization Tool and the USA-NPN website. It has been replaced by an enhanced forecast: Emerald Ash Borer Adult Emergence and Egg Hatch Forecasts, Contiguous United States, 2023 - Current Year_For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Seismicity catalogs, gridded seismic hazard curve data, gridded ground motion data, and mapped gridded ground motion values are available for the 2016 One-Year Seismic Hazard Forecast for the Central and Eastern U.S. from Induced and Natural Earthquakes. Probabilistic seismic hazard data and maps of the conterminous U.S. for peak ground acceleration (PGA) and 0.2 and 1.0 second spectral acceleration at a probability level of 1 percent in 1 year (annual probability of 0.0101), assuming firm rock soil conditions at 760 m/s, are available. Hazard was calculated on a 0.05 degree by 0.05 degree grid, defined by a bounding box encompassing the central and eastern U.S. (-115 to -65 degrees longitude west, 24.6 to 50 degrees latitude north). Note, hazard in the western U.S. (-125 to -115 longitude west) is taken from gridded hazard curve results of the 2014 National Seismic Hazard Model for the Conterminous U.S. (https://doi.org/10.5066/P9P77LGZ). Development of the 2016 One-Year Seismic Hazard Forecast for the Central and Eastern U.S. from Induced and Natural Earthquakes is documented in the USGS Open-File Report 2016-1035 (https://doi.org/10.3133/ofr20161035). This dataset is considered a legacy dataset. The original dataset was uploaded to the USGS website at the time of publication of the seismic hazard model (2016) but was later moved over the the USGS ScienceBase Catalog (2019). The original dataset was assumed to be complete and accurate, but may contain inconsistencies when compared to more recent, actively maintained datasets.