Hey guys, I am working on a project to help predict if a voicemail is spam! I am building the dataset, and I have around 300 voicemails, almost half are spam and the others are not. I want to create a dataset of at least 500-1000 voicemails.

So I am requesting that anyone share their spam voicemails and/or normal voicemails (which can be non-personal). It can be in any audio format and shared however you are comfortable with!

I am trying to find information about airports. I want to get the name of the airport, IATA code, lat, long, and mayne city, country and etc. Any suggestions?

There are plenty of Word Frequency lists but plurals, adjectives, adverbs of the same word end up in different positions in these lists.

I’m looking for a dataset or a way to create a dataset that has all forms or one word clumped together so it’s less about frequency and more about how familiar the word (and its different forms) is if that makes sense.

For instance, i have a list whete the word “have” is at 25th place, “has” at 39 and “had” at 105. Clearly, anyone who knows one of these words would know the other two as well.

Apologies if I did not get my point across clearly. Any help is appreciated. Thanks!

Hello guys, I’m looking for international data about imports and exports from around the world. Do you know any websites where I can find it?

Hey Redditors,

I know the cars196 dataset is nothing new, but I wanted to share some label errors and outliers that I found within it.

It’s interesting to note that the primary goal of the original paper that curated/used this dataset was “fine-grained categorization” meaning discerning the differences between something like a Chevrolet Cargo Van and a GMC Cargo Van. I found numerous examples of images that exhibit very nuanced mislabelling which is directly counterintuitive to the task they sought to research.

Here are a few examples of nuanced label errors that I found:

Audi TT RS Coupe labeled as an Audi TT Hatchback Audi S5 Convertible labeled as an Audi RS4 Jeep Grand Cherokee labeled as a Dodge Durango

I also found examples of outliers and generally ambiguous images:

multiple cars in one image top-down style images vehicles that didn’t belong to any classes.

I found these issues to be pretty interesting, yet I wasn’t surprised. It’s pretty well known that many common ML datasets exhibit thousands of errors.

If you’re interested in how I found them, feel free to read about it here.

This is for all data enthusiasts out there!
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I’m currently doing research on the lack of water access for the ASEAN Data Science Explorers 2023 competition, and any data source or even incomplete datasets would be useful for us. Thank you in advance

The file contains 3.1 million rows, each representing one article observed at one point in time.

The file uses these columns:

timestamp: The time (in UTC) of the fetch. All articles from the same fetch will have the same timestamp. position: The article’s zero-indexed position in the trending strip, from left to right. text: The text of the link used to highlight the article. Note: Sometimes the same article is associated with different text at different points in time. url: The link’s URL. Note: Sometimes (although relatively rarely) the URL for the same underlying article changes over time.

Note: Although the script generally ran every five minutes, there are some gaps in the data, accounting for roughly 3% of the total time period covered. These gaps owe to two main factors: technical complications (such as server downtime) and periods during which the website swapped out the trending strip with breaking news alerts, single-story highlights, or other notices. Unfortunately, I did not have the foresight to collect data that would distinguish between those scenarios.

Statistics Canada added new features to enhance the overall data user experience on the Standards, Data Sources and Methods Hub. With its improved design, new frequently asked question section and quick access links to resources, the hub is meant to be a one-stop shop for data users, statisticians and others for:

variables and classifications survey methodology key aspects of data quality direct access to questionnaires.

Explore the hub and tell us what you think, so we can make sure this page meets your needs!

Visit the Standards, Data Sources and Methods Hub.

[We are Canada’s national statistical agency. We are here to engage with Canadians and provide them with high-quality statistical information that matters! Publishing in a subreddit does not imply we endorse the content posted by other redditors.]

***

Des améliorations ont été apportées au Carrefour des normes, sources de données et méthodes de Statistique Canada pour rendre l’expérience utilisateur plus conviviale. Avec sa conception améliorée, sa nouvelle section Foire aux questions et ses liens d’accès rapide aux ressources, ce carrefour se veut un guichet unique pour les utilisateurs de données, les statisticiens et autres, qui y trouveront tout ce dont ils ont besoin sur :

les variables et les classifications; la méthodologie d’enquête; la qualité des données; l’accès direct aux questionnaires.

Explorez le Carrefour et dites-nous ce que vous en pensez, nous voulons nous assurer qu’il répond à vos besoins!

Carrefour des normes, sources de données et méthodes.

[Nous sommes l’organisme national de statistique du Canada. Nous sommes ici pour discuter avec les Canadiens et les Canadiennes et leur fournir des renseignements statistiques de grande qualité qui comptent! Le fait de publier dans un sous-reddit ne signifie pas que nous approuvons le contenu affiché par d’autres utilisateurs de Reddit.]

Hello,
I’m currently conducting research on a major investment firm and I’m exploring its influence over the media. Could you please provide information on resources that would give the frequency or ratio of media coverage regarding this company? This data would greatly contribute to my analysis. Thank you for your assistance.
I hope my request is clear. Thank you again.

I’m wanting to answer a question about whether companies who run sports-guessing competitions make good predictions in aggregate, despite the fact that there will be many people in the organisation that don’t care about sports at all, and just pick at random.

What I’m looking for is the data from a company that ran a tipping competition for some sports competition where I can analyse the answers. (e.g. if it was the NBA, I can look that up; if it was your internal squash competition, that’s OK as long as it has who won, as well as the predictions of who would win.)

In return I’ll hopefully be able to confirm that there’s a way of maximising your total return on next year’s competition.

Hey guys I need some help. I’m using statista and working on my thesis.

I have data that’s 1 and 0 (present and not present) and I’m trying to figure out if the data is statistically significant but there isn’t a normal distribution. I’m not sure what to do. Any help would be appreciated.

Does anyone have any sample of datasets that I might use to interpret unstructured clinical notes from EHR systems? The objective is to analyze notes and look for words that can be used to categorize a condition

I am looking to see if there is any data on the share of 3G and 4G phone shipments by country / region or anything close to this?

Tried looking myself but could not find the “perfect” data on this.

Hi, my apologies for possibly asking the wrong questions here. I am a total newbie to all things machine learning, have just discovered kaggle and such and I’m a bit stuck with a silly question: I’ve discovered like a million different datasets on there, but I’m just wondering how people are putting these sets to good use. For instance there’s a big dataset about the Titanic. I can’t fathom a realistic use case where this dataset would prove to be useful. I guess I don’t understand just yet where the ‘machine learning’ aspect in datasets like these come into play. What is it exactly you are predicting with these?

Can somebody please enlighten me what I’m obviously missing here? I really want to know.

Thank you