Dataset

This dataset was created by Rishabh

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Dataset

This dataset was created by Ismail Hossain

Released under Data files © Original Authors

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Dataset

This dataset was created by byvi19

Released under Apache 2.0

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Dataset

This dataset was created by Khaled Taha

Released under Apache 2.0

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Context This dataset is a collection of datasets from different sources related to the automatic detection of cyber-bullying. The data is from different social media platforms like Kaggle, Twitter, Wikipedia Talk pages and YouTube. The data contain text and labeled as bullying or not. The data contains different types of cyber-bullying like hate speech, aggression, insults and toxicity.

Content The data is from different social media platforms like Kaggle, Twitter, Wikipedia Talk pages and YouTube. The data contain text and labeled as bullying or not. The data contains different types of cyber-bullying like hate speech, aggression, insults and toxicity.

Acknowledgements Elsafoury, Fatma (2020), “Cyberbullying datasets”, Mendeley Data, V1, doi: 10.17632/jf4pzyvnpj.1

Dataset

This dataset was created by Shirley Ontaneda

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Dataset

This dataset was created by Deeksha3@

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Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

Dataset

This dataset was created by Madhushree Sannigrahi

Released under MIT

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Dataset

This dataset was created by Dener Mendes

Released under Apache 2.0

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Overview Welcome to Kaggle's third annual Machine Learning and Data Science Survey ― and our second-ever survey data challenge. You can read our executive summary here.

This year, as in 2017 and 2018, we set out to conduct an industry-wide survey that presents a truly comprehensive view of the state of data science and machine learning. The survey was live for three weeks in October, and after cleaning the data we finished with 19,717 responses!

There's a lot to explore here. The results include raw numbers about who is working with data, what’s happening with machine learning in different industries, and the best ways for new data scientists to break into the field. We've published the data in as raw a format as possible without compromising anonymization, which makes it an unusual example of a survey dataset.

Challenge This year Kaggle is launching the second annual Data Science Survey Challenge, where we will be awarding a prize pool of $30,000 to notebook authors who tell a rich story about a subset of the data science and machine learning community.

In our third year running this survey, we were once again awed by the global, diverse, and dynamic nature of the data science and machine learning industry. This survey data EDA provides an overview of the industry on an aggregate scale, but it also leaves us wanting to know more about the many specific communities comprised within the survey. For that reason, we’re inviting the Kaggle community to dive deep into the survey datasets and help us tell the diverse stories of data scientists from around the world.

The challenge objective: tell a data story about a subset of the data science community represented in this survey, through a combination of both narrative text and data exploration. A “story” could be defined any number of ways, and that’s deliberate. The challenge is to deeply explore (through data) the impact, priorities, or concerns of a specific group of data science and machine learning practitioners. That group can be defined in the macro (for example: anyone who does most of their coding in Python) or the micro (for example: female data science students studying machine learning in masters programs). This is an opportunity to be creative and tell the story of a community you identify with or are passionate about!

Submissions will be evaluated on the following:

Composition - Is there a clear narrative thread to the story that’s articulated and supported by data? The subject should be well defined, well researched, and well supported through the use of data and visualizations. Originality - Does the reader learn something new through this submission? Or is the reader challenged to think about something in a new way? A great entry will be informative, thought provoking, and fresh all at the same time. Documentation - Are your code, and notebook, and additional data sources well documented so a reader can understand what you did? Are your sources clearly cited? A high quality analysis should be concise and clear at each step so the rationale is easy to follow and the process is reproducible To be valid, a submission must be contained in one notebook, made public on or before the submission deadline. Participants are free to use any datasets in addition to the Kaggle Data Science survey, but those datasets must also be publicly available on Kaggle by the deadline for a submission to be valid.

How to Participate To make a submission, complete the submission form. Only one submission will be judged per participant, so if you make multiple submissions we will review the last (most recent) entry.

No submission is necessary for the Weekly Notebook Award. To be eligible, a notebook must be public and use the 2019 Data Science Survey as a data source.

Submission deadline: 11:59PM UTC, December 2nd, 2019.

Survey Methodology This survey received 19,717 usable respondents from 171 countries and territories. If a country or territory received less than 50 respondents, we grouped them into a group named “Other” for anonymity.

We excluded respondents who were flagged by our survey system as “Spam”.

Most of our respondents were found primarily through Kaggle channels, like our email list, discussion forums and social media channels.

The survey was live from October 8th to October 28th. We allowed respondents to complete the survey at any time during that window. The median response time for those who participated in the survey was approximately 10 minutes.

Not every question was shown to every respondent. You can learn more about the different segments we used in the survey_schema.csv file. In general, respondents with more experience were asked more questions and respondents with less experience were asked less questions.

To protect the respondents’ identity, the answers to multiple choice questions have been separated into a separate data file from the open-ended responses. We do not provide a key to match up the multiple choice and free form responses. Further, the free form responses have been randomized column-wise such that the responses that appear on the same row did not necessarily come from the same survey-taker.

Multiple choice single response questions fit into individual columns whereas multiple choice multiple response questions were split into multiple columns. Text responses were encoded to protect user privacy and countries with fewer than 50 respondents were grouped into the category "other".

Data has been released under a CC 2.0 license: https://creativecommons.org/licenses/by/2.0/

Dataset

This dataset was created by Kiran Sangamnere

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Context

The MNIST dataset is one of the best known image classification problems out there, and a veritable classic of the field of machine learning. This dataset is more challenging version of the same root problem: classifying letters from images. This is a multiclass classification dataset of glyphs of English letters A - J.

This dataset is used extensively in the Udacity Deep Learning course, and is available in the Tensorflow Github repo (under Examples). I'm not aware of any license governing the use of this data, so I'm posting it here so that the community can use it with Kaggle kernels.

Content

notMNIST _large.zip is a large but dirty version of the dataset with 529,119 images, and notMNIST_small.zip is a small hand-cleaned version of the dataset, with 18726 images. The dataset was assembled by Yaroslav Bulatov, and can be obtained on his blog. According to this blog entry there is about a 6.5% label error rate on the large uncleaned dataset, and a 0.5% label error rate on the small hand-cleaned dataset.

The two files each containing 28x28 grayscale images of letters A - J, organized into directories by letter. notMNIST_large.zip contains 529,119 images and notMNIST_small.zip contains 18726 images.

Acknowledgements

Thanks to Yaroslav Bulatov for putting together the dataset.

Dataset

This dataset was created by Jeff Hale

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Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset.

If you are using our dataset, please also cite our paper -

MDPI and ACS Style Eunus, S.I.; Hossain, S.; Ridwan, A.E.M.; Adnan, A.; Islam, M.S.; Karim, D.Z.; Alam, G.R.; Uddin, J. ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection. AI 2024, 5, 482-503. https://doi.org/10.3390/ai5020024

AMA Style Eunus SI, Hossain S, Ridwan AEM, Adnan A, Islam MS, Karim DZ, Alam GR, Uddin J. ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection. AI. 2024; 5(2):482-503. https://doi.org/10.3390/ai5020024

Chicago/Turabian Style Eunus, Salman Ibne, Shahriar Hossain, A. E. M. Ridwan, Ashik Adnan, Md. Saiful Islam, Dewan Ziaul Karim, Golam Rabiul Alam, and Jia Uddin. 2024. "ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection" AI 5, no. 2: 482-503. https://doi.org/10.3390/ai5020024

Dataset

This dataset was created by Nelson InCube

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Dataset

This dataset was created by Mishalle Ni

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Dataset

This dataset was created by M. Raza Siddique

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Dataset

This dataset was created by like

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Dataset

This dataset was created by Nathan Karasch

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