Context

The issue of “fake news” has arisen recently as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge was organized in early 2017 to encourage development of machine learning-based classification systems that perform “stance detection” -- i.e. identifying whether a particular news headline “agrees” with, “disagrees” with, “discusses,” or is unrelated to a particular news article -- in order to allow journalists and others to more easily find and investigate possible instances of “fake news.”

Content

The data provided is (headline, body, stance) instances, where stance is one of {unrelated, discuss, agree, disagree}. The dataset is provided as two CSVs:

train_bodies.csv

This file contains the body text of articles (the articleBody column) with corresponding IDs (Body ID)

train_stances.csv

This file contains the labeled stances (the Stance column) for pairs of article headlines (Headline) and article bodies (Body ID, referring to entries in train_bodies.csv).

Distribution of the data

The distribution of Stance classes in train_stances.csv is as follows:

There are 4 possible classifications: 1. The article text agrees with the headline. 2. The article text disagrees with the headline. 3. The article text is a discussion of the headline, without taking a position on it. 4. The article text is unrelated to the headline (i.e. it doesn’t address the same topic).

Acknowledgements

For details of the task, see FakeNewsChallenge.org

Dataset

This dataset was created by Satyam Kr

Released under MIT

Contents

Criteo Display Advertising Challenge dataset, which is provided by the Criteo company on the famous machine learning website Kaggle for advertising CTR .

Universal Embedding Challenge baseline model implementation.

This folder contains the baseline model implementation for the Kaggle universal image embedding challenge based on

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
• Training data-efficient image transformers & distillation through attention.

Following the above ideas, we also add a 64 projection layer on top of the Vision Transformer base model as the final embedding, since the competition requires embeddings of at most 64 dimensions. Please find more details in image_classification.py.

To use the code, please firstly install the prerequisites

pip install -r universal_embedding_challenge/requirements.txt

git clone https://github.com/tensorflow/models.git /tmp/models
export PYTHONPATH=$PYTHONPATH:/tmp/models
pip install --user -r /tmp/models/official/requirements.txt

Secondly, please download the imagenet1k data in TFRecord format from https://www.kaggle.com/datasets/hmendonca/imagenet-1k-tfrecords-ilsvrc2012-part-0 and https://www.kaggle.com/datasets/hmendonca/imagenet-1k-tfrecords-ilsvrc2012-part-1, and merge them together under folder imagenet-2012-tfrecord/. As a result, the paths to the training datasets and the validation datasets should be imagenet-2012-tfrecord/train* and imagenet-2012-tfrecord/validation*, respectively.

The trainer for the model is implemented in train.py, and the following example launches the training

python -m universal_embedding_challenge.train \
 --experiment=vit_with_bottleneck_imagenet_pretrain \
 --mode=train_and_eval \
 --model_dir=/tmp/imagenet1k_test

The trained model checkpoints could be further converted to savedModel format using export_saved_model.py for Kaggle submission.

The code to compute metrics for Universal Embedding Challenge is implemented in metrics.py and the code to read the solution file is implemented in read_retrieval_solution.py.

Dataset

This dataset was created by Muhammad Ahmed

Contents

Dataset

This dataset was created by Sreenanda Sai Dasari

Contents

VaggP/Eedi-competition-kaggle-prompt-formats dataset hosted on Hugging Face and contributed by the HF Datasets community

The Kaggle display advertising challenge dataset.

This is an enriched version of the Code4ML dataset, a large-scale corpus of annotated Python code snippets, competition summaries, and data descriptions sourced from Kaggle. The initial release includes approximately 2.5 million snippets of machine learning code extracted from around 100,000 Jupyter notebooks. A portion of these snippets has been manually annotated by human assessors through a custom-built, user-friendly interface designed for this task.

The original dataset is organized into multiple CSV files, each containing structured data on different entities:

• code_blocks.csv: Contains raw code snippets extracted from Kaggle.
• kernels_meta.csv: Metadata for the notebooks (kernels) from which the code snippets were derived.
• competitions_meta.csv: Metadata describing Kaggle competitions, including information about tasks and data.
• markup_data.csv: Annotated code blocks with semantic types, allowing deeper analysis of code structure.
• vertices.csv: A mapping from numeric IDs to semantic types and subclasses, used to interpret annotated code blocks.

Table 1. code_blocks.csv structure

Table 2. kernels_meta.csv structure

Table 3. competitions_meta.csv structure

Table 4. markup_data.csv structure

The dataset allows mapping between these tables. For example:

• code_blocks.csv can be linked to kernels_meta.csv via the kernel_id column.
• kernels_meta.csv is connected to competitions_meta.csv through comp_name. To maintain quality, kernels_meta.csv includes only notebooks with available Kaggle scores.

In addition, data_with_preds.csv contains automatically classified code blocks, with a mapping back to code_blocks.csvvia the code_blocks_index column.

Code4ML 2.0 Enhancements

The updated Code4ML 2.0 corpus introduces kernels extracted from Meta Kaggle Code. These kernels correspond to the kaggle competitions launched since 2020. The natural descriptions of the competitions are retrieved with the aim of LLM.

Notebooks in kernels_meta2.csv may not have a Kaggle score but include a leaderboard ranking (rank), providing additional context for evaluation.

competitions_meta_2.csv is enriched with data_cards, decsribing the data used in the competitions.

Applications

The Code4ML 2.0 corpus is a versatile resource, enabling training and evaluation of models in areas such as:

• Code generation
• Code understanding
• Natural language processing of code-related tasks

Dataset Summary

Natural Language Processing with Disaster Tweets: https://www.kaggle.com/competitions/nlp-getting-started/data This particular challenge is perfect for data scientists looking to get started with Natural Language Processing. The competition dataset is not too big, and even if you don’t have much personal computing power, you can do all of the work in our free, no-setup, Jupyter Notebooks environment called Kaggle Notebooks.

Columns

id - a unique identifier for each tweet… See the full description on the dataset page: https://huggingface.co/datasets/gdwangh/kaggle-nlp-getting-start.

This dataset contains relevant notebook submission files and papers:

Notebook submission files from:

PS S3E18 EDA + Ensembles by @zhukovoleksiy v8 0.65031.

PS_3.18_LGBM_bin by @akioonodera v9 0.64706.

PS3E18 EDA| Ensemble ML Pipeline |BinaryPredictict by @tetsutani v37 0.65540.

0.65447 | Ensemble | AutoML | Enzyme Classify by @utisop v10 0.65447.

pyBoost baselinepyBoost baseline by @l0glikelihood v4 0.65446.

Random Forest EC classification by @jbomitchell RF62853_submission.csv 0.62853.

Overfit Champion by @onurkoc83 v1 0.65810.

Playground Series S3E18 - EDA & Separate Learning by @mateuszk013 v1 0.64933.

Ensemble ML Pipeline + Bagging = 0.65557 by @chingiznurzhanov v7 0.65557.

PS3E18| FeatureEnginering+Stacking by @jaygun84 v5 0.64845.

S03E18 EDA | VotingClassifier | Optuna v15 0.64776.

PS3E18 - GaussianNB by @mehrankazeminia v1 0.65898, v2 0.66009 & v3 0.66117.

Enzyme Weighted Voting by @nivedithavudayagiri v2 0.65028.

Multi-label With TF-Decision Forests by @gusthema v6 0.63374.

S3E18 Target_Encoding LB 0.65947 by @meisa0 v1 0.65947.

Boost Classifier Model by @satyaprakashshukl v7 0.64965.

PS3E18: Multiple lightgbm models + Optuna by syerramilli v4 0.64982.

s3e18_solution for overfitting public :0.64785 by @onurkoc83 v1 0.64785.

PSS3E18 : FLAML : roc_auc_weighted by @gauravduttakiit v2 0.64732.

PGS318: combiner by @kdmitrie v4 0.65350.

averaging best solutions mean vs Weighted mean by @omarrajaa v5 0.66106.

Papers

N Nath & JBO Mitchell, Is EC class predictable from reaction mechanism? BMC Bioinformatics, 13:60 (2012) doi: 10.1186/1471-2105-13-60

L De Ferrari & JBO Mitchell, From sequence to enzyme mechanism using multi-label machine learning, BMC Bioinformatics, 15:150 (2014) doi: 10.1186/1471-2105-15-150

N Nath, JBO Mitchell & G Caetano-Anollés, The Natural History of Biocatalytic Mechanisms, PLoS Computational Biology, 10, e1003642 (2014) doi: 10.1371/journal.pcbi.1003642

KE Beattie, L De Ferrari & JBO Mitchell, Why do sequence signatures predict enzyme mechanism? Homology versus Chemistry, Evolutionary Bioinformatics, 11: 267-274 (2015) doi: 10.4137/EBO.S31482

HY Mussa, L De Ferrari & JBO Mitchell, Enzyme Mechanism Prediction: A Template Matching Problem on InterPro Signature Subspaces, BMC Research Reports, 8:744 (2015) doi: 10.1186/s13104-015-1730-7

Dataset

This dataset was created by Alexander Chervov

Contents

The dataset consists of Haematoxylin and Eosin stained histology images at 20x objective magnification (~0.5 microns/pixel) from 6 different data sources. For each image, an instance segmentation and a classification mask is provided. Within the dataset, each nucleus is assigned to one of the following categories:

• Epithelial
• Lymphocyte
• Plasma
• Eosinophil
• Neutrophil
• Connective tissue For more information on the dataset and the associated categories, we encourage participants to read the original dataset paper.

Data Format

Our provided patch-level dataset contains 4,981 non-overlapping images of size 256x256 provided in the following format: - RGB images - Segmentation & classification maps - Nuclei counts The RGB images and segmentation/classification maps are each stored as a single NumPy array. The RGB image array has dimensions 4981x256x256x3, whereas the segmentation & classification map array has dimensions 4981x256x256x2. Here, the first channel is the instance segmentation map and the second channel is the classification map. For the nuclei counts, we provide a single csv file, where each row corresponds to a given patch and the columns determine the counts for each type of nucleus. The row ordering is in line with the order of patches within the numpy files. https://grand-challenge-public-prod.s3.amazonaws.com/i/2021/11/20/sample.png" alt=""> A given nucleus is considered present in the image if any part of it is within the central 224x224 region within the patch. This ensures that a nucleus is only considered for counting if it lies completely within the original 256x256 image.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

This dataset was provided by the Organizers of the CoNIC Challenge: - Simon Graham (TIA, PathLAKE) - Mostafa Jahanifar (TIA, PathLAKE) - Dang Vu (TIA) - Giorgos Hadjigeorghiou (TIA, PathLAKE) - Thomas Leech (TIA, PathLAKE) - David Snead (UHCW, PathLAKE) - Shan Raza (TIA, PathLAKE) - Fayyaz Minhas (TIA, PathLAKE) - Nasir Rajpoot (TIA, PathLAKE)

TIA: Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, United Kingdom

UHCW: Department of Pathology, University Hospitals Coventry and Warwickshire, United Kingdom

PathLAKE: Pathology Image Data Lake for Analytics Knowledge & Education, University Hospitals Coventry and Warwickshire, United Kingdom

Dataset

This dataset was created by Gbolahan

Contents

This data set is for creating predictive models for the CrunchDAO tournament. Registration is required in order to participate in the competition, and to be eligible to earn $CRUNCH tokens.

See notebooks (Code tab) for how to import and explore the data, and build predictive models.

• EDA
• QuickStarter

See Terms of Use for data license.

Dataset

This dataset was created by VIJAY DEVANE

Released under Apache 2.0

Contents

Shake-what ?!

The Shake phenomenon occurs when the competition is shifting between two different datasets :

\[ \text{Public test set} \ \Rightarrow \ \text{Private test set} \quad \Leftrightarrow \quad LB-\text{public} \ \Rightarrow \ LB-\text{private} \]

The private test set that so far was unavailable becomes available, and thus the models scores are re-calculated. This re-evaluation elicits a respective re-ranking of the contestants in the competition. The shake allows participants to assess the severity of their overfitting to the public dataset, and act to improve their model until the deadline.

Unable to find a uniform conventional term for this mechanism, I will use my common sense to define the following intuition :

             <img src="https://github.com/Daniboy370/Uploads/blob/master/Kaggle-shake-ups/images/latex.png?raw=true" width="550">

From the starter kernel :

               <img src="https://github.com/Daniboy370/Uploads/blob/master/Kaggle-shake-ups/vids/shakeup_VID.gif?raw=true" width="625">

Content

Seven datasets of competitions which were scraped from Kaggle :

As an example, consider the following dataframe from the Quora competition : Team Name | Rank-private | Rank-public | Shake | Score-private | Score-public --- | --- The Zoo |1|7|6|0.71323|0.71123 ...| ...| ...| ...| ...| ... D.J. Trump|1401|65|-1336|0.000|0.70573

I encourage everybody to investigate thoroughly the dataset in sought of interesting findings !

\[ \text{Enjoy !}\]

Collections of kernels submissions for the Kaggle survey competitions from 2017 to 2022. As this data was collected during the 2022 survey competition, it does not contain all the kernels for year 2022 .

The dataset for this competition (both train and test) was generated from a deep learning model fine-tuned on the Used Car Price Prediction Dataset. While feature distributions are similar to the original, they are not identical. You are welcome to use the original dataset to explore differences and to see if incorporating it into your training improves model performance, though it is not mandatory.

Files:

train.csv: The training dataset; refer to the original dataset link above for column descriptions. test.csv: The test dataset; your objective is to predict the target value, Price. sample_submission.csv: A sample submission file in the correct format.

📝 Description:

A cleaned version of Competitions.csv focused on timeline analysis.

✅ Includes: CompetitionId, Title, Deadline, EnabledDate, HostSegmentTitle ✅ Helps understand growth over time, and regional hosting focus ✅ Can be joined with teams_clean.csv and user_achievements_clean.csv