This is the dataset of Kaggle Masters and Grandmasters by country and their rank on each category (05/03/2024)

This is created by using the Kaggle meta dataset.

This Dataset now updates Weekly.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10030651%2Ff010515a07e80b9e6088cdfab14eb6e6%2Fnotebook%20picture.png?generation=1714740456810221&alt=media" alt="">

The Score used in the notebook

grandmaster = 8 master = 4 contributor = 2 expert = 1 novice = 0

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 !

\[ \text{Enjoy !}\]

Context

This dataset contains top 100 of Kaggle competitions ranking. The dataset will be updated every month.

Content

100 rows and 13 columns. Columns' description are listed below.

• User : Name of the user
• Tier : Grandmaster, Master or Expert
• Company/School : Company/School info of the user if mentioned
• Country : Country info of the user if mentioned
• Competitions_Num : Number of competitions joined
• Competitions_Gold : Number of competitions gold medals won
• Competitions_Silver : Number of competitions silver medals won
• Competitions_Bronze : Number of competitions bronze medals won
• Datasets_Num : Number of public datasets
• Notebooks_Num : Number of public notebooks
• Discussions_Num : Number of topics/comments posted
• Points : Total points
• Profile : Link of Kaggle profile

Acknowledgements

Data from Kaggle. Image from Smartcat.

If you're reading this, please upvote.

Context

This dataset contains Kaggle ranking of competitions.

Content

5000 rows and 8 columns. Columns' description are listed below.

• Rank : Rank of the user
• Tier : Grandmaster, Master or Expert
• Username : Name of the user
• Join Date : Year of join
• Gold Medals : Number of gold medals
• Silver Medals : Number of silver medals
• Bronze Medals : Number of bronze medals
• Points : Total points

Acknowledgements

Data from Kaggle. Image from Olympics.

If you're reading this, please upvote.

Dataset

This dataset was created by nitishaadhikari

Contents

Dataset

This dataset was created by SiyaramGupta

Released under CC0: Public Domain

Contents

Introducing an extensive and valuable Duty-Free Item Master dataset now available on Kaggle! This dataset is a comprehensive collection of duty-free items, offering a wealth of information for analysis, research, and various applications.

Whether you're a data enthusiast, a researcher, or a business professional, this dataset provides insights into the world of duty-free items. Explore the data, uncover trends, and gain a deeper understanding of this intriguing domain.

Don't miss the opportunity to dive into this rich resource and discover the hidden gems within duty-free item data. Download it now and start exploring!

📜 License Type: This dataset is shared under a "Proprietary" license. The data contained in this dataset is derived from an ERP system and may contain proprietary, confidential, or sensitive information.

🚫 Usage Restrictions: Users are granted permission to use this dataset solely for non-commercial, research, or analysis purposes. Any commercial use, distribution, or replication of the data is strictly prohibited.

https://upload.wikimedia.org/wikipedia/commons/thumb/a/a4/Mastercard_2019_logo.svg/195px-Mastercard_2019_logo.svg.png" alt="Mastercard">

Mastercard Inc. (stylized as MasterCard from 1979 to 2016 and MasterCard since 2016) is an American multinational financial services corporation headquartered in the Mastercard International Global Headquarters in Purchase, New York. The Global Operations Headquarters is located in O'Fallon, Missouri, a municipality of St. Charles County, Missouri. Throughout the world, its principal business is to process payments between the banks of merchants and the card-issuing banks or credit unions of the purchasers who use the "Mastercard" brand debit, credit, and prepaid cards to make purchases. Mastercard Worldwide has been a publicly traded company since 2006. Prior to its initial public offering, Mastercard Worldwide was a cooperative owned by the more than 25,000 financial institutions that issue its branded cards.

Mastercard, originally known as Interbank from 1966 to 1969 and Master Charge from 1969 to 1979, was created by an alliance of several regional bank card associations in response to the BankAmericard issued by Bank of America, which later became the Visa credit card issued by Visa Inc.

Mastercard is one of the best performing stocks of the decade of 2011-2020

This is the dataset of Kaggle Grandmasters by country and their rank on each category (created 04/30/2024)

This is created by using the Kaggle meta dataset.

This Dataset now updates Weekly.

The score is calculated by summing the user's rank following this values :

grandmaster = 8 master = 4 contributor = 2 expert = 1 novice = 0

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10030651%2F441fb5ff55fa17ce4b3bc5b4a2834e6e%2Fnotebook%20poicture-min.png?generation=1714567647651441&alt=media" alt="">

Dataset

This dataset was created by gcenachi

Contents

Dataset

This dataset was created by JohnHuff-CCSW

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by StillCler

Contents

Complete Kaggle Ranking competitions (Total = 10.809) at March-19-2023: (Downloaded by scraping using RSelenium)

Level 280 Grand Master / 1.934 Master / 8.596 Expert Countries(Total = 111) Total 111, TOP: US 1.716 / China 1.266 / Japan 1.196 / Russia 611 / India 536 Medals(Total = 59.115) Gold 6.630 / Silver 22.481 / Bronze 20.004 Continent Asia 4.406 / Europe 1.803 / America 1.783 South America(Total = 105) Brazil 76 / Argentina 12 / Colombia 5 / Chile 5 / Peru 4 / Venezuela 2 / Ecuador 1 Colombia(Total = 5) Best Ranking @omarvivas #3.385

The data contains ranks of Experts, Masters, and Grandmasters of the Discussion Tier. You can apply EDA to this data and see which country is having the highest-ranked Kaggle Users. Name: User name. Rank: User rank on the leaderboard. Level: User level whether a user is an expert, master, or grandmaster. Link: Profile link Gold: Total number of gold a user got. Silver: Total number of silver a user got. Bronze: Total number of bronze a user got. Points: Total number of points a user got. Joined: Year/Month joined Total Competitions: Total number of competitions a user participated in. Total Dataset: Total number of datasets a user uploaded. Total Codes: Total number of codes/notebooks a user uploaded. Total Discussion: Total number of discussions a user had. Highest Rank: Highest rank hit by a user. Current Rank: User's current rank. Current Level: Current Level represents whether a user's level is expert, master, or grandmaster in one of the four tiers. City: User's city. State: User's state. Country: User's country.

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 Tikadisplay

Released under Apache 2.0

Contents

This dataset contains the top performances from the 2023 USA Master's Outdoor Track and Field season. It focuses on elite athletes across all age groups, providing a comprehensive overview of the best performances in a wide range of track and field events. Each entry includes the event type, athlete's age category, name, gender, and their recorded performance.

This dataset is an invaluable resource for athletes, coaches, and researchers looking to analyze performance trends in master's track and field or benchmark against top-tier athletes. It can be used for statistical analysis, age group comparisons, and gender performance analysis across different events.

Fields

• event: The type of track or field event (e.g., 50m, 100m, long jump).
• gender/age: The gender of each athlete ('m' for men and 'w' for women) and the age category of the athlete (e.g., m30 for men aged 30-34). Age categories cover 5 year ranges (e.g., W55 is women aged 55-59).
• name: The full name of the athlete who achieved the performance.
• performance: The performance metric recorded for the event, typically measured in seconds (or minutes and seconds) for track events and meters for field events

Example notebook: https://www.kaggle.com/code/michaeldelamaza/relationship-long-jump-100m

Dataset

This dataset was created by ayoub chaoui

Contents

Dataset

This dataset was created by Matt B

Contents

Dataset

This dataset was created by Katie Li

Contents