Dataset

This dataset was created by Regi

Contents

Enriching the Meta-Kaggle dataset using the Meta Kaggle Code to extract all Imports (for both R and Python) and Method Calls (only Python) as lists, which are then added to the KernelVersions.csv file as the columns Imports and MethodCalls.

Most Imported R Packages	Most Imported Python Packages
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17421843%2F5bb95536aa5d8092d56f526aa04c8cd1%2Foutput.png?generation=1749374431744993&alt=media" alt="">	https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17421843%2Fa3d9a02ae0b314bfa6b3eb411c405ec0%2Foutput1.png?generation=1749374439690291&alt=media" alt="">

We perform this extraction using the following three regex patterns:

PYTHON_IMPORT_REGEX = re.compile(r'(?:from\s+([a-zA-Z0-9_\.]+)\s+import|import\s+([a-zA-Z0-9_\.]+))')
PYTHON_METHOD_REGEX = *I wish I could add the regex here but kaggle kinda breaks if I do lol*
R_IMPORT_REGEX = re.compile(r'(?:library|require)\((?:[\'"]?)([a-zA-Z0-9_.]+)(?:[\'"]?)\)')

This dataset was created on 06-06-2025. Since the computation required for this process is very resource-intensive and cannot be run on a Kaggle kernel, it is not scheduled. A notebook demonstrating how to create this dataset and what insights it provides can be found here.

Dataset

This dataset was created by Alexander Ryzhkov

Contents

This dataset contains the python files containing snippets required for the Kaggle kernel - https://www.kaggle.com/code/adeepak7/tensorflow-s-global-and-operation-level-seeds/

Since the kernel is around setting/re-setting global and local level seeds, the nullification of the effect of these seeds in the subsequent cells wasn't possible. Hence, the snippets have been provided as separate python files and these python files are executed independently in the separate cells.

This dataset is designed to support research in anomaly detection for OS kernels, particularly in the context of power monitoring systems used in embedded environments. It simulates the interaction between system-level operations and power consumption behaviors, providing a rich set of features for training and evaluating hybrid models.

The dataset contains 1,000 records of yet realistic system behavior, including:

System call sequences

Power usage logs (in watts)

CPU and memory utilization

Process identifiers and names

Timestamps

Labeled entries (Normal or Anomaly)

Anomalies are injected using fuzzy testing principles to simulate abnormal power spikes, syscall irregularities, or excessive resource usage, mimicking real-world kernel faults or malicious activity. This dataset enables the development of robust models that can learn complex, uncertain system behavior patterns for enhanced security and stability of embedded power monitoring applications.

Context

The reason I did this was because I wanted to know if there was a correlation between Kaggles' top Kernels and Datasets with its popularity. (wanted to know how to get tops, lol). I scrapped the data using DataMiner

Content

top-kernels has:

• Name. name
• Upvotes. upvotes
• Language. language used
• Comments. amount comments
• IsNotebook. is it a notebook or a script
• visuals. how many visuals?

top-datasets has:

• Name. name
• upvotes. number of upvotes
• downloads. number of downloads
• comments. number of comments
• author. author name
• updated. last updated
• description. dataset description

Go kaggle team

Dataset

This dataset was created by Salil Gautam

Contents

Dataset

This dataset was created by Maksim Filin

Contents

Data Set Information:

The dataset comprised wheat kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each. The data set can be used for the tasks of classification and cluster analysis.All of these parameters were real-valued continuous

Attribute Information:

To construct the data, seven geometric parameters of wheat kernels were measured:

1. area A,
2. perimeter P,
3. compactness C = 4*pi*A/P^2,
4. length of kernel,
5. width of kernel,
6. asymmetry coefficient
7. length of kernel groove.

Dataset

This dataset was created by Gautier

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by joeland209

Contents

haha

Dataset

This dataset was created by deeplearner

Contents

Dataset

This dataset was created by Justin Chae

Contents

Dataset

This dataset was created by ZykoTsai

Contents

Dataset

This dataset was created by Lavanya Shukla

Contents

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12065493%2Fefa5252a24bf0bdd393156ad5778ed02%2Fkernel_dataclass.png?generation=1669324775217833&alt=media" alt="">

All in all, in the years 2017-2022 1822 kernels used the Kaggle Survey datasets. We have ordered our data into several distinct datasets, each of which was useful in obtaining answers to our questions on at least one of the topics. The obtained datasets are briefly overviewed below.

• notebooks.zip

  Contains 1822 raw notebooks saved as either ipynb or Rmd. 58 notebooks could not be executed neither in Python nor in R, so they were given the extension unknown_format.txt. The name of each file is the notebook_id as listed on kaggle.com and matches notebook_id in the file all_kernels.csv, which is described below. Among other things, this dataset was used to obtain a per-notebook list of imported libraries, as well as the questions that were addressed by each notebook.

• all_kernels.csv

  Each row of this dataset contains data about one of the 1822 kernels. The columns correspond to all the fields listed in the Kernel class image above. A more detailed overview of the columns can be found on the dataset's Kaggle page. # TODO

• cleaned_kernels.csv

  This is in effect the main dataset we used in our competition notebook. We took all_kernels.csv and removed from it 233 rows which described kernels which were just unchanged forks of other kernels.

• all_questions.json

  Contains all Kaggle Survey questions from the years 2017-2022. In the year 2017, the survey questions were unnumbered, so we numbered them ourselves, keeping the original order and using zero-based indexing. Surveys 2018-2022 have numbered questions, so the index was taken unchanged.

• question_map.csv

  Looking at survey questions over several years, one can note that certain questions repeat. For example, every year's survey contains a question What is your age. All such repetitions are captured in this dataset. For each unique question, the question number and the survey year where this question appears is given. The question numbers are described in the preceding paragraph sorted_questions_all.json. Certain questions are worded differently but functionally identical. If such questions were joined, a note was added, to alert other users of this dataset.

Dataset

This dataset was created by Abraham Anderson

Contents

Open Machine Learning Course mlcourse.ai is designed to perfectly balance theory and practice; therefore, each topic is followed by an assignment with a deadline in a week. You can also take part in several Kaggle Inclass competitions held during the course and write your own tutorials. The next session launches in September, 2019. For more info go to the mlcourse.ai main page. Outline This is the list of published articles on medium.com (English), habr.com (Russian), and jqr.com (Chinese). See Kernels of this Dataset for the same material in English. 1. Exploratory Data Analysis with Pandas uk ru, cn, Kaggle Kernel 2. Visual Data Analysis with Python uk ru, cn, Kaggle Kernels: part1, part2 3. Classification, Decision Trees and k Nearest Neighbors uk, ru, cn, Kaggle Kernel 4. Linear Classification and Regression uk, ru, cn, Kaggle Kernels: part1, part2, part3, part4, part5 5. Bagging and Random Forest uk, ru, cn, Kaggle Kernels: part1, part2, part3 6. Feature Engineering and Feature Selection uk, ru, cn, Kaggle Kernel 7. Unsupervised Learning: Principal Component Analysis and Clustering uk, ru, cn, Kaggle Kernel 8. Vowpal Wabbit: Learning with Gigabytes of Data uk, ru, cn, Kaggle Kernel 9. Time Series Analysis with Python, part 1 uk, ru, cn. Predicting future with Facebook Prophet, part 2 uk, cn Kaggle Kernels: part1, part2 10. Gradient Boosting uk, ru, cn, Kaggle Kernel Assignments Each topic is followed by an assignment. See demo versions in this Dataset. Solutions will be discussed in the upcoming run of the course. Kaggle competitions 1. Catch Me If You Can: Intruder Detection through Webpage Session Tracking. Kaggle Inclass 2. How good is your Medium article? Kaggle Inclass Rating Throughout the course we are maintaining a student rating. It takes into account credits scored in assignments and Kaggle competitions. Top students (according to the final rating) will be listed on a special Wiki page. Community Discussions between students are held in the #mlcourse_ai channel of the OpenDataScience Slack team. A registration form will be shared prior to the start of the new session Collaboration You can publish Kernels using this Dataset. But please respect others' interests: don't share solutions to assignments and well-performing solutions for Kaggle Inclass competitions. If you notice any typos/errors in course material, please open an Issue or make a pull request in the course repo. The course is free but you can support organizers by making a pledge on Patreon (monthly support) or a one-time payment on Ko-fi

Dataset

This dataset was created by Darien Schettler

Released under CC0: Public Domain

Contents

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

Column	Description
code_blocks_index	Global index linking code blocks to markup_data.csv.
kernel_id	Identifier for the Kaggle Jupyter notebook from which the code block was extracted.
code_block_id	Position of the code block within the notebook.
code_block	The actual machine learning code snippet.

Table 2. kernels_meta.csv structure

Column	Description
kernel_id	Identifier for the Kaggle Jupyter notebook.
kaggle_score	Performance metric of the notebook.
kaggle_comments	Number of comments on the notebook.
kaggle_upvotes	Number of upvotes the notebook received.
kernel_link	URL to the notebook.
comp_name	Name of the associated Kaggle competition.

Table 3. competitions_meta.csv structure

Column	Description
comp_name	Name of the Kaggle competition.
description	Overview of the competition task.
data_type	Type of data used in the competition.
comp_type	Classification of the competition.
subtitle	Short description of the task.
EvaluationAlgorithmAbbreviation	Metric used for assessing competition submissions.
data_sources	Links to datasets used.
metric type	Class label for the assessment metric.

Table 4. markup_data.csv structure

Column	Description
code_block	Machine learning code block.
too_long	Flag indicating whether the block spans multiple semantic types.
marks	Confidence level of the annotation.
graph_vertex_id	ID of the semantic type.

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