The dataset has been introduced by the below-mentioned researches: E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, A. A. Ghorbani. "CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment," Sensor (2023) – (submitted to Journal of Sensors). The present data contains different kinds of IoT intrusions. The categories of the IoT intrusions enlisted in the data are as follows: DDoS Brute Force Spoofing DoS Recon Web-based Mirai

There are several subcategories are present in the data for each kind of intrusion types in the IoT. The dataset contains 1191264 instances of network for intrusions and 47 features of each of the intrusions. The dataset can be used to prepare the predictive model through which different kind of intrusive attacks can be detected. The data is also suitable for designing the IDS system.

This dataset is an extension of my previous work on creating a dataset for natural language processing tasks. It leverages binary representation to characterise various machine learning models. The attributes in the dataset are derived from a dictionary, which was constructed from a corpus of prompts typically provided to a large language model (LLM). These prompts reference specific machine learning algorithms and their implementations. For instance, consider a user asking an LLM or a generative AI to create a Multi-Layer Perceptron (MLP) model for a particular application. By applying this concept to multiple machine learning models, we constructed our corpus. This corpus was then transformed into the current dataset using a bag-of-words approach. In this dataset, each attribute corresponds to a word from our dictionary, represented as a binary value: 1 indicates the presence of the word in a given prompt, and 0 indicates its absence. At the end of each entry, there is a label. Each entry in the dataset pertains to a single class, where each class represents a distinct machine learning model or algorithm. This dataset is intended for multi-class classification tasks, not multi-label classification, as each entry is associated with only one label and does not belong to multiple labels simultaneously. This dataset has been utilised with a Convolutional Neural Network (CNN) using the Keras Automodel API, achieving impressive training and testing accuracy rates exceeding 97%. Post-training, the model's predictive performance was rigorously evaluated in a production environment, where it continued to demonstrate exceptional accuracy. For this evaluation, we employed a series of questions, which are listed below. These questions were intentionally designed to be similar to ensure that the model can effectively distinguish between different machine learning models, even when the prompts are closely related.

KNN How would you create a KNN model to classify emails as spam or not spam based on their content and metadata? How could you implement a KNN model to classify handwritten digits using the MNIST dataset? How would you use a KNN approach to build a recommendation system for suggesting movies to users based on their ratings and preferences? How could you employ a KNN algorithm to predict the price of a house based on features such as its location, size, and number of bedrooms etc? Can you create a KNN model for classifying different species of flowers based on their petal length, petal width, sepal length, and sepal width? How would you utilise a KNN model to predict the sentiment (positive, negative, or neutral) of text reviews or comments? Can you create a KNN model for me that could be used in malware classification? Can you make me a KNN model that can detect a network intrusion when looking at encrypted network traffic? Can you make a KNN model that would predict the stock price of a given stock for the next week? Can you create a KNN model that could be used to detect malware when using a dataset relating to certain permissions a piece of software may have access to?

Decision Tree Can you describe the steps involved in building a decision tree model to classify medical images as malignant or benign for cancer diagnosis and return a model for me? How can you utilise a decision tree approach to develop a model for classifying news articles into different categories (e.g., politics, sports, entertainment) based on their textual content? What approach would you take to create a decision tree model for recommending personalised university courses to students based on their academic strengths and weaknesses? Can you describe how to create a decision tree model for identifying potential fraud in financial transactions based on transaction history, user behaviour, and other relevant data? In what ways might you apply a decision tree model to classify customer complaints into different categories determining the severity of language used? Can you create a decision tree classifier for me? Can you make me a decision tree model that will help me determine the best course of action across a given set of strategies? Can you create a decision tree model for me that can recommend certain cars to customers based on their preferences and budget? How can you make a decision tree model that will predict the movement of star constellations in the sky based on data provided by the NASA website? How do I create a decision tree for time-series forecasting?

Random Forest Can you describe the steps involved in building a random forest model to classify different types of anomalies in network traffic data for cybersecurity purposes and return the code for me? In what ways could you implement a random forest model to predict the severity of traffic congestion in urban areas based on historical traffic patterns, weather...

Analysis of ‘Deep Learning A-Z - ANN dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/filippoo/deep-learning-az-ann on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is the dataset used in the section "ANN (Artificial Neural Networks)" of the Udemy course from Kirill Eremenko (Data Scientist & Forex Systems Expert) and Hadelin de Ponteves (Data Scientist), called Deep Learning A-Z™: Hands-On Artificial Neural Networks. The dataset is very useful for beginners of Machine Learning, and a simple playground where to compare several techniques/skills.

It can be freely downloaded here: https://www.superdatascience.com/deep-learning/

The story: A bank is investigating a very high rate of customer leaving the bank. Here is a 10.000 records dataset to investigate and predict which of the customers are more likely to leave the bank soon.

The story of the story: I'd like to compare several techniques (better if not alone, and with the experience of several Kaggle users) to improve my basic knowledge on Machine Learning.

Content

I will write more later, but the columns names are very self-explaining.

Acknowledgements

Udemy instructors Kirill Eremenko (Data Scientist & Forex Systems Expert) and Hadelin de Ponteves (Data Scientist), and their efforts to provide this dataset to their students.

Inspiration

Which methods score best with this dataset? Which are fastest (or, executable in a decent time)? Which are the basic steps with such a simple dataset, very useful to beginners?

--- Original source retains full ownership of the source dataset ---

A BitTorrent file to download data with the title 'Udemy - Machine Learning A-Z Become Kaggle Master'

The BDG2 open data set consists of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters are collected from 19 sites across North America and Europe, and they measure electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. This subset includes data from 2,380 meters from 1,448 buildings that were used in the GEPIII, a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

Here is the dataset for classifying the different classes of traffic signs. There are around 58 classes and each class has around 120 images. the labels.csv file has the respective description of the traffic sign class. You can change the assignment of these classIDs with descriptions. We can use the basic CNN model to get decent val accuracy. We have around 2000 files for testing.

You can view the notebook named official in the code section to train and test basic cnn model.

Please upvote the notebook and dataset if you like this.

In this paper, we introduce a novel benchmarking framework designed specifically for evaluations of data science agents. Our contributions are three-fold. First, we propose DSEval, an evaluation paradigm that enlarges the evaluation scope to the full lifecycle of LLM-based data science agents. We also cover aspects including but not limited to the quality of the derived analytical solutions or machine learning models, as well as potential side effects such as unintentional changes to the original data. Second, we incorporate a novel bootstrapped annotation process letting LLM themselves generate and annotate the benchmarks with ``human in the loop''. A novel language (i.e., DSEAL) has been proposed and the derived four benchmarks have significantly improved the benchmark scalability and coverage, with largely reduced human labor. Third, based on DSEval and the four benchmarks, we conduct a comprehensive evaluation of various data science agents from different aspects. Our findings reveal the common challenges and limitations of the current works, providing useful insights and shedding light on future research on LLM-based data science agents.

This is one of DSEval benchmarks.

Dataset

This dataset was created by Jay2K

Contents

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition 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

The community-driven model service platform market is experiencing robust growth, projected to reach $35.14 billion in 2025 and expanding at a compound annual growth rate (CAGR) of 10.1% from 2025 to 2033. This surge is driven by several key factors. The increasing accessibility of machine learning models, fueled by platforms like Kaggle, GitHub, and Hugging Face, is lowering the barrier to entry for developers and researchers. The collaborative nature of these platforms fosters innovation and accelerates model development, leading to a wider adoption of AI solutions across various industries. Furthermore, the growing demand for specialized and customized AI models is pushing businesses to leverage community-driven platforms, where they can find pre-trained models or collaborate on developing tailored solutions, thereby reducing development time and costs. The trend towards open-source models and the rise of model zoos contribute significantly to this market expansion. While challenges exist, such as ensuring model quality, security, and addressing potential biases, the overall market trajectory remains strongly positive. The market's segmentation likely includes various model types (e.g., image recognition, natural language processing, time series analysis), deployment options (cloud-based, on-premise), and target industries (healthcare, finance, retail). Leading players, such as Kaggle, GitHub, Hugging Face, TensorFlow Hub, Model Zoo, DrivenData, and Cortex, are actively shaping the market landscape through continuous innovation and community engagement. The geographical distribution of the market is likely to reflect the global concentration of AI expertise and technological infrastructure, with regions like North America and Europe holding significant market shares initially, followed by rapid expansion in Asia and other developing regions as digital infrastructure improves. Future growth will hinge on continued technological advancements, further integration with cloud platforms, and the development of robust governance frameworks to address ethical concerns surrounding AI model development and deployment.

This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 1,000+ images of underpass images from multiple cities. These images captured and crowdsourced from over 200+ different locations, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. It contains a wide variety of Kitchen images. This dataset can be used scene classification and domestic object detection.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Dataset Features

• Dataset size : 1000+ images
• Captured by : Over 200+ crowdsource contributors
• Resolution : HD and above (1920x1080 and above)
• Location : Captured with 200+ locations
• Diversity : Various lighting conditions like day, night, varied distances, view points etc.
• Device used : Captured using mobile phones in 2020-2022
• Usage : Identifying underpass on the roads, scene understanding and for autonomous driving vehicles.

Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

A company has a fleet of devices transmitting daily sensor readings. They would like to create a predictive maintenance solution to proactively identify when maintenance should be performed. This approach promises cost savings over routine or time based preventive maintenance, because tasks are performed only when warranted.

The task is to build a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to Predict is called failure with binary value 0 for non-failure and 1 for failure.

Bolt joints are very common and important in engineering structures. Due to extreme service environment and load factors, bolts often get loose or even disengaged. To real-time or timely detect the loosed or disengaged bolts is an urgent need in practical engineering, which is critical to keep structural safety and service life. In recent years, many bolt loosening detection methods using deep learning and machine learning techniques have been proposed and are attracting more and more attention. However, most of these studies use bolt images captured in laboratory for deep leaning model training. The images are obtained in a well-controlled light, distance, and view angle conditions. Also, the bolted structures are well designed experimental structures with brand new bolts and the bolts are exposed without any shelter nearby. It is noted that in practical engineering, the above well controlled lab conditions are not easy realized and the real bolt images often have blur edges, oblique perspective, partial occlusion and indistinguishable colors etc., which make the trained models obtained in laboratory conditions loss their accuracy or fails. Therefore, the aim of this study is to develop a dataset named NPU-BOLT for bolt object detection in natural scene images and open it to researchers for public use and further development. In the first version of the dataset, it contains 337 samples of bolt joints images mainly in the natural environment, with image data sizes ranging from 400*400 to 6000*4000, totaling approximately 1275 bolt targets. The bolt targets are annotated into four categories named blur bolt, bolt head, bolt nut and bolt side. The dataset is tested with advanced object detection models including yolov5, Faster-RCNN and CenterNet. The evaluation results show that the bolt target detection model trained using this dataset can well locate and classify the bolt head and bolt nut in natural environment. In the yolov5-l model, the average precision of the two main categories reach 97.38% and 91.88%, respectively. The proposed dataset bridges the gap in the current field of bolt object detection. Meanwhile, it is welcomed the related researchers supplement and improve the dataset in the future.

Dataset

This dataset was created by Rishabh

Contents

Exercise: Machine Learning Competitions

When you click on Run / All, the notebook will give you an error: "Files doesn't exist" With this DataSet you fix that. It's the same from DanB. Please UPVOTE!

Enjoy!

The dataset contains a diverse range of examples, including classification, regression, clustering, and dimensionality reduction problems, with varying levels of complexity and varying numbers of features. Each dataset comes with a detailed description of the problem and the corresponding features, making it easy to understand and work with. Additionally, the dataset provides an opportunity for machine learning enthusiasts to experiment with different SkLearn algorithms and evaluate their performance on different datasets. This dataset is perfect for both beginners and advanced practitioners looking to hone their skills in various machine learning techniques.

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 Aayush Sharma

Contents

Dataset

This dataset was created by abdullah525

Released under Apache 2.0

Contents