Utilize our machine learning datasets to develop and validate your models. Our datasets are designed to support a variety of machine learning applications, from image recognition to natural language processing and recommendation systems. You can access a comprehensive dataset or tailor a subset to fit your specific requirements, using data from a combination of various sources and websites, including custom ones. Popular use cases include model training and validation, where the dataset can be used to ensure robust performance across different applications. Additionally, the dataset helps in algorithm benchmarking by providing extensive data to test and compare various machine learning algorithms, identifying the most effective ones for tasks such as fraud detection, sentiment analysis, and predictive maintenance. Furthermore, it supports feature engineering by allowing you to uncover significant data attributes, enhancing the predictive accuracy of your machine learning models for applications like customer segmentation, personalized marketing, and financial forecasting.

Sure! I'd be happy to provide you with an introduction to machine learning basics for beginners. Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. Here are some key concepts and terms to help you get started:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled training data. The training data consists of input examples and their corresponding correct output or target values. The algorithm learns to generalize from this data and make predictions or classify new, unseen examples.

2. Unsupervised Learning: Unsupervised learning involves learning patterns and relationships from unlabeled data. Unlike supervised learning, there are no target values provided. Instead, the algorithm aims to discover inherent structures or clusters in the data.

3. Training Data and Test Data: Machine learning models require a dataset to learn from. The dataset is typically split into two parts: the training data and the test data. The model learns from the training data, and the test data is used to evaluate its performance and generalization ability.

4. Features and Labels: In supervised learning, the input examples are often represented by features or attributes. For example, in a spam email classification task, features might include the presence of certain keywords or the length of the email. The corresponding output or target values are called labels, indicating the class or category to which the example belongs (e.g., spam or not spam).

5. Model Evaluation Metrics: To assess the performance of a machine learning model, various evaluation metrics are used. Common metrics include accuracy (the proportion of correctly predicted examples), precision (the proportion of true positives among all positive predictions), recall (the proportion of true positives predicted correctly), and F1 score (a combination of precision and recall).

6. Overfitting and Underfitting: Overfitting occurs when a model becomes too complex and learns to memorize the training data instead of generalizing well to unseen examples. On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. Balancing the complexity of the model is crucial to achieve good generalization.

7. Feature Engineering: Feature engineering involves selecting or creating relevant features that can help improve the performance of a machine learning model. It often requires domain knowledge and creativity to transform raw data into a suitable representation that captures the important information.

8. Bias and Variance Trade-off: The bias-variance trade-off is a fundamental concept in machine learning. Bias refers to the errors introduced by the model's assumptions and simplifications, while variance refers to the model's sensitivity to small fluctuations in the training data. Reducing bias may increase variance and vice versa. Finding the right balance is important for building a well-performing model.

9. Supervised Learning Algorithms: There are various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Each algorithm has its own strengths, weaknesses, and specific use cases.

10. Unsupervised Learning Algorithms: Unsupervised learning algorithms include clustering algorithms like k-means clustering and hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA) and t-SNE, and anomaly detection algorithms, among others.

These concepts provide a starting point for understanding the basics of machine learning. As you delve deeper, you can explore more advanced topics such as deep learning, reinforcement learning, and natural language processing. Remember to practice hands-on with real-world datasets to gain practical experience and further refine your skills.

Description

The Learning Path Index Dataset is a comprehensive collection of byte-sized courses and learning materials tailored for individuals eager to delve into the fields of Data Science, Machine Learning, and Artificial Intelligence (AI), making it an indispensable reference for students, professionals, and educators in the Data Science and AI communities.

This Kaggle Dataset along with the KaggleX Learning Path Index GitHub Repo were created by the mentors and mentees of Cohort 3 KaggleX BIPOC Mentorship Program (between August 2023 and November 2023, also see this). See Credits section at the bottom of the long description.

Inspiration

This dataset was created out of a commitment to facilitate learning and growth within the Data Science, Machine Learning, and AI communities. It started off as an idea at the end of Cohort 2 of the KaggleX BIPOC Mentorship Program brainstorming and feedback session. It was one of the ideas to create byte-sized learning material to help our KaggleX mentees learn things faster. It aspires to simplify the process of finding, evaluating, and selecting the most fitting educational resources.

Context

This dataset was meticulously curated to assist learners in navigating the vast landscape of Data Science, Machine Learning, and AI education. It serves as a compass for those aiming to develop their skills and expertise in these rapidly evolving fields.

The mentors and mentees communicated via Discord, Trello, Google Hangout, etc... to put together these artifacts and made them public for everyone to use and contribute back.

Sources

The dataset compiles data from a curated selection of reputable sources including leading educational platforms such as Google Developer, Google Cloud Skill Boost, IBM, Fast AI, etc. By drawing from these trusted sources, we ensure that the data is both accurate and pertinent. The raw data and other artifacts as a result of this exercise can be found on the GitHub Repo i.e. KaggleX Learning Path Index GitHub Repo.

Content

The dataset encompasses the following attributes:

• Course / Learning Material: The title of the Data Science, Machine Learning, or AI course or learning material.
• Source: The provider or institution offering the course.
• Course Level: The proficiency level, ranging from Beginner to Advanced.
• Type (Free or Paid): Indicates whether the course is available for free or requires payment.
• Module: Specific module or section within the course.
• Duration: The estimated time required to complete the module or course.
• Module / Sub-module Difficulty Level: The complexity level of the module or sub-module.
• Keywords / Tags / Skills / Interests / Categories: Relevant keywords, tags, or categories associated with the course with a focus on Data Science, Machine Learning, and AI.
• Links: Hyperlinks to access the course or learning material directly.

How to contribute to this initiative?

• You can also join us by taking part in the next KaggleX BIPOC Mentorship program (also see this)
• Keep your eyes open on the Kaggle Discussions page and other KaggleX social media channels. Or find us on the Kaggle Discord channel to learn more about the next steps
• Create notebooks from this data
• Create supplementary or complementary data for or from this dataset
• Submit corrections/enhancements or anything else to help improve this dataset so it has a wider use and purpose

License

The Learning Path Index Dataset is openly shared under a permissive license, allowing users to utilize the data for educational, analytical, and research purposes within the Data Science, Machine Learning, and AI domains. Feel free to fork the dataset and make it your own, we would be delighted if you contributed back to the dataset and/or our KaggleX Learning Path Index GitHub Repo as well.

Important Links

• KaggleX BIPOC Mentorship program (also see this)
• KaggleX Learning Path Index Dataset
• KaggleX Learning Path Index GitHub Repo
• New Official Kaggle Discord Server!

Credits

Credits for all the work done to create this Kaggle Dataset and the KaggleX [Learnin...

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

Overview

The data has been split into two groups:

• training set (train.csv)
• test set (test.csv)

The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.

We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.

Data Dictionary:

| Variable | Definition | Key | | --- | --- | | survival | Survival | 0 = No, 1 = Yes | | pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd | | sex | Sex | | | Age | Age in years | | | sibsp | # of siblings / spouses aboard the Titanic | | | parch | # of parents / children aboard the Titanic | | | ticket | Ticket number | | | fare | Passenger fare | | | cabin | Cabin number | | | embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |

Variable Notes

pclass: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5

sibsp: The dataset defines family relations in this way... Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored)

parch: The dataset defines family relations in this way... Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.

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This dataset is about book series. It has 1 row and is filtered where the books is Building machine learning systems with Python : master the art of machine learning with Python and build effective machine learning sytems with this intensive hands-on guide. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Dataset Description

Context and Methodology

• Research Domain/Project:
  This dataset was created for a machine learning experiment aimed at developing a classification model to predict outcomes based on a set of features. The primary research domain is disease prediction in patients. The dataset was used in the context of training, validating, and testing.

• Purpose of the Dataset:
  The purpose of this dataset is to provide training, validation, and testing data for the development of machine learning models. It includes labeled examples that help train classifiers to recognize patterns in the data and make predictions.

• Dataset Creation:
  Data preprocessing steps involved cleaning, normalization, and splitting the data into training, validation, and test sets. The data was carefully curated to ensure its quality and relevance to the problem at hand. For any missing values or outliers, appropriate handling techniques were applied (e.g., imputation, removal, etc.).

Technical Details

• Structure of the Dataset:
  The dataset consists of several files organized into folders by data type:

  • Training Data: Contains the training dataset used to train the machine learning model.

  • Validation Data: Used for hyperparameter tuning and model selection.

  • Test Data: Reserved for final model evaluation.

  Each folder contains files with consistent naming conventions for easy navigation, such as train_data.csv, validation_data.csv, and test_data.csv. Each file follows a tabular format with columns representing features and rows representing individual data points.

• Software Requirements:
  To open and work with this dataset, you need VS Code or Jupyter, which could include tools like:

  • Python (with libraries such as pandas, numpy, scikit-learn, matplotlib, etc.)

Further Details

• Reusability:
  Users of this dataset should be aware that it is designed for machine learning experiments involving classification tasks. The dataset is already split into training, validation, and test subsets. Any model trained with this dataset should be evaluated using the test set to ensure proper validation.

• Limitations:
  The dataset may not cover all edge cases, and it might have biases depending on the selection of data sources. It's important to consider these limitations when generalizing model results to real-world applications.

This dataset is about books. It has 1 row and is filtered where the book is Machine learning in Java : helpful techniques to design, build, and deploy powerful machine learning applications in Java. It features 7 columns including author, publication date, language, and book publisher.

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Project Machine Learning

## Overview

Project Machine Learning is a dataset for object detection tasks - it contains Deteksi Rempah Rempah annotations for 1,270 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The dataset is prepared and intended as a data source for development of a stress analysis method based on machine learning. It consists of finite element stress analyses of randomly generated mechanical structures. The dataset contains more than 270,794 pairs of stress analyses images (von Mises stress) of randomly generated 2D structures with predefined thickness and material properties. All the structures are fixed at their bottom edges and loaded with gravity force only. See PREVIEW directory with some examples. The zip file contains all the files in the dataset.

The SalmonScan dataset is a collection of images of salmon fish, including healthy fish and infected fish. The dataset consists of two classes of images:

Fresh salmon 🐟 Infected Salmon 🐠

This dataset is ideal for various computer vision tasks in machine learning and deep learning applications. Whether you are a researcher, developer, or student, the SalmonScan dataset offers a rich and diverse data source to support your projects and experiments.

So, dive in and explore the fascinating world of salmon health and disease!

The SalmonScan dataset (raw) consists of 24 fresh fish and 91 infected fish. [Due to server cleaning in the past, some raw datasets have been deleted]

The SalmonScan dataset (augmented) consists of approximately 1,208 images of salmon fish, classified into two classes:

• Fresh salmon (healthy fish with no visible signs of disease), 456 images
• Infected Salmon containing disease, 752 images

Each class contains a representative and diverse collection of images, capturing a range of different perspectives, scales, and lighting conditions. The images have been carefully curated to ensure that they are of high quality and suitable for use in a variety of computer vision tasks.

Data Preprocessing

The input images were preprocessed to enhance their quality and suitability for further analysis. The following steps were taken:

Resizing 📏: All the images were resized to a uniform size of 600 pixels in width and 250 pixels in height to ensure compatibility with the learning algorithm. Image Augmentation 📸: To overcome the small amount of images, various image augmentation techniques were applied to the input images. These included: Horizontal Flip ↩️: The images were horizontally flipped to create additional samples. Vertical Flip ⬆️: The images were vertically flipped to create additional samples. Rotation 🔄: The images were rotated to create additional samples. Cropping 🪓: A portion of the image was randomly cropped to create additional samples. Gaussian Noise 🌌: Gaussian noise was added to the images to create additional samples. Shearing 🌆: The images were sheared to create additional samples. Contrast Adjustment (Gamma) ⚖️: The gamma correction was applied to the images to adjust their contrast. Contrast Adjustment (Sigmoid) ⚖️: The sigmoid function was applied to the images to adjust their contrast.

Usage

To use the salmon scan dataset in your ML and DL projects, follow these steps:

• Clone or download the salmon scan dataset repository from GitHub.
• Use standard libraries such as numpy or pandas to convert the images into arrays, which can be input into a machine learning or deep learning model.
• Split the dataset into training, validation, and test sets as per your requirement.
• Preprocess the data as needed, such as resizing and normalizing the images.
• Train your ML/DL model using the preprocessed training data.
• Evaluate the model on the test set and make predictions on new, unseen data.

The final dataset utilised for the publication "Investigating Reinforcement Learning Approaches In Stock Market Trading" was processed by downloading and combining data from multiple reputable sources to suit the specific needs of this project. Raw data were retrieved by downloading them using a Python finance API. Afterwards, Python and NumPy were used to combine and normalise the data to create the final dataset.The raw data was sourced as follows:Stock Prices of NVIDIA & AMD, Financial Indexes, and Commodity Prices: Retrieved from Yahoo Finance.Economic Indicators: Collected from the US Federal Reserve.The dataset was normalised to minute intervals, and the stock prices were adjusted to account for stock splits.This dataset was used for exploring the application of reinforcement learning in stock market trading. After creating the dataset, it was used in s reinforcement learning environment to train several reinforcement learning algorithms, including deep Q-learning, policy networks, policy networks with baselines, actor-critic methods, and time series incorporation. The performance of these algorithms was then compared based on profit made and other financial evaluation metrics, to investigate the application of reinforcement learning algorithms in stock market trading.The attached 'README.txt' contains methodological information and a glossary of all the variables in the .csv file.

This repository contains a comprehensive collection of RNA multi-loops extracted from major RNA databases, along with benchmark results for various RNA design algorithms. The resource is intended to facilitate research and development in RNA design, particularly for multi-loop structures.

Dataset created for machine learning and deep learning training and teaching purposes.
Can for instance be used for classification, regression, and forecasting tasks.
Complex enough to demonstrate realistic issues such as overfitting and unbalanced data, while still remaining intuitively accessible.

ORIGINAL DATA TAKEN FROM:

EUROPEAN CLIMATE ASSESSMENT & DATASET (ECA&D), file created on 22-04-2021
THESE DATA CAN BE USED FREELY PROVIDED THAT THE FOLLOWING SOURCE IS ACKNOWLEDGED:

Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface
air temperature and precipitation series for the European Climate Assessment.
Int. J. of Climatol., 22, 1441-1453.
Data and metadata available at http://www.ecad.eu

For more information see metadata.txt file.

The Python code used to create the weather prediction dataset from the ECA&D data can be found on GitHub: https://github.com/florian-huber/weather_prediction_dataset
(this repository also contains Jupyter notebooks with teaching examples)

The following dataset has been used for the paper entitled "Design Analytics for Mobile Learning: Scaling up theClassification of Learning Designs based onCognitive and Contextual Elements".

Abstract

This research was triggered by the identified need in literature for large-scale studies about the kind of designs that teachers create for Mobile Learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analysing designs has been to manually classify them following high-level pedagogically-guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of Supervised Machine Learning (SML) to automatically classify the textual content of m-learning designs, through pedagogically-relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in them. As not all the SML models are transparent, while often researchers need to understand the behaviour behind them, the second goal of this paper considers the trade-off between models’ performance and interpretability in the context of design analytics for m-learning. To achieve these goals we compiled a dataset of designs deployed through two tools, Avastusrada and Smartzoos. With it, we trained and compared different models and feature extraction techniques. We further optimized andcompared the best-performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal through an illustrative case. We found that SML can reliably classify designs, with accuracy>0.86and Cohen’s kappa>0.69.

This dataset features over 330,000 high-quality interior design images sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a richly varied and extensively annotated collection of indoor environment visuals.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with object and scene detection metadata, making it ideal for tasks such as room classification, furniture detection, and spatial layout analysis. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities: the images are collected through a proprietary gamified platform for photographers. Competitions centered on interior design themes ensure a steady stream of fresh, high-quality submissions. Custom datasets can be sourced on-demand within 72 hours to fulfill specific requests, such as particular room types, design styles, or furnishings.

2. Global Diversity: photographs have been sourced from contributors in over 100 countries, covering a wide spectrum of architectural styles, cultural aesthetics, and functional spaces. The images include homes, offices, restaurants, studios, and public interiors—ranging from minimalist and modern to classic and eclectic designs.

3. High-Quality Imagery: the dataset includes standard to ultra-high-definition images that capture fine interior details. Both professionally staged and candid real-life spaces are included, offering versatility for training AI across design evaluation, object detection, and environmental understanding.

4. Popularity Scores: each image is assigned a popularity score based on its performance in GuruShots competitions. This provides valuable insights into global aesthetic trends, helping AI models learn user preferences, design appeal, and stylistic relevance.

5. AI-Ready Design: the dataset is optimized for machine learning tasks such as interior scene recognition, style transfer, virtual staging, and layout generation. It integrates smoothly with popular AI development environments and tools.

6. Licensing & Compliance: the dataset fully complies with data privacy regulations and includes transparent licensing suitable for commercial and academic use.

Use Cases: 1. Training AI for interior design recommendation engines and virtual staging tools. 2. Enhancing smart home applications and spatial recognition systems. 3. Powering AR/VR platforms for virtual tours, furniture placement, and room redesign. 4. Supporting architectural visualization, decor style transfer, and real estate marketing.

This dataset offers a comprehensive, high-quality resource tailored for AI-driven innovation in design, real estate, and spatial computing. Customizations are available upon request. Contact us to learn more!

The following dataset has been used for the paper entitled "Design Analytics for Mobile Learning: Scaling up theClassification of Learning Designs based onCognitive and Contextual Elements". Abstract This research was triggered by the identified need in literature for large-scale studies about the kind of designs that teachers create for Mobile Learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analysing designs has been to manually classify them following high-level pedagogically-guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of Supervised Machine Learning (SML) to automatically classify the textual content of m-learning designs, through pedagogically-relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in them. As not all the SML models are transparent, while often researchers need to understand the behaviour behind them, the second goal of this paper considers the trade-off between models’ performance and interpretability in the context of design analytics for m-learning. To achieve these goals we compiled a dataset of designs deployed through two tools, Avastusrada and Smartzoos. With it, we trained and compared different models and feature extraction techniques. We further optimized andcompared the best-performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal through an illustrative case. We found that SML can reliably classify designs, with accuracy>0.86and Cohen’s kappa>0.69.

Currency recognition and classification is one essential task to do. Both paper and coin currency play important role in transactions in everyday life. But provided there are many datasets available for paper currency, and very less datasets are available for coin currency. Coin currency recognition becomes important because even though the amount for which people do coin transactions is small but inaccuracy in recognition can lead to huge loss. Following are the objectives to create this dataset:

· Create a dataset of old and new Indian coin currency.

· Dataset of good quality images with properly fixed dimensions of 256*256 pixels.

· Dataset is in two formats, Yolo and PASCAL(VOC).

This image dataset consists of 1907 images, having 4 classes: Re 1 coin, Rs 2 coins, Rs 5 coins, and Rs 10 coins. The images were taken by the mobile phone’s rear camera. They were taken under different conditions like light differences, and different backgrounds, also each class has a different type of coin, for example, a 1re coin has 3 types of different coins based on its design and structure.