biology

The Cuff-Less Blood Pressure Estimation Dataset [2] from the UCI Machine Learning Repository. It is a subset of the MIMIC-II Waveform Dataset that contains 12000 records of simultaneous PPG and ABP from 942 patients with a sampling rate of 125 Hz. The 12000 records were uniformly split into four parts with 3000 records each. However, as the subject information is lacking, the Hold-one-out strategy was utilized to generate training, validation, and test sets once the data was preprocessed. In the end, the UCI dataset had 291,078 segments, which was around 404 hours of recording, making it substantially the biggest data set with a considerably higher ratio of continuous segments per record (32.15).

[2] Kachuee, M., Kiani, M. M., Mohammadzade, H. & Shabany, M. Cuff-less blood pressure estimation data set (2015). UCI repository https://archive.ics.uci.edu/ml/datasets/Cuff-Less+Blood+Pressure+Estimation.

TopMBAApplicants/uci-ml-repo dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by MD.Romzan Alom

Released under MIT

Contents

Basic information on 40 datasets from UCI repository used in this study including information about number of instances, attributes, classes, length of longest attribute name (LAN) and length of the longest nominal attribute value (LAV).

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the d

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

Dataset

This dataset was created by Nagaveda Reddy

Contents

About this Dataset

This CSV contain a data set prepared for the use of participants for the 1994 AAAI Spring Symposium on Artificial Intelligence in Medicine.

Content

Original files were obtained from: https://archive.ics.uci.edu/ml/datasets/diabetes

Archived file diabetes-data.tar.z which contains 70 sets of data recorded on diabetes patients (several weeks' to months' worth of glucose, insulin, and lifestyle data per patient + a description of the problem domain) is extracted and processed and merged as a CSV file.

The Code field of the CSV is deciphered as follows:

33 = Regular insulin dose 34 = NPH insulin dose 35 = UltraLente insulin dose 48 = Unspecified blood glucose measurement 57 = Unspecified blood glucose measurement 58 = Pre-breakfast blood glucose measurement 59 = Post-breakfast blood glucose measurement 60 = Pre-lunch blood glucose measurement 61 = Post-lunch blood glucose measurement 62 = Pre-supper blood glucose measurement 63 = Post-supper blood glucose measurement 64 = Pre-snack blood glucose measurement 65 = Hypoglycemic symptoms 66 = Typical meal ingestion 67 = More-than-usual meal ingestion 68 = Less-than-usual meal ingestion 69 = Typical exercise activity 70 = More-than-usual exercise activity 71 = Less-than-usual exercise activity 72 = Unspecified special event

Datasets from Scikit-learn are: ‘Iris’, ‘Wine’, ‘Breast Cancer Wisconsin (Diagnostic)’. Datasets from UCI repository are: ‘Seeds’ ‘Banknote Authentication’ (‘Banknotes’), ‘Heart disease’ ‘ Parkinsons ‘, ‘Ecoli’, ‘Thyroid (Thyroid gland data)’

Facebook social network - A social friendship network extracted from Facebook consisting of people (nodes) with edges representing friendship ties.

Datasets available at UCI Machine Learning Repository and other repositories. List of datasets used in the experiment with their sources. ForestCover dataset @ https://archive.ics.uci.edu/ml/datasets/Covertype KDD Cup99 dataset @ https://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data PAMAP dataset @ https://archive.ics.uci.edu/ml/datasets/PAMAP2+Physical+Activity+Monitoring Powersupply @ http://www.cse.fau.edu/~xqzhu/stream.html SEA @ http://www.liaad.up.pt/kdus/products/datasets-for-concept-drift Syn002 & Syn003 (generated) @ http://moa.cms.waikato.ac.nz/details/classification/streams/ MNIST @ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html News20 @ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html

Dataset

This dataset was created by MayankDubey

Contents

Imbalanced dataset for benchmarking

The different algorithms of the imbalanced-learn toolbox are evaluated on a set of common dataset, which are more or less balanced. These benchmark have been proposed in [1]. The following section presents the main characteristics of this benchmark.

Characteristics

References

[1] Ding, Zejin, "Diversified Ensemble Classifiers for H ighly Imbalanced Data Learning and their Application in Bioinformatics." Dissertation, Georgia State University, (2011).

[2] Blake, Catherine, and Christopher J. Merz. "UCI Repository of machine learning databases." (1998).

[3] Chang, Chih-Chung, and Chih-Jen Lin. "LIBSVM: a library for support vector machines." ACM Transactions on Intelligent Systems and Technology (TIST) 2.3 (2011): 27.

[4] Caruana, Rich, Thorsten Joachims, and Lars Backstrom. "KDD-Cup 2004: results and analysis." ACM SIGKDD Explorations Newsletter 6.2 (2004): 95-108.

Two preprocessed datasets collected from the UCI repository that can be used for the purpose of structure learning from multivariate data of different types.

Air Quality

This dataset represents hourly averaged measurements of 5 metal oxide chemical sensors embedded in an air quality chemical multisensor device. The certified analyzer was located on the field in a significantly polluted area, at road level, within an Italian city. Data were recorded from March 2004 to February 2005 (one year), representing the longest freely available recordings of on-field deployed air quality chemical sensor device responses [1]. More information about the attributes and their type can be found in airqualitydataset_description.html.

Size of dataset: 9358
Number of Features: 16
Type of data: discrete and continuous
Ground Truth: No

Contains the responses of a gas multisensor device deployed on the field in an Italian city. Hourly responses averages are recorded along with gas concentrations references from a certified analyzer. There are 15 attributes. Date and Time as well as discrete and real covariates.

0 Date (DD/MM/YYYY)
1 Time (HH.MM.SS)
2 True hourly averaged concentration CO in mg/m^3 (reference analyzer)
3 PT08.S1 (tin oxide) hourly averaged sensor response (nominally CO targeted)
4 True hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer)
5 True hourly averaged Benzene concentration in microg/m^3 (reference analyzer)
6 PT08.S2 (titania) hourly averaged sensor response (nominally NMHC targeted)
7 True hourly averaged NOx concentration in ppb (reference analyzer)
8 PT08.S3 (tungsten oxide) hourly averaged sensor response (nominally NOx targeted)
9 True hourly averaged NO2 concentration in microg/m^3 (reference analyzer)
10 PT08.S4 (tungsten oxide) hourly averaged sensor response (nominally NO2 targeted)
11 PT08.S5 (indium oxide) hourly averaged sensor response (nominally O3 targeted)
12 Temperature in °C
13 Relative Humidity (%)
14 AH Absolute Humidity

US Census (1990)

This dataset is a discretized version of the USCensus1990raw dataset. The data was collected as part of the 1990 census, and it describes one percent sample of the Public Use Microdata Samples (PUMS) person records drawn from the full 1990 census sample (all fifty states and the District of Columbia but not including "PUMA Cross State Lines One Percent Persons Records") [2]. More information about the attributes and their type can be found in census1990_description.html.

Size of dataset: 2458285
Number of features: 68
Ground truth: No

References:

[1] S. De Vito and E. Massera and M. Piga and L. Martinotto and G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005 https://doi.org/10.1016/j.snb.2007.09.060

[2] Meek, Thiesson and Heckerman (2001), "The Learning Curve Method Applied to Clustering",The Journal of Machine Learning Research. (Also see MSR-TR-2001-34 available athttps://www.microsoft.com/en-us/research/wp-content/uploads/2001/01/lc-aistats.pdf)

This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN. - Class 1: Normal Day - Class 2: Game Day There is nothing to infer from the order of examples in the train and test set. Missing values are represented with NaN in the text file. Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh

Dataset Card for Online Shoppers Purchasing Intention Dataset

  Dataset Summary

This dataset is a reupload of the Online Shoppers Purchasing Intention Dataset from the UCI Machine Learning Repository.

NOTE: The information below is from the original dataset description from UCI's website.

  Overview

Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples… See the full description on the dataset page: https://huggingface.co/datasets/jlh/uci-shopper.

Context

This notebook was created for analysis and prediction making of the Default of credit card clients Data Set from UCI Machine Learning Library. The data set can be accessed separately from the UCI Machine Learning Repository page, here.

Content

In their paper "The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. (Yeh I. C. & Lien C. H.,2009)", which can be found here, Yeh I. C. & Lien C. H. review six data mining techniques (discriminant analysis, logistic regression, Bayesclassifier, nearest neighbor, artificial neural networks, and classification trees) and their applications on credit scoring. Then, using the real cardholders’ credit risk data in Taiwan, they compare the classification accuracy among them.

Models

We will create 3 models in order to make predictions and compare them with the original paper. These models are: - Logistic Regression - Decision tree - Neural Network

After the initial predictions, each model will be "optimized" by GridSearchCV estimator, which will search for the best set of hyperparameters for every model.

Goal

Using the models we created, we will try to predict the class value of dpnm column with better scores (accuracy and f1) than the scores presented in the original paper.

Context

The dataset used can be found on the UCI Machine Learning Repository at the following location:

Heart Disease Dataset

There are several copies of this dataset to be found on Kaggle, with people focusing on different types of analyses of the data. This specific copy can be analysed by anyone interested, but is primarily used by a study group from the Udacity Bertelsmann Technology Scholarship to practice analysis of association between variables as well as implementation and comparison of various Machine Learning models.

Content

According to the paper by (Detrano et al., 1989) as found on the UCI Dataset webpage, the data represents data collected for 303 patients referred for coronary angiography at the Cleveland Clinic between May 1981 and September 1984. The 13 independent/ features variables can be divided into 3 groups as follows:

Routine evaluation (based on historical data):

• ECG at rest
• Serum Cholesterol
• Fasting blood sugar

Non-invasive test data (informed consent obtained for data as part of research protocol):

• Exercise ECG
  • ST-segment peak slope (upsloping, flat or downsloping)
  • ST-segment depression
• Excercise Thallium scintigraphy (fixed, reversible or none)
• Cardiac fluoroscopy (number of vessels appeared to contain calcium)

Other demographic and clinical variables (based on routine data):

• Age
• Sex
• Chest pain type
• Systolic blood pressure
• ST-T-wave abnormality (T-wave abnormality)
• Probably or definite ventricular hypertrophy (Este's criteria)
• The dependent/ response variable was the angiographic test result indicating a >50% diameter narrowing.

Data Dictionary

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3632459%2Fa01747fb0158dc51c12bc0824c9c4ae4%2Fdata_dictionary2.png?generation=1609522473018549&alt=media" alt="">

Acknowledgements

UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Donor:

David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Inspiration

The objective of the analysis is to use statistical learning to identify factors associated with Coronary Artery Disease as indicated by a coronary angiography interpreted by a Cardiologist (as per paper written by Detrano et al cited before).

Details of the datasets from UCI repository used in the experiments.