From original source: ----- Additional Information Information about customers consists of 86 variables and includes product usage data and socio-demographic data derived from zip area codes. The data…
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9822 instances - 86 features - 2 classes - 0 missing values
From original source: ----- Context "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets] Content…
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7043 instances - 20 features - 2 classes - 11 missing values
From original source: ----- Additional Information The dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the…
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243 instances - 14 features - 2 classes - 0 missing values
tester
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536 instances - 1 features - classes - 0 missing values
Fuel Datasets in India States
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41406 instances - 6 features - classes - 24 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8218, f_measure: 0.7515, kappa: 0.4469, kb_relative_information_score: 0.3057, mean_absolute_error: 0.3204, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7552, number_of_instances: 768, precision: 0.7503, predictive_accuracy: 0.7552, prior_entropy: 0.9331, relative_absolute_error: 0.7051, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4019, root_relative_squared_error: 0.8433, unweighted_recall: 0.7176,
Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm selection problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 4 classes - 0 missing values
Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 0 classes - 0 missing values
Algorithm selection problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC.
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1120 instances - 46 features - 5 classes - 0 missing values
The dataset includes several weather parameter and and information about campsite utilisation in germany. The data is available on a montly basis and separated by the german bundeslander. The data…
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4255 instances - 36 features - classes - 3334 missing values
daily pickup data for 329 FHV companies from January 2015 through August 2015. From original source: ----- There is also a file other-FHV-data-jan-aug-2015.csv containing daily pickup data for 329 FHV…
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29826 instances - 5 features - classes - 9276 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8245, f_measure: 0.7636, kappa: 0.4726, kb_relative_information_score: 0.3104, mean_absolute_error: 0.318, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7682, number_of_instances: 768, precision: 0.7631, predictive_accuracy: 0.7682, prior_entropy: 0.9331, relative_absolute_error: 0.6997, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4006, root_relative_squared_error: 0.8405, unweighted_recall: 0.7285,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8267, f_measure: 0.771, kappa: 0.4885, kb_relative_information_score: 0.3145, mean_absolute_error: 0.3165, mean_prior_absolute_error: 0.4545, weighted_recall: 0.776, number_of_instances: 768, precision: 0.7711, predictive_accuracy: 0.776, prior_entropy: 0.9331, relative_absolute_error: 0.6965, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3991, root_relative_squared_error: 0.8373, unweighted_recall: 0.7354,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8242, f_measure: 0.7686, kappa: 0.4847, kb_relative_information_score: 0.3092, mean_absolute_error: 0.3191, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7721, number_of_instances: 768, precision: 0.7677, predictive_accuracy: 0.7721, prior_entropy: 0.9331, relative_absolute_error: 0.7022, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4007, root_relative_squared_error: 0.8407, unweighted_recall: 0.7358,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8315, f_measure: 0.761, kappa: 0.4666, kb_relative_information_score: 0.3146, mean_absolute_error: 0.3166, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7656, number_of_instances: 768, precision: 0.7604, predictive_accuracy: 0.7656, prior_entropy: 0.9331, relative_absolute_error: 0.6966, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.397, root_relative_squared_error: 0.8329, unweighted_recall: 0.7256,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8281, f_measure: 0.7633, kappa: 0.4716, kb_relative_information_score: 0.314, mean_absolute_error: 0.317, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7682, number_of_instances: 768, precision: 0.763, predictive_accuracy: 0.7682, prior_entropy: 0.9331, relative_absolute_error: 0.6975, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.398, root_relative_squared_error: 0.8351, unweighted_recall: 0.7276,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.83, f_measure: 0.7595, kappa: 0.4632, kb_relative_information_score: 0.3157, mean_absolute_error: 0.3167, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7643, number_of_instances: 768, precision: 0.7589, predictive_accuracy: 0.7643, prior_entropy: 0.9331, relative_absolute_error: 0.6967, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3973, root_relative_squared_error: 0.8335, unweighted_recall: 0.7238,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8248, f_measure: 0.771, kappa: 0.4885, kb_relative_information_score: 0.3106, mean_absolute_error: 0.3193, mean_prior_absolute_error: 0.4545, weighted_recall: 0.776, number_of_instances: 768, precision: 0.7711, predictive_accuracy: 0.776, prior_entropy: 0.9331, relative_absolute_error: 0.7025, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4003, root_relative_squared_error: 0.8398, unweighted_recall: 0.7354,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8254, f_measure: 0.7683, kappa: 0.4838, kb_relative_information_score: 0.3124, mean_absolute_error: 0.3183, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7721, number_of_instances: 768, precision: 0.7675, predictive_accuracy: 0.7721, prior_entropy: 0.9331, relative_absolute_error: 0.7003, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3996, root_relative_squared_error: 0.8383, unweighted_recall: 0.735,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8279, f_measure: 0.7674, kappa: 0.4822, kb_relative_information_score: 0.315, mean_absolute_error: 0.317, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7708, number_of_instances: 768, precision: 0.7664, predictive_accuracy: 0.7708, prior_entropy: 0.9331, relative_absolute_error: 0.6974, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3984, root_relative_squared_error: 0.8358, unweighted_recall: 0.7348,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8264, f_measure: 0.7521, kappa: 0.4459, kb_relative_information_score: 0.3129, mean_absolute_error: 0.3177, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7578, number_of_instances: 768, precision: 0.7518, predictive_accuracy: 0.7578, prior_entropy: 0.9331, relative_absolute_error: 0.699, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3996, root_relative_squared_error: 0.8384, unweighted_recall: 0.7144,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8247, f_measure: 0.7553, kappa: 0.4552, kb_relative_information_score: 0.3066, mean_absolute_error: 0.3205, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7591, number_of_instances: 768, precision: 0.7542, predictive_accuracy: 0.7591, prior_entropy: 0.9331, relative_absolute_error: 0.7051, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4008, root_relative_squared_error: 0.841, unweighted_recall: 0.7215,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8252, f_measure: 0.7654, kappa: 0.4757, kb_relative_information_score: 0.3071, mean_absolute_error: 0.3196, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7708, number_of_instances: 768, precision: 0.7655, predictive_accuracy: 0.7708, prior_entropy: 0.9331, relative_absolute_error: 0.7033, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4006, root_relative_squared_error: 0.8404, unweighted_recall: 0.7288,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8292, f_measure: 0.7597, kappa: 0.4655, kb_relative_information_score: 0.3114, mean_absolute_error: 0.3178, mean_prior_absolute_error: 0.4545, weighted_recall: 0.763, number_of_instances: 768, precision: 0.7586, predictive_accuracy: 0.763, prior_entropy: 0.9331, relative_absolute_error: 0.6993, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.399, root_relative_squared_error: 0.837, unweighted_recall: 0.7271,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8211, f_measure: 0.7707, kappa: 0.4887, kb_relative_information_score: 0.304, mean_absolute_error: 0.3211, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7747, number_of_instances: 768, precision: 0.7701, predictive_accuracy: 0.7747, prior_entropy: 0.9331, relative_absolute_error: 0.7064, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4025, root_relative_squared_error: 0.8444, unweighted_recall: 0.737,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8252, f_measure: 0.763, kappa: 0.472, kb_relative_information_score: 0.3102, mean_absolute_error: 0.3181, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7669, number_of_instances: 768, precision: 0.7621, predictive_accuracy: 0.7669, prior_entropy: 0.9331, relative_absolute_error: 0.6999, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4003, root_relative_squared_error: 0.8399, unweighted_recall: 0.7292,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8268, f_measure: 0.7651, kappa: 0.476, kb_relative_information_score: 0.3126, mean_absolute_error: 0.3177, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7695, number_of_instances: 768, precision: 0.7645, predictive_accuracy: 0.7695, prior_entropy: 0.9331, relative_absolute_error: 0.699, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3988, root_relative_squared_error: 0.8367, unweighted_recall: 0.7304,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8227, f_measure: 0.7684, kappa: 0.4826, kb_relative_information_score: 0.31, mean_absolute_error: 0.3192, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7734, number_of_instances: 768, precision: 0.7684, predictive_accuracy: 0.7734, prior_entropy: 0.9331, relative_absolute_error: 0.7024, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.4003, root_relative_squared_error: 0.8399, unweighted_recall: 0.7325,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8282, f_measure: 0.7728, kappa: 0.4928, kb_relative_information_score: 0.3112, mean_absolute_error: 0.3185, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7773, number_of_instances: 768, precision: 0.7726, predictive_accuracy: 0.7773, prior_entropy: 0.9331, relative_absolute_error: 0.7007, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3981, root_relative_squared_error: 0.8353, unweighted_recall: 0.7381,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8288, f_measure: 0.758, kappa: 0.4597, kb_relative_information_score: 0.315, mean_absolute_error: 0.3168, mean_prior_absolute_error: 0.4545, weighted_recall: 0.763, number_of_instances: 768, precision: 0.7575, predictive_accuracy: 0.763, prior_entropy: 0.9331, relative_absolute_error: 0.697, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3985, root_relative_squared_error: 0.836, unweighted_recall: 0.7219,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8287, f_measure: 0.7616, kappa: 0.4672, kb_relative_information_score: 0.31, mean_absolute_error: 0.3191, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7669, number_of_instances: 768, precision: 0.7615, predictive_accuracy: 0.7669, prior_entropy: 0.9331, relative_absolute_error: 0.7022, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3987, root_relative_squared_error: 0.8365, unweighted_recall: 0.7249,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.828, f_measure: 0.7562, kappa: 0.4567, kb_relative_information_score: 0.3139, mean_absolute_error: 0.3168, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7604, number_of_instances: 768, precision: 0.7553, predictive_accuracy: 0.7604, prior_entropy: 0.9331, relative_absolute_error: 0.6971, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3987, root_relative_squared_error: 0.8364, unweighted_recall: 0.7216,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8262, f_measure: 0.7689, kappa: 0.4844, kb_relative_information_score: 0.3136, mean_absolute_error: 0.317, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7734, number_of_instances: 768, precision: 0.7686, predictive_accuracy: 0.7734, prior_entropy: 0.9331, relative_absolute_error: 0.6976, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.3995, root_relative_squared_error: 0.8383, unweighted_recall: 0.7342,
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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Description: The "wheel_of_fortune.csv" dataset is a structured collection crafted to support analyses and applications related to the popular game show "Wheel of Fortune." This dataset incorporates…
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Quarterly Database for Macroeconomic Research From original website: ----- FRED-MD and FRED-QD are large macroeconomic databases designed for the empirical analysis of 'big data'. The datasets of…
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258 instances - 204 features - classes - 0 missing values
Electricity Load Diagrams between 2011 and 2014, resampled hourly. From original source: ----- Data set has no missing values. Values are in kW of each 15 min. To convert values in kWh values must be…
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26305 instances - 319 features - classes - 0 missing values
data
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207 instances - 61 features - classes - 0 missing values
Description: The 'wheel_of_fortune.csv' dataset is an intriguing collection designed for various applications, including natural language processing, game development, and cultural studies. It…
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Description: The dataset named 'wheel_of_fortune.csv' is carefully curated for enthusiasts and researchers interested in linguistic patterns, game design, and computational linguistics analysis. It…
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Description: The Student_performance_data_.csv dataset is a comprehensive collection of data aimed at analyzing the factors influencing student performance across various dimensions. This dataset…
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2392 instances - 15 features - classes - 0 missing values
Description: The dataset, named 'diabetes.csv', serves as a comprehensive resource for understanding various factors that may influence the occurrence of diabetes in individuals. Consisting of several…
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768 instances - 9 features - classes - 0 missing values
MCAD
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124 instances - 11 features - classes - 0 missing values
Wind power production in MW recorded per every 4 seconds starting from 01/08/2019 in Australia. From the website: ----- This dataset contains a single very long daily time series representing the wind…
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7397147 instances - 4 features - classes - 0 missing values
Solar power production in MW recorded per every 4 seconds starting from 01/08/2019 in Australia. From the website: ----- This dataset contains a single very long daily time series representing the…
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7397222 instances - 4 features - classes - 0 missing values
Number of births in the United States. From original source: ----- Number of births in the United States. There are several data sets covering different date ranges and obtaining data from different…
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7305 instances - 4 features - classes - 0 missing values
Mean daily flow in cubic meters per second (cumecs) of the Saugeen River. From original source: ----- Mean daily flow in cubic meters per second (cumecs) of the Saugeen River at Walkerton, Jan 1, 1915…
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23741 instances - 4 features - classes - 0 missing values
Daily total sunspot number from 1818 to 2023. From original source: ----- Time range: 1/1/1818 - last elapsed month (provisional values) Data description: Daily total sunspot number derived by the…
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75233 instances - 6 features - classes - 6480 missing values
Daily values of confirmed cases, deaths and recovers for COVID-19 in US. From original source: ----- MThis folder contains daily time series summary tables, including confirmed, deaths and recovered.…
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3819906 instances - 15 features - classes - 0 missing values
Daily values of confirmed cases, deaths and recovers for COVID-19 in several countries. From original source: ----- MThis folder contains daily time series summary tables, including confirmed, deaths…
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313182 instances - 10 features - classes - 0 missing values
Monthly patient count for products that are related to medical problems. From original source: ----- Monthly patient count for products that are related to medical problems. There are 767 time series…
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64428 instances - 6 features - classes - 0 missing values
Uber, Lyft and weather hourly data. From original website: ----- Context Uber and Lyft's ride prices are not constant like public transport. They are greatly affected by the demand and supply of rides…
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77904 instances - 21 features - classes - 0 missing values
Monthly sales car parts. 2674 series. Jan 1998 - Mar 2002. Extracted from 'expsmooth' R package. There are 2677 columns: id_series: The id of the time series. date: The date of the time series in the…
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51 instances - 2677 features - classes - 0 missing values
Pedestrian Counting System published by the city of Melbourne, preprocessed data. From original source: ----- This dataset contains hourly pedestrian counts since 2009 from pedestrian sensor devices…
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43824 instances - 54 features - classes - 151586 missing values
Bitcoin data scrapped from BitInfoCharts, without 'tweets' and with preprocessing. Several Bitcoin related data scrapped directly from BitInfoCharts. 'date' in the format %Y-%m-%d. The 'tweets' column…
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4792 instances - 21 features - classes - 0 missing values
Bitcoin data scrapped from BitInfoCharts, with preprocessing. Several Bitcoin related data scrapped directly from BitInfoCharts. 'date' in the format %Y-%m-%d. We have only kept the rows between the…
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3262 instances - 22 features - classes - 0 missing values
Dominick dataset forecasting data, weekly data. From original source: ----- This dataset contains 115704 weekly time series representing the profit of individual stock keeping units from a retailer.…
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19092987 instances - 3 features - classes - 0 missing values
Australian Electricity Demand forecasting data, half-hourly data. From original source: ----- This dataset contains 5 time series representing the half hourly electricity demand of 5 states in…
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1155264 instances - 5 features - classes - 0 missing values
CIF 2016 time series forecasting competition , monthly data. From original source: ----- Competition Data Format Data file containing time series to be predicted is a text file having the following…
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7108 instances - 3 features - classes - 0 missing values
Tourism competion for time series forecasting, monthly data From original source: ----- The data we use include 366 monthly series, 427 quarterly series and 518 yearly series. They were supplied by…
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109280 instances - 4 features - classes - 0 missing values
Tourism competion for time series forecasting, quarterly data. From original source: ----- The data we use include 366 monthly series, 427 quarterly series and 518 yearly series. They were supplied by…
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42544 instances - 4 features - classes - 0 missing values
Tourism competion for time series forecasting, yearly data. From original source: ----- The data we use include 366 monthly series, 427 quarterly series and 518 yearly series. They were supplied by…
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12678 instances - 4 features - classes - 0 missing values
M4-Competition for time series forecasting, hourly data. From original source: ----- The fourth competition, M4, started on 1 January 2018 and ended in 31 May 2018. The M4 extended and replicated the…
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373372 instances - 5 features - classes - 0 missing values
M4-Competition for time series forecasting, daily data. From original source: ----- The fourth competition, M4, started on 1 January 2018 and ended in 31 May 2018. The M4 extended and replicated the…
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10023836 instances - 5 features - classes - 0 missing values
M4-Competition for time series forecasting, weekly data. From original source: ----- The fourth competition, M4, started on 1 January 2018 and ended in 31 May 2018. The M4 extended and replicated the…
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371579 instances - 5 features - classes - 0 missing values
M4-Competition for time series forecasting, monthly data. From original source: ----- The fourth competition, M4, started on 1 January 2018 and ended in 31 May 2018. The M4 extended and replicated the…
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11246411 instances - 5 features - classes - 0 missing values
M4-Competition for time series forecasting, quarterly data. From original source: ----- The fourth competition, M4, started on 1 January 2018 and ended in 31 May 2018. The M4 extended and replicated…
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2406108 instances - 5 features - classes - 0 missing values
M4-Competition for time series forecasting, yearly data From original source: ----- The fourth competition, M4, started on 1 January 2018 and ended in 31 May 2018. The M4 extended and replicated the…
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858458 instances - 5 features - classes - 0 missing values
M3-Competition for time series forecasting, other data From original source: ----- The 3003 series of the M3-Competition were selected on a quota basis to include various types of time series data…
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13325 instances - 4 features - classes - 0 missing values
M3-Competition for time series forecasting, monthly data. From original source: ----- The 3003 series of the M3-Competition were selected on a quota basis to include various types of time series data…
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167562 instances - 5 features - classes - 0 missing values
M3-Competition for time series forecasting, quarterly data From original source: ----- The 3003 series of the M3-Competition were selected on a quota basis to include various types of time series data…
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37004 instances - 5 features - classes - 0 missing values
M3-Competition for time series forecasting, yearly data. From original source: ----- The 3003 series of the M3-Competition were selected on a quota basis to include various types of time series data…
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18319 instances - 5 features - classes - 0 missing values
Weekly U.S. Product Supplied of Finished Motor Gasoline (Thousand Barrels per Day). Provided by U.S. Energy Information AdministratiQon. Downloaded from the website as available on 24-05-2024. There…
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1737 instances - 4 features - classes - 0 missing values
Weather measures from Versuchsbeete provided by the Max-Planck-Institute for Biogeochemistry Several weather measures provided by Max-Planck-Institute for Biogeochemistry from the Weather Station on…
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916772 instances - 36 features - classes - 730081 missing values
Weather measures from Saaleaue provided by the Max-Planck-Institute for Biogeochemistry. Several weather measures provided by Max-Planck-Institute for Biogeochemistry from the Weather Station on Top…
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1151865 instances - 33 features - classes - 391940 missing values
Weather measures from Beutenberg provided by the Max-Planck-Institute for Biogeochemistry Several weather measures provided by Max-Planck-Institute for Biogeochemistry from the Weather Station on Top…
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1078742 instances - 24 features - classes - 243919 missing values
Monthly Database for Macroeconomic Research From original website: ----- FRED-MD and FRED-QD are large macroeconomic databases designed for the empirical analysis of 'big data'. The datasets of…
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778 instances - 119 features - classes - 0 missing values
Hourly temperature and rainfall observation from the Bureau of Metereology of the Australian Government. From original source: ----- Historical rainfall and temperature forecast and observations…
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8058447 instances - 8 features - classes - 492318 missing values
Electricity Load Diagrams between 2011 and 2014. From original source: ----- Data set has no missing values. Values are in kW of each 15 min. To convert values in kWh values must be divided by 4. Each…
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105217 instances - 319 features - classes - 0 missing values
Outpatient Illness Surveillance weekly data. From original source: ----- Outpatient Illness Surveillance - Information on patient visits to health care providers for influenza-like illness is…
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1099 instances - 12 features - classes - 0 missing values
Electric power distribution, 15 minutely data. From original source: ----- The electric power distribution problem is the distribution of electricity to different areas depends on its sequential…
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69680 instances - 10 features - classes - 0 missing values
Electric power distribution, 15 minutely data. From original source: ----- The electric power distribution problem is the distribution of electricity to different areas depends on its sequential…
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69680 instances - 10 features - classes - 0 missing values
Electric power distribution, hourly data. From original source: ----- The electric power distribution problem is the distribution of electricity to different areas depends on its sequential usage. But…
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17420 instances - 10 features - classes - 0 missing values
Electric power distribution, hourly data. From original source: ----- The electric power distribution problem is the distribution of electricity to different areas depends on its sequential usage. But…
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17420 instances - 10 features - classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9368, f_measure: 0.8778, kappa: 0.7288, kb_relative_information_score: 0.5977, mean_absolute_error: 0.1941, mean_prior_absolute_error: 0.456, weighted_recall: 0.8798, number_of_instances: 19020, precision: 0.8795, predictive_accuracy: 0.8798, prior_entropy: 0.9355, relative_absolute_error: 0.4257, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2987, root_relative_squared_error: 0.6256, unweighted_recall: 0.8541,
Data Banknote
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1372 instances - 2 features - classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9367, f_measure: 0.8798, kappa: 0.7332, kb_relative_information_score: 0.5973, mean_absolute_error: 0.1942, mean_prior_absolute_error: 0.456, weighted_recall: 0.8817, number_of_instances: 19020, precision: 0.8814, predictive_accuracy: 0.8817, prior_entropy: 0.9355, relative_absolute_error: 0.4258, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2991, root_relative_squared_error: 0.6265, unweighted_recall: 0.8565,
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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some description
1 datasets, 1 tasks, 1 flows, 1 runs
some description
1 datasets, 1 tasks, 1 flows, 1 runs
some description
1 datasets, 1 tasks, 1 flows, 1 runs