Machine Learning
This dataset contains nearly 1 Million unique movie reviews from 1150 different IMDb movies spread across 17 IMDb genres - Action, Adventure, Animation, Biography, Comedy, Crime, Drama, Fantasy, History, Horror, Music, Mystery, Romance, Sci-Fi, Sport, Thriller and War. The dataset also contains movie metadata such as date of release of the movie, run length, IMDb rating, movie rating (PG-13, R, etc), number of IMDb raters, and number of reviews per movie.
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This dataset contains 1.65 lakhs tweet ids related to death of Sushant Singh Rajput in English language. For whole dataset with all other fields drop a mail at avishekgarain@gmail.com.
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The first bit of light is the gesture of being, on a massive screen of the black panorama. A small point of existence, a gesture of being. The universal appeal of gesture is far beyond the barriers of languages and planets. These are the microtransactions of symbols and patterns which have traces of the common ancestors of many civilizations.
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The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced.
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This is a simple batch of data sets of points containing only integer attributes. The data sets were generated with a randomly correlated data set generator (DOI: 10.13140/RG.2.2.34866.43200).
This batch includes a total of 12 data sets which can be used to validate implementations of clustering algorithms such as k-nearest neighbours, or k-means.
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Vehicular networks have various characteristics that can be helpful in their inter-relations identifications. Considering that two vehicles are moving at a certain speed and distance, it is important to know about their communication capability. The vehicles can communicate within their communication range. However, given previous data of a road segment, our dataset can identify the compatibility time between two selected vehicles. The compatibility time is defined as the time two vehicles will be within the communication range of each other.
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A high-fidelity CarSim model is used to collect the data for almost 50 maneuvers for two different tractors with different trailer attached to them. For instance, 10 Single Lane Change (SLC) maneuvers are considered in CarSim including 5 tests with E-class SUV and 5 tests with a pick-up truck. Moreover, at each test, the trailer payload and geometry, CG location, and track width, have been changed to collect sufficient data.
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The Badminton Activity Recognition (BAR) Dataset was collected for the sport of Badminton for 12 commonly played strokes. Besides the strokes, the objective of the dataset is to capture the associated leg movements.
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IEEE 802.11ac performance dataset contains information regarding normalized throughput achieved under five link configuration parameters and a channel condition measured by SNR. The five link configuration parameters are channel bandwidth, multiple-input multiple output (MIMO) antenna, modulation and coding schemes (MCS), guard interval and frame aggregation. In the dataset, there are seven columns: SNR value, MIMO, channel bandwidth, MCS, guard interval, frame aggregation and normalized throughput.
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The dataset contains the signal recording acquired on vehicle (car) drivers (ten experienced drivers and ten learner drivers) on the same 28.7 km route in the Silesian Voivodeship (in Polish województwo śląskie) in southern Poland. Experienced drivers performed the tasks in their own cars whereas the learner drivers performed the tasks under a supervison of a driving instructor in a specially marked cars (with L sign).
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