Context

Research to develop a machine learning trade strategy.

Content

Given dataset include price column, price difference columns and few technical analysis columns. Focus on technical analysis features and relevant price difference features excluding price columns for training. All these data is historical price of EUR/USD pair at 1min timeframe for 1 month.

Context

Cityscapes data (dataset home page) contains labeled videos taken from vehicles driven in Germany. This version is a processed subsample created as part of the Pix2Pix paper. The dataset has still images from the original videos, and the semantic segmentation labels are shown in images alongside the original image. This is one of the best datasets around for semantic segmentation tasks.

Acknowledgements

This dataset is the same as what is available here from the Berkeley AI Research group.

License

The Cityscapes data available from cityscapes-dataset.com has the following license:

This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Daimler AG, MPI Informatics, TU Darmstadt) do not accept any responsibility for errors or omissions. That you include a reference to the Cityscapes Dataset in any work that makes use of the dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the Cityscapes website. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain. That all rights not expressly granted to you are reserved by (Daimler AG, MPI Informatics, TU Darmstadt).

Inspiration

Can you identify you identify what objects are where in these images from a vehicle.

Blue Label Telecoms reported ZAR625.81M in Selling and Administration Expenses for its fiscal semester ending in November of 2024. Data for Blue Label Telecoms | BLU - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Cells perceive and interact with their environment in part through the expression, activation, and regulation of receptors on their plasma membrane. These receptors are dynamically trafficked from the plasma membrane in a process called endocytosis and delivered to the plasma membrane via exocytosis. Different receptors take diverse routes through the cell before being delivered via exocytosis. The data in this project focuses on 3 prototypical plasma membrane receptors - the B2 adrenergic receptor, the µ opioid receptor, and the transferrin receptor. Using a pH-sensitive green fluorescent protein variant, we visualized these receptors in cells as they recycled to the plasma membrane. We subsequently hand-labeled a subset of the data in order to build an automated image analysis method that could be used to detect receptor exocytosis across diverse imaging conditions. This repository contains our primary microscopy data from these studies as well as the labeling for use in supervised machine learning.

These data support Evans et al 2021 and subsequent publications.

Data Collection
TIFF image stacks were collected using a Nikon Eclipse TiE Inverted Microscope using TIRF illumination with a solid state 488nm laser through a Nikon 60x/1.49NA TIRF objective and captured using an Andor iXon 897+ EMCCD camera. The camera was windowed to a 300x300 pixel view and images were collected with a 18.5ms exposures (~54Hz). Images were collected across two days, with two coverslips of each condition collected on day 1, and one coverslip collected on day 2.

DNA Constructs
The 3 cargos imaged in these data are the transferrin receptor (TfR), the B2-adrenergic receptor (B2AR, B2), and the µ opioid receptor (MOR). Constructs encoding these receptors, tagged extracellularly with the ph-sensitive GFP variant Superecliptic pHluorin (SpH, Sankaranarayanan et al. 2000, have been previously described in Yudowski et al. 2006 for B2AR, Yu et al. 2010 for MOR, and Yudowski et al. 2009 for TfR.

Cell Culture
HEK293 cells were cultured in DMEM High Glucose (Hyclone) supplemented with 10% Heat Inactivated FBS (Gibco). Cells expressing B2 and MOR were stably selected from transient transfection using G418. Cells expressing TfR were transfected 3 days before the experiments presented here using Effectene following manufacturers' instructions. Before imaging, cells were transferred to 25mm diameter #1.5 glass coverslips (Electron Microscopy Sciences). Two days after plating, experiments began.

Imaging conditions
Cells were imaged in L-15 minimal media supplemented with 1% FBS. For MOR and B2, cells were imaged for 1 minute at ~0.16Hz without perturbation. Then agonist was added (10µM DAMGO for MOR, 10µM isoproterenol for B2) to the media and cells were imaged for 5 minutes to ensure that receptors clustered and internalized. After internalization, cells were bleached with 100% laser power for 1 minute and then imaged at 54Hz to visualize exocytic events. exocytosis was captured for up to 20 minutes after initial treatment, one cell at a time. For TfR, a single frame was taken before bleaching to show receptor expression levels and then cells were bleached and imaged as described above.

Data blinding
After collection, files were renamed as described in map.md. All metadata files and internalization imaging were separated into the 2 "extras" folders. The exocytosis movies were 'scrambled' to hide cargo identity using the included scrambler.py file. OPP_scramble.log described the mapping of scrambled filenames to the original imaging.

Human labeling
A subset of the images (22, with roughly equal representation across cargos) were hand labeled for exocytic events. Images were viewed in FIJI Schindelin et al. 2012 nad played back at 0.5x. When exocytic events were identified by eye, the playback was paused and the appearance of an event was found through manual advancing of the frames of the movie. The event was labeled using the Cell Counter plugin. Each movie was watched twice to identify as many events as possible. Labeled events are saved a in this dataset.

Data organization
All exocytic event movies and any matching human labeling are included in this base directory. All internalization movies and all metadata for all movies are included in the Extras folder for the day that movie was recorded. Coverslip and cargo identity are listed in map.md and the ground truth for cargo identity is in OPP_scramble.log

• The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)

• The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.

• The data were originally collected by Nielsen Market Track. They were analysed on the Smart Protein project in 2021 and used to publish an extensive market data report and to host a public webinar, both entitled Plant-based foods in Europe: how big is the market?

This dataset consists of 348 non-zero onset Vietnamese speeches (with their transcripts and the labelled start and end times of each speech) extracted from approximately 30-hour of FPT Open Speech Data (released publicly in 2018 by FPT Corporation). The extraction process was done automatically by a Python program written by the contributor.

The speeches are in *.mp3 format and *.wav format (Mono, 48 kHz, 32-bit float) while the transcript file is in *.txt format with utf-8 encoding scheme.

The dataset is useful for any onset detection research and development since the start and end times of each speech are already labelled.

Copyright 2018 FPT Corporation Permission is hereby granted, free of charge, non-exclusive, worldwide, irrevocable, to any person obtaining a copy of this data or software and associated documentation files (the “Data or Software”), to deal in the Data or Software without restriction, including without limitation the rights to use, copy, modify, remix, transform, merge, build upon, publish, distribute and redistribute, sublicense, and/or sell copies of the Data or Software, for any purpose, even commercially, and to permit persons to whom the Data or Software is furnished to do so, subject to the following conditions: The above copyright notice, and this permission notice, and indication of any modification to the Data or Software, shall be included in all copies or substantial portions of the Data or Software. THE DATA OR SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR SOFTWARE OR THE USE OR OTHER DEALINGS IN THE DATA OR SOFTWARE. Patent and trademark rights are not licensed under this FPT Public License.

Full code for the data analysis and simulations presented in this paper is available at https://github.com/ciaran-evans/label-shift-detection. The data used in the dengue case study was made publicly available by [3], and a copy is provided in the repository with the code. (ZIP)

Classifiers have been developed to help diagnose dengue fever in patients presenting with febrile symptoms. However, classifier predictions often rely on the assumption that new observations come from the same distribution as training data. If the population prevalence of dengue changes, as would happen with a dengue outbreak, it is important to raise an alarm as soon as possible, so that appropriate public health measures can be taken and also so that the classifier can be re-calibrated. In this paper, we consider the problem of detecting such a change in distribution in sequentially-observed, unlabeled classification data. We focus on label shift changes to the distribution, where the class priors shift but the class conditional distributions remain unchanged. We reduce this problem to the problem of detecting a change in the one-dimensional classifier scores, leading to simple nonparametric sequential changepoint detection procedures. Our procedures leverage classifier training data to estimate the detection statistic, and converge to their parametric counterparts in the size of the training data. In simulated outbreaks with real dengue data, we show that our method outperforms other detection procedures in this label shift setting.

MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs.

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review.

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.

• MuMu dataset (mapping, metadata, annotations and text reviews)
• Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments

These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

Scientific References

Please cite the following paper if using MuMu dataset or Tartarus library.

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916

Comparison of the information used by each changepoint detection procedure considered in simulations.

Brand Label and Wholesale Information for Alcohol Products Registered in NYS.The New York State Alcohol Beverage Control Law specifies that no manufacturer or wholesaler shall sell to any retailer nor shall any retailer purchase any alcoholic beverages unless these beverages are labeled in accordance with the Authority's Rules and Federal Regulations and unless such label shall be registered with and approved by the State Liquor Authority. Effective January 1, 1994, wine does not need to be brand label registered if the wine has received label approval from the Bureau of Alcohol, Tobacco and Firearms (BATF).

This data provides detailed information on the items subject to the 'Energy Consumption Efficiency Rating Labeling System' introduced under the 'Energy Utilization Rationalization Act'. This system is a representative energy management policy of the government that requires labeling of energy consumption efficiency from grade 1 (highest efficiency) to grade 5 (lowest efficiency) for home appliances and office equipment with high power consumption and distribution rates. In addition, the Minimum Energy Efficiency Standard (MEPS) is applied, which prohibits the production and sale of products that do not meet the grade 5 standard. The main purpose of this dataset is to provide transparent information so that consumers can easily identify and purchase energy-efficient products, thereby encouraging wise consumption. Through this, manufacturers (importers) are encouraged to produce and sell energy-saving products at the source, reducing energy consumption nationwide and contributing to reducing greenhouse gas emissions. The ultimate goal is to reduce household electricity bills and promote sustainable energy consumption.

Comparison of method performance for detecting an abrupt change in dengue prevalence.

Aerial Semantic Drone Dataset

The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an altitude of 5 to 30 meters above the ground. A high-resolution camera was used to acquire images at a size of 6000x4000px (24Mpx). The training set contains 400 publicly available images and the test set is made up of 200 private images.

This dataset is taken from https://www.kaggle.com/awsaf49/semantic-drone-dataset. We remove and add files and information that we needed for our research purpose. We create our tiff files with a resolution of 1200x800 pixel in 24 channel with each channel represent classes that have been preprocessed from png files label. We reduce the resolution and compress the tif files with tiffile python library.

If you have any problem with tif dataset that we have been modified you can contact nunenuh@gmail.com and gaungalif@gmail.com.

This dataset was a copy from the original dataset (link below), we provide and add some improvement in the semantic data and classes. There are the availability of semantic data in png and tiff format with a smaller size as needed.

Semantic Annotation

The images are labelled densely using polygons and contain the following 24 classes:

unlabeled paved-area dirt grass gravel water rocks pool vegetation roof wall window door fence fence-pole person dog car bicycle tree bald-tree ar-marker obstacle conflicting

Directory Structure and Files

> images
> labels/png
> labels/tiff
 - class_to_idx.json
 - classes.csv
 - classes.json
 - idx_to_class.json

Included Data

• 400 training images in jpg format can be found in "aerial_semantic_drone/images"
• Dense semantic annotations in png format can be found in "aerial_semantic_drone/labels/png"
• Dense semantic annotations in tiff format can be found in "aerial_semantic_drone/labels/tiff"
• Semantic class definition in csv format can be found in "aerial_semantic_drone/classes.csv"
• Semantic class definition in json can be found in "aerial_semantic_drone/classes.json"
• Index to class name file can be found in "aerial_semantic_drone/idx_to_class.json"
• Class name to index file can be found in "aerial_semantic_drone/idx_to_class.json"

Contact

aerial@icg.tugraz.at

Citation

If you use this dataset in your research, please cite the following URL: www.dronedataset.icg.tugraz.at

License

The Drone Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Graz University of Technology) do not accept any responsibility for errors or omissions. That you include a reference to the Semantic Drone Dataset in any work that makes use of the dataset. For research papers or other media link to the Semantic Drone Dataset webpage.

That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain. That all rights not expressly granted to you are reserved by us (Graz University of Technology).

About this data

LTFS provides it’s loan services to its customers and is interested in selling more of its Top-up loan services to its existing customers.

Develop a model for the interesting business challenge ‘Upsell Predictions'

Content

1. Customer’s Demographics: The demography table along with the target variable & demographic information contains variables related to Frequency of the loan, Tenure of the loan, Disbursal Amount for a loan & LTV.

2. Bureau data: Bureau data contains the behavioural and transactional attributes of the customers like current balance, Loan Amount, Overdue etc. for various tradelines of a given customer

Acknowledgements

Analytics Vidya and LTFS Teams

Inspiration

Explote different classification algorithms along with exploratory analysis of the data!!!

Blue Label Telecoms مصاريف البيع والإدارة - القيم الحالية، والبيانات التاريخية، والتنبؤات والإحصاءات والرسوم البيانية والتقويم الاقتصادي - Nov 2025.Data for Blue Label Telecoms | مصاريف البيع والإدارة including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Blue Label Telecoms فروش و مدیریت هزینه - ارزش های فعلی، داده های تاریخی، پیش بینی، آمار، نمودار و تقویم اقتصادی - Oct 2025.Data for Blue Label Telecoms | فروش و مدیریت هزینه including historical, tables and charts were last updated by Trading Economics this last October in 2025.

Blue Label Telecoms 销售和管理费用 - 当前值，历史数据，预测，统计，图表和经济日历 - Nov 2025.Data for Blue Label Telecoms | 销售和管理费用 including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Blue Label Telecoms Gastos De Venta Y Administración - Los valores actuales, los datos históricos, las previsiones, estadísticas, gráficas y calendario económico - Nov 2025.Data for Blue Label Telecoms | Gastos De Venta Y Administración including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Blue Label Telecoms Frais De Vente Et D'Administration - Les valeurs actuelles, des données historiques, des prévisions, des statistiques, des tableaux et le calendrier économique - Nov 2025.Data for Blue Label Telecoms | Frais De Vente Et D'Administration including historical, tables and charts were last updated by Trading Economics this last November in 2025.