D

dataset

This project aims to simplify and automate the reimbursement process for (pre-sales or any professionals who travels fairly frequently). By leveraging OCR (Optical Character Recognition) technology, the system extracts key information such as prices and expense types from receipts and bills. The extracted data is then saved to a drive, making it easier for sales teams to submit and track their expenses. The goal is to reduce manual effort, minimize errors, and streamline the reimbursement workflow.

The OpenPowerlifting Project. https://www.openpowerlifting.org

Extension with extra functions for outputting information in JSON format, including filtering, searching, pagination, and data transformation.

BETA: Dependency Scanning for supported projects

Apertium occitan français

OpenML dataset: women_clothing_review https://www.openml.org/d/46659

OpenML dataset: Year_Prediction_MSD https://www.openml.org/d/46672

OpenML dataset: heart_failure_clinical_records https://www.openml.org/d/46612

OpenML dataset: Ott https://www.openml.org/d/46670

OpenML dataset: US_Accidents_March23 https://www.openml.org/d/46650

OpenML dataset: kick_starter_funding https://www.openml.org/d/46668

OpenML dataset: DDXPlus https://www.openml.org/d/46649

OpenML dataset: jc_penney_products https://www.openml.org/d/46661

OpenML dataset: google_qa_question_type_reason_explanation https://www.openml.org/d/46658

OpenML dataset: data_scientist_salary https://www.openml.org/d/46664

OpenML dataset: ae_price_prediction https://www.openml.org/d/46656

OpenML dataset: google_qa_answer_type_reason_explanation https://www.openml.org/d/46657

OpenML dataset: product_sentiment_machine_hack https://www.openml.org/d/46651

OpenML dataset: california_house_price https://www.openml.org/d/46669