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It used real-life data from Walmart and was conducted on Kaggle's Platform. It offered substantial prizes totaling US$100,000 to the winners. The data was provided by Walmart and consisted of around 42,000 hierarchical daily time series, starting at the level of SKUs and ending with the total demand of some large geographical area.
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Anthony John Goldbloom (born 21 June 1983) is the founder and former CEO of Kaggle, a data science competition platform which has used predictive modelling competitions to solve data problems for companies, such as NASA, Wikipedia, [1] Ford and Deloitte.
Jeremy Howard (born 13 November 1973) is an Australian data scientist, entrepreneur, and educator. [1] He is the co-founder of fast.ai, where he teaches introductory courses, [2] develops software, and conducts research in the area of deep learning. Previously he founded and led Fastmail, Optimal Decisions Group, and Enlitic.
The Global Energy Forecasting Competition (GEFCom) is a competition conducted by a team led by Dr. Tao Hong that invites submissions around the world for forecasting energy demand. [1] GEFCom was first held in 2012 on Kaggle , [ 2 ] and the second GEFCom was held in 2014 on CrowdANALYTIX.
The MM5 is a limited-area, terrain-following sigma coordinate model that is used to replicate or forecast mesoscale and regional scale atmospheric circulation. [1] It has been updated many times since the 1970s to fix bugs, adapt to new technologies, and work on different types of computers and software.
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis.
Then, analyze the source data to determine the most appropriate data and model building approach (models are only as useful as the applicable data used to build them). Select and transform the data in order to create models. Create and test models in order to evaluate if they are valid and will be able to meet project goals and metrics.
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