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The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...
Although used car prices are finally starting to decline, it could be years before the market returns to a pre-pandemic normal. A recent study conducted by the car insurance comparison site Jerry...
Floating car data (FCD) in traffic engineering and management is typically timestamped geo-localization and speed data directly collected by moving vehicles, in contrast to traditional traffic data collected at a fixed location by a stationary device or observer.
The methods employed in pricing science may be categorized into two broad areas: 1. forecasting and 2. optimization.The forecasting problem reflects the fact that the pricing decisions are intended to affect purchase events over some future time horizon.
10 June 2013: R.L. Polk & Company, a provider of auto-industry data including used-car history company Carfax, for $1.4 billion. [ 24 ] 26 September 2017: automotiveMastermind Inc., a provider of predictive analytics and marketing automation for the automotive industry.
Predictive analytics, or predictive AI, encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.
A consensus forecast is a prediction of the future created by combining several separate forecasts which have often been created using different methodologies. They are used in a number of sciences, ranging from econometrics to meteorology, and are also known as combining forecasts, forecast averaging or model averaging (in econometrics and statistics) and committee machines, ensemble ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]