  Over the past 20 years, Kenya's demand for petroleum products has
proliferated. This is mainly because this particular commodity is used in many
sectors of the country's economy. Exchange rates are impacted by constantly
shifting prices, which also impact Kenya's industrial output of commodities.
The cost of other items produced and even the expansion of the economy is
significantly impacted by any change in the price of petroleum products.
Therefore, accurate petroleum price forecasting is critical for devising
policies that are suitable to curb fuel-related shocks. Data mining techniques
are the tools used to find valuable patterns in data. Data mining techniques
used in petroleum price prediction, including artificial neural networks
(ANNs), support vector machines (SVMs), and intelligent optimization techniques
like the genetic algorithm (GA), have grown increasingly popular. This study
provides a comprehensive review of the existing data mining techniques for
making predictions on petroleum prices. The data mining techniques are
classified into regression models, deep neural network models, fuzzy sets and
logic, and hybrid models. A detailed discussion of how these models are
developed and the accuracy of the models is provided.
