This project has been prepared using fuel data from Italy. The data set, which has a time series, contains weekly data from 2005 to 2022. In addition, there is an equal number of data for 6 different fuel types in this data set. Deep learning and machine learning algorithms are used separately for these fuel types. In these algorithms, deep learning approach is used; CNN and LSTM algorithms were applied. In the machine learning approach, Random Forest and XGB Boost Algorithms were applied. However, XGB Boost algorithm results are not included in the report. In addition, Prophet Algorithm was applied to the data set in order to examine the time series trend.
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