This research applies data analytics techniques and algorithms to analyze and forecast UAE’s trade. Regression model was applied to study the relationship between exports, imports, and Gross Domestic Product (GDP). GDP is “the total monetary or market value of all the finished goods and services produced within a country’s borders in a specific time period. As a broad measure of overall domestic production, it functions as a comprehensive scorecard of a given country’s economic health” (Gross Domestic Product(GDP), 2022) . In this project we applied several data analytics techniques to analyze UAE trade data. We also concluded that, based on analyzed data, exports have a positive relationship to GDP. On the other hand, Imports, based on analyzed data, have a negative relationship to GDP. ARIMA model was applied to analyze and forecast the UAE’s exports and imports trends. Python programming language was applied to perform the data preprocessing, data exploration, data analytics techniques and algorithms implementation and visualizations. The data sets used in this analysis cover 19 years from (2001-2019) with annual intervals from different international organizations such as the World Bank, International Monetary Fund (IMF), World Trade Organization (WTO), and UNCTAD. This study will help the decision makers of UAE to formulate trade strategies based on observed trends and forecast analysis that will be performed as part of this project.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Alnajada, Ruba, "Using Data Analytics Techniques to Analyze and Predict UAE Trade" (2022). Thesis. Rochester Institute of Technology. Accessed from