Forecasting Tourism Income

During my academic journey, the "Machine Learning with R" course from the economics department provided a unique opportunity to delve into R, a language I hadn't encountered before. Despite starting with zero prior knowledge, I embarked on a remarkable project – the Tourism Income prediction model. Applying fundamental machine learning techniques, particularly linear regression models, we navigated through data manipulation, exploring patterns and correlations. Focusing on the GDPs of countries frequently visiting Turkey, including the UK, Germany, and France, we crafted a model that surpassed our expectations. This project challenged me to apply theoretical concepts in a practical setting, making informed decisions about data manipulation, model selection, and interpretation. It was a valuable learning experience, showcasing the power of machine learning in making sensible forecasts for Turkey's tourism income.

Technologies Used

For the Tourism Income project, I harnessed powerful algorithms and tools within the R programming language to explore and predict economic trends. This insightful project, developed for the "Machine Learning with R" course, marked my entry into the world of R programming.

• R Programming: Leveraged for its robust statistical and machine learning capabilities, I utilized R to implement linear regression models, unraveling patterns within tourism income data.
• Gradient Boosting: Applied advanced machine learning techniques, specifically gradient boosting algorithms, to enhance the predictive accuracy of the model.
• ggplot Library: Employed this data visualization powerhouse to plot and illustrate the forecasted results, providing a clear and insightful representation of the predicted tourism income trends.
• R Studio: Served as the primary integrated development environment (IDE), facilitating collaborative coding and enhancing the overall efficiency of the project.

This combination of algorithms and tools empowered the Tourism Income project to make informed predictions about Turkey's tourism income based on the GDPs of key visiting countries. The implementation of advanced machine learning techniques, coupled with effective data visualization, provided a comprehensive understanding of economic forecasts.

You can find a pdf version for the R markdown, explaining the code and algorithms used in details over plots drawn using ggplot; here. You can also check out its code at its GitHub Repository.