Sri Lanka Tourism Arrival Predictor
Traditional ML forecasting with SHAP explainability
COMPLETEDSOLO
Overview
An end-to-end machine learning project that forecasts monthly tourist arrivals to Sri Lanka using SLTDA public data, compares traditional regression models, and provides explainable predictions through a Streamlit web app.
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Project Details
ClientUniversity of Moratuwa (ML Assignment)
RoleMachine Learning Developer
TimelineJan 2026 - Feb 2026
Duration2 Weeks
Technologies
PythonPandasNumPyscikit-learnSHAPMatplotlibSeabornStreamlit
Key Features
- Public Sri Lankan Dataset Pipeline: Compiled monthly tourist arrival data (2016-2025) from SLTDA public reports with source traceability.
- Traditional ML Model Comparison: Compared Ridge Regression, SVR (RBF), and Random Forest using chronological splitting and time-series cross-validation.
- Best Model Evaluation: Selected Random Forest as best model based on validation/test metrics (RMSE, MAE, MAPE, R2).
- SHAP Explainability: Generated SHAP global importance and summary plots to explain what drives model predictions.
- Interactive Forecasting App: Built a beginner-friendly Streamlit app with scenario-based forecasting, downloadable CSV output, and in-app metric explanations.