segnig/Tanzania-Tourism-Prediction

Tanzania Tourism Prediction Zindi competition. # Tanzania Tourism Prediction ## Overview The Tanzanian tourism sector plays a significant role in the Tanzanian economy, contributing about 17% to the country’s GDP and 25% of all foreign exchange revenues. The sector, which provides direct employment for more than 600,000 people and up to 2 million people indirectly, generated approximately $2.4 billion in 2018 according to government statistics. Tanzania received a record 1.1 million international visitor arrivals in 2014, mostly from Europe, the US, and Africa. Tanzania is the only country in the world that has allocated more than 25% of its total area for wildlife, national parks, and protected areas. Its famous attractions include the Serengeti plains, the Ngorongoro Crater, Mount Kilimanjaro, and the Mafia Island marine park. **This project was developed as a solution to the Tanzania Tourism Prediction challenge on Zindi.** ## Objective The primary objective of this project is to develop a machine learning model to **predict how much money a tourist will spend when visiting Tanzania**. This predictive model aims to assist tour operators and the Tanzania Tourism Board in automatically helping tourists across the world estimate their expenditure before visiting Tanzania. ## Evaluation The evaluation metric for this competition is the **Mean Absolute Error (MAE)**. For every row in the dataset, the model correctly outputs the target variable `total_cost`. Example submission format: ```csv test_id,total_cost tour_6322,65000 tour_1153,11000 ``` ## Project Structure ```text Tanzania-Tourism-Prediction/ ├── 01_baseline_modeling.ipynb # Initial Data Exploration, Baseline Models, and Feature Engineering ├── 02_advanced_modeling_catboost.ipynb # Refined modeling (CatBoost) and final inference generation └── README.md # Project documentation ``` ## Methodology & Algorithms Used Throughout the project, several machine learning methodologies were applied and evaluated to …