Deus-Kayiranga/Rwanada-Tourist-Visit-Analysis-Across-the-World

This project aims to analyze tourism trends in Rwanda by identifying peak tourist seasons, analyzing the origin of tourists, and visualizing changes in tourist numbers over the years. The results will help generate data-driven recommendations for improving tourism strategies. # Rwanda-Tourist-Visit-Analysis-Across-the-World This project aims to analyze tourism trends in Rwanda by identifying peak tourist seasons, analyzing the origin of tourists, and visualizing changes in tourist numbers over the years. The results will help generate data-driven recommendations for improving tourism strategies. ## 📍 Project Overview This project aims to explore and analyze **Rwanda's tourism trends** over several years using public datasets from national and international sources. The primary focus was to: - Identify **peak tourist seasons** - Understand **tourist origins** - Visualize **park visit patterns** and their correlation with international arrivals - Develop **data-driven recommendations** for enhancing Rwanda's tourism strategies --- ## 🗃️ Datasets Used 1. **International Tourist Arrivals** – From Rwanda Development Board (RDB) 2. **National Park Visit Records** – Per park, annually 3. **Visitor Origin Breakdown** – Countries contributing the highest number of tourists > Format: `.csv` > Source: RDB & International Development Reports (NISR, UNWTO, etc.) --- ## 🔧 Tools Used - 🐍 **Python (Jupyter Notebook)** – Data preparation, EDA, modeling - 📈 **Power BI Desktop** – Interactive visualization and dashboard creation - 📁 **Pandas & Scikit-Learn** – For data manipulation and machine learning - 🌐 **GitHub** – Version control and documentation --- ## 📊 Power BI Dashboard A comprehensive and interactive Power BI dashboard was created to explore: - 📆 Tourist arrival patterns by **year and season** - 🏞️ Visits to major national parks like **Volcanoes**, **Nyungwe**, and **Akagera** - 🌍 **Country-wise** tourist contribution - 📉 Correlation between **tourist influx** and **park visits** - 🕒 Seasonality using monthly breakdowns and trends --- ## Here is the Dashboard for my power Bi ## 📁 Python Analysis Highlights (Jupyter) ### ✅ Data Understanding & Cleaning - Handled null values and cleaned categorical inconsistencies - Parsed time …