AmisiJospin/Data-Analysis-Project

This GitHub repository contains Jupyter notebooks analyzing key aspects of refugee life in the Dzaleka Refugee Camp, located in Dowa, Malawi. # Dzaleka Refugee Camp Data Analysis Project ## Overview This GitHub repository contains Jupyter notebooks analyzing key aspects of refugee life in the Dzaleka Refugee Camp, located in Dowa, Malawi. The project focuses on three main areas: - **Assistance Distribution**: Preferences for humanitarian aid in the form of food, money, or a combination. - **Durable Solutions**: Refugee preferences for long-term solutions such as resettlement, citizenship, or repatriation. - **Water Fetching**: Patterns of water collection, including frequency and volume by area within or near the camp. These analyses are based on sample datasets collected from camp participants, aiming to provide insights for humanitarian organizations, policymakers, and researchers to improve support and resource allocation. All data is anonymized and focuses on aggregate trends. ## Repository Structure - **`assistance_distribution.ipynb`**: Notebook analyzing preferences for food and/or monetary assistance among refugees. - **`duration_solution.ipynb`**: Notebook exploring preferred durable solutions (resettlement, citizenship, repatriation) by nationality. - **`water_fetching.ipynb`**: Notebook examining water fetching habits, including containers per day, days per week, and a pie chart visualization of monthly contributions by area. - **`README.md`**: This file providing project documentation. ## Data Sources The datasets are derived from surveys or records of refugees in Dzaleka Refugee Camp. Key highlights: - **Assistance Distribution**: Includes participant names (anonymized) and preferences (e.g., "Food", "Money", "Food plus money"). - **Durable Solutions**: Maps nationalities (e.g., Congo, Burundi, Rwanda) to preferred solutions (e.g., Resettlement, Rapatriation, Citizenship). - **Water Fetching**: Covers areas (e.g., Karonga, Likuni) with metrics like containers fetched per day and days per week, culminating in monthly estimates. Note: These are sample datasets for demonstration. Real-world …