fadillalillianhoud/Malaria-Analysis

Exploratory data analysis of malaria cases in Ghana and Nigeria. Includes data cleaning, visualization, and insights into trends, prevalence, and risk factors. # Malaria-Analysis Exploratory data analysis of malaria cases in Ghana and Nigeria. Includes data cleaning, visualization, and insights into trends, prevalence, and risk factors. # Malaria Epidemiology in Ghana and Nigeria **An Exploratory Data Analysis Project by Fadilla Houd** ## Overview This project explores malaria epidemiology in Ghana and Nigeria using publicly available datasets. The analysis highlights trends, prevalence, and risk factors that influence malaria outcomes in these two West African countries. ## Objectives - Compare malaria prevalence between Ghana and Nigeria. - Examine demographic and geographic factors linked to malaria. - Visualize patterns in malaria cases over time. - Suggest data-driven solutions for malaria control. ## Methods - Data Cleaning: Removal of duplicates, handling missing values, and standardization. - Analysis: Conducted using Python (Pandas, NumPy). - Visualization: Graphs and charts created with Matplotlib and Seaborn. ## Key Insights - Nigeria shows higher malaria case numbers compared to Ghana, reflecting population size and regional exposure. - Seasonal patterns affect case fluctuations. - Urban vs rural disparities reveal gaps in access to prevention and treatment. ## Tools - Google Colab - Python (Pandas, NumPy, Matplotlib, Seaborn) ## Files - `notebooks/`: Contains the exploratory data analysis (this .ipynb file). - `data/`: Raw and cleaned datasets. - `visuals/`: Charts and figures generated from analysis. ## Future Work - Extend analysis to other African countries. - Investigate impact of interventions (bed nets, medication, vaccines). - Explore socio-economic links to malaria prevalence. --- *Author: Fadilla Houd*