Nestoraddah/Python-Healthcare-Malaria-Samples-analysis

Malaria Data Analysis- Ghana Health Survey # Malaria Data Analysis- Ghana Health Survey This project analyzes malaria survey data collected from hospitals across Ghana to uncover demographic, geographical, and clinical trends in malaria infections. Using Python (Pandas, NumPy, Seaborn, Matplotlib), the dataset was thoroughly cleaned by removing duplicates, imputing missing literacy levels, and standardizing column formats to ensure reliability and consistency. The analysis explores patterns across sample types, age groups, gender, and regions to better understand national malaria prevalence. Key Business Questions Answered: - Which sample type is most commonly used for malaria testing across regions? - What is the average age of individuals tested for malaria? - Which age group records the highest positivity rate? - Do infection rates differ by gender or region? - How does literacy level (imputed) correlate with malaria occurrence? Insights & Impact The findings highlight clear demographic trends, including the dominance of specific sample types for diagnosis, measurable age-related infection patterns, and meaningful regional disparities. These insights support healthcare leaders in improving resource distribution and strengthening targeted interventions. Recommendations - Prioritize high-risk age groups for testing and prevention campaigns. - Allocate diagnostic resources to regions with elevated positivity rates. - Incorporate literacy-based health education strategies to improve malaria awareness. - Maintain consistent data collection frameworks to enhance future epidemiological analyses.