0nserio/Analyzing-Malaria-in-Africa-using-Python

## Malaria in Africa Data Analysis ### Project Overview This project aims to explore various aspects of malaria in Africa, including its incidence, the reported cases, and the impact of population distribution and access to basic services on the spread and control of the disease. Through data analysis and visualization, we seek to shed light on the dynamics of malaria and the efforts being undertaken to combat this devastating epidemic. ### Dataset * Country Name * Year * Country Code * Incidence of malaria (per 1,000 population at risk) * Malaria cases reported', 'Rural population (% of total population) * Rural population growth (annual %) * Urban population (% of total population) * Urban population growth (annual %) * People using at least basic drinking water services (% of population) * People using at least basic drinking water services, rural (% of rural population) * People using at least basic drinking water services, urban (% of urban population) * People using at least basic sanitation services (% of population) * People using at least basic sanitation services, rural (% of rural population) * People using at least basic sanitation services, urban (% of urban population) * Latitude * longitude * geometry ### Dataset Source The dataset can be accessed from Kaggle: Malaria in Africa ### Requirements To run the project, the following packages are needed: * Jupyter Notebook * Pandas * NumPy * Matplotlib * Seaborn ### Project Steps 1. Data Cleaning: Handling missing values. Converting categorical variables to numerical format. Outlier detection and treatment, if necessary. 2. Exploratory Data Analysis (EDA): Univariate Analysis: Analyzing individual variables to understand their distribution and patterns. Bivariate Analysis: Exploring relationships between variables. Multivariate Analysis: Studying the interaction of multiple variables to see more complex relationships and survival trends. 3. Visualization: Graphical representations to identify patte …