Description
This project is dedicated to creating an interactive dashboard for in-depth analysis of malaria-related data in African countries. The dataset comprises information on malaria incidence, preventive measures, and demographic characteristics spanning various countries and years.
Project Overview
- Data Preprocessing and Cleaning
The notebooks directory hosts Jupyter Notebooks outlining the steps taken for data preprocessing and cleaning. These notebooks address missing values, standardize data, ensuring it is primed for analysis and dashboard creation.
- Dashboard Creation
The dashboard directory houses code and assets for crafting an interactive dashboard. The dashboard delivers visual insights into malaria incidence, prevention measures, and demographic trends in African countries. Key features encompass an overview of malaria cases, analysis of preventive measures, and demographic statistics.
- Exploratory Data Analysis (EDA)
The notebooks directory also contains notebooks dedicated to exploratory data analysis, presenting key findings and patterns within the malaria-related datasets.
Dashboard Features
Malaria Incidence Overview: Visual representation of malaria cases, incidence rates, and trends. Preventive Measures Analysis: Insights into the utilization of bed nets, antimalarial drugs, and preventive treatments. Demographic Statistics: Population distribution, rural vs. urban living, and growth trends.
Tools and Technologies Used
Python: The programming language employed for data preprocessing, analysis, and dashboard creation. Jupyter Notebooks: Utilized for interactive data analysis and visualization. Pandas, NumPy, Matplotlib, Seaborn: Python libraries for data manipulation and visualization. Dashboard Framework (Dash, Plotly): Employed for constructing interactive and responsive dashboards. HTML, CSS: Used for styling the dashboard.
Acknowledgments
The datasets used in this project are sourced from https://www.kaggle.com/datasets/lydia70/malaria-in-africa.