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https://github.com/martinkalema/malaria-in-africa
This project is aimed at understanding, mitigating, and controlling the impact of malaria in Africa.
https://github.com/martinkalema/malaria-in-africa
data-mining data-preprocessing data-visualization
Last synced: 5 days ago
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This project is aimed at understanding, mitigating, and controlling the impact of malaria in Africa.
- Host: GitHub
- URL: https://github.com/martinkalema/malaria-in-africa
- Owner: MartinKalema
- Created: 2023-08-23T17:25:28.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-29T22:19:38.000Z (10 months ago)
- Last Synced: 2024-01-30T00:43:31.394Z (10 months ago)
- Topics: data-mining, data-preprocessing, data-visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.13 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Malaria in Africa Data Science Project
## Project Overview
This data science project is aimed at analyzing and understanding the prevalence, distribution, and influencing factors of malaria in Africa. Malaria remains a significant public health challenge in many African countries, and this project seeks to provide insights that can inform public health interventions and policies.
## Project Goals
1. **Data Collection**: Gather relevant data from various sources, including epidemiological data, environmental factors, healthcare infrastructure, and socio-economic indicators for African countries.
2. **Data Cleaning and Preprocessing**: Prepare and clean the collected data, addressing missing values, outliers, and inconsistencies to ensure its quality and usability.
3. **Exploratory Data Analysis (EDA)**: Perform in-depth exploratory analysis to uncover patterns, trends, and correlations within the data. Visualize key insights for a better understanding of the malaria situation in Africa.
4. **Statistical Modeling**: Develop predictive models to assess factors influencing malaria prevalence and identify high-risk areas. Explore machine learning algorithms for predictive accuracy.
5. **Geospatial Analysis**: Utilize geospatial data and mapping tools to visualize the geographical distribution of malaria cases and identify hotspots.
6. **Data Visualization**: Create informative and visually appealing graphs, charts, and maps to communicate findings effectively to both technical and non-technical stakeholders.
7. **Recommendations**: Provide actionable recommendations for policymakers and healthcare organizations to improve malaria control and prevention strategies in Africa.
## Dataset Link
This dataset was obtained from Kaggle. Click this link[World Bank Open Data](https://data.worldbank.org/)
## Installation
Clone this repository into your current working directory using this bash command
```
git clone https://github.com/MartinKalema/Malaria-In-Africa-Data-Science-Project
```