Data Sciencebeginner
Malaria Risk Prediction
Analyze health data to find risk patterns and recommend interventions across African communities.
Learn First
- 1
Introduction & Ethics
Introduction
Understand the data sources, formats, and the ethics of working with African data.
- Where the data comes from
- What questions it can answer
- Responsible use
- 2
Data Cleaning Fundamentals
Data Cleaning
Handle missing values, outliers, and inconsistencies.
import pandas as pd df = pd.read_csv("data.csv") df = df.dropna() - 3
Exploratory Data Analysis
Exploratory Data Analysis
Visualize patterns across regions and time.
- Distributions
- Correlations
- Trends by district
- 4
Modeling & Evaluation
Modeling
Build and evaluate a simple model, then write your recommendations.
- Train/test split
- Baseline model
- Metrics that matter
Dataset
- Title
- Sub-Saharan Malaria Indicators
- Country
- Multi-country
- Source
- WHO / DHS (sample)
- License
- CC BY 4.0
- Ethics note
- Aggregated, de-identified. Do not attempt re-identification of communities.
Instructions
Goal
Predict malaria risk by region and recommend where interventions should go first.
The data
Use the linked Malaria in Africa dataset (open it from the Dataset section, or in your Colab notebook).
Steps
- Open the starter notebook in Colab (button on the right).
- Load and clean the dataset.
- Explore trends by country and year.
- Build a simple risk model and evaluate it.
- Write 3–5 recommendations for decision-makers.
Deliverables
- Your Colab notebook link
- A short report or slides (optional)
How to submit
Sign in, then paste your Colab / GitHub / report links in Submit your work. A mentor reviews it and you earn a certificate.
Rubric
- Data Cleaning20 pts
- Exploratory Analysis20 pts
- Visualizations20 pts
- Model / Evaluation20 pts
- Report / Recommendations20 pts
- Total100 pts