This project analyzes global maternal health indicators to monitor progress toward Sustainable Development Goal 3.1, which aims to reduce the global maternal mortality ratio to less than 70 per 100,000 live births by 2030.
The analysis is performed using the AI Kosh SDG dataset and deployed using IBM Cloud Lite services.
Maternal mortality remains uneven across countries due to gaps in healthcare access, skilled birth attendance, adolescent pregnancies, and healthcare expenditure.
This project performs data-driven analysis to identify:
- Trends in maternal mortality
- Regional disparities
- Key factors affecting maternal health outcomes
- Analyze maternal health indicators country-wise
- Identify trends and regional disparities
- Study correlations between healthcare investment and maternal mortality
- Provide visual insights for policy support
Source: AI Kosh – National Indicator Framework SDG Dataset
https://www.data.gov.in/resource/sustainable-development-goals-national-indicator-framework-version-31-2021
Key indicators used:
- Maternal Mortality Ratio (MMR)
- Antenatal Care Coverage
- Skilled Birth Attendance
- Adolescent Birth Rate
- Healthcare Expenditure
- IBM Cloud Lite (Watson Studio)
- Python
- Pandas & NumPy
- Matplotlib & Seaborn
- Jupyter Notebook
- Data Collection from SDG dataset
- Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Correlation Analysis (Pearson)
- Data Visualization
- Higher skilled birth attendance strongly reduces maternal mortality
- Increased healthcare expenditure correlates with lower MMR
- Adolescent birth rate is positively linked to maternal deaths
- Significant disparities exist between regions
- Predictive modeling for MMR forecasting
- Interactive dashboards
- Integration with WHO & World Bank datasets
Bhavishya – IBM Cloud Capstone Project