This project analyzes socio-economic factors impacting child mortality rates through unsupervised learning techniques. By applying clustering methods, it identifies patterns and groupings within the data, helping policymakers target effective interventions.
- Data Cleaning and Preprocessing: Handling missing values, removing duplicates, and preparing data for analysis.
- Exploratory Data Analysis: Visualizations and summaries for deeper data understanding.
- Modeling: Implementation of clustering algorithms, including KMeans, Agglomerative Clustering, Gaussian Mixture Models, and DBSCAN.
- Conclusion: Key insights and implications drawn from the analysis.
The analysis indicates strong correlations between socio-economic factors and child mortality rates, offering a foundation for focused health initiatives.
Future work includes refining clustering models, incorporating additional data, and exploring predictive modeling techniques.
- Python
- Libraries:
pandas,numpy,scikit-learn,matplotlib,seaborn
I welcome feedback, suggestions, and reviews for this project.
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