Clustering - Countries by Socio-economic and Health Indicators (Unsupervised Learning)

Overview

This project applies unsupervised clustering techniques to categorize countries based on socio-economic and health indicators, facilitating analysis of their overall development levels.

Project Goals

• Classify countries into meaningful clusters using unsupervised learning.
• Visualize and analyze patterns across socio-economic and health-related data.
• Utilize dimensionality reduction and clustering techniques for accurate grouping.

Steps Included

1. Exploratory Data Analysis (EDA)

• Initial data exploration
• Data cleaning and preprocessing

2. Data Visualization

• Geographical Maps
• Heatmaps
• Histograms for understanding feature distributions

3. Data Scaling

• Normalization to ensure equitable influence of all features

4. Feature Engineering

• Principal Component Analysis (PCA) for dimensionality reduction and feature extraction

5. Clustering Analysis

• Elbow Method for optimal cluster determination
• Silhouette Score Analysis for validating cluster quality

6. K-Means Clustering

• Application of the K-Means algorithm (unsupervised learning) to segment countries

How to Run

To run this project locally:

1. Clone the repository:

   git clone https://github.com/azizzoaib786/countries-dataset-clustering.git

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the notebook:

   • Open countries-dataset-clustering.ipynb in Jupyter Notebook or JupyterLab.
   • Execute all cells (Run All).

Requirements

• Python (3.7 or higher recommended)
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• geopandas (for map visualizations)
• Jupyter Notebook/JupyterLab

Contributions

Contributions are encouraged! Fork the repository, make improvements, and submit a pull request.

Contact

• Email: [email protected]

License

This project is licensed under the MIT License.