Global-Insights-Clustering

Global Insights: Country Clustering Analysis Using Unsupervised Machine Learning

Overview

With so many disasters happening all around the world, it’s important for us to know where aid should go first. There are countries that are self-sustained and there are other countries that rely on funds to be sent. So how can we determine what countries need aid first? HELP International makes a difference in the lives of everyday people across underdeveloped regions. It’s important for us to categorize countries based on socio-economic and health factors to assess their overall development. Through this final project, I hope to sort countries into 4 categories, Upper-Middle Developed, Lower-Middle Developed, Highly Developed and Least developed countries based on features like Child mortality rate, Import / Exports of goods, Total health spending, Net income per person, life expectancy, and the GDP per capita. Overall aiming to help countries that need the most aid.

Features

• Data Exploration: The notebook provides a comprehensive exploration of the dataset, including summary statistics, visualization of distributions, and identification of potential outliers.
• Preprocessing: Data preprocessing steps are included to clean and prepare the dataset for clustering analysis. This may involve handling missing values, feature scaling, and encoding categorical variables.
• Clustering Algorithms: Several clustering algorithms are implemented and evaluated in the notebook. Common algorithms such as K-means, DBSCAN, and hierarchical clustering may be utilized to cluster the data points.
• Evaluation Metrics: Performance metrics for evaluating clustering results are discussed and computed. These metrics help assess the quality of clustering and aid in selecting the most appropriate algorithm and parameter settings.
• Visualization: The notebook includes visualizations of clustering results to aid in interpretation and understanding. This may involve plotting clusters in feature space or visualizing cluster centroids.

Requirements

• Python 3.x
• Jupyter Notebook
• Required Python packages (NumPy, pandas, scikit-learn, matplotlib, seaborn)

Usage

1. Clone the repository to your local machine:

git clone https://github.com/fuadh246/Global-Insights-Clustering.git

2. Navigate to the model directory:

cd Global-Insights-Clustering/model

3. Open the Cluster.ipynb notebook using Jupyter Notebook:

jupyter notebook Cluster.ipynb

4. Follow the instructions within the notebook to execute code cells and explore the clustering analysis.

Contribution

Contributions to this project are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT