Clustering Project

Overview

This project focuses on applying clustering algorithmson a socio-economic dataset to understand patterns and relationships between various indicators, such as income, child mortality, imports, exports, and GDP. Clustering is an unsupervised machine learning technique used to identify patterns or groupings within a dataset. The project aims to identify clusters of countries based on similar characteristics, enabling insights into their socio-economic conditions. The project is presented as an interactive Jupyter Notebook that walks through the implementation, analysis, and visualization of clustering methods.

Table of Contents

• Features
• Project Structure
• Installation
• Usage
• Dependencies
• Configuration
• Project Details
• License

Features

• Data Preprocessing: Cleaning and preparing data for clustering.
• Clustering Algorithms: Implementation of algorithms such as K-Means, Hierarchical Clustering, and DBSCAN.
• Visualization: Includes plots for visualizing clustered data and evaluation metrics.
• Interactive Notebook: Step-by-step explanations and code cells for easy execution and understanding.

Project Structure

Clustering-main/
│
├── Clustering.ipynb                        # Jupyter Notebook with clustering implementation
└── README.md                                # Project documentation

Installation

Prerequisites

• Python 3.8+
• Jupyter Notebook or Jupyter Lab

Setup

1. Clone the repository:

   git clone https://github.com/Ayodimeji1/clustering.git
   cd Clustering-main
   

2. Install the required packages:

   pip install numpy pandas matplotlib seaborn scikit-learn
   

Usage

1. Launch Jupyter Notebook:

   jupyter notebook
   

2. Open Clustering.ipynb in the Jupyter interface and execute the cells step-by-step to follow the clustering analysis and visualizations.

Dependencies

• NumPy: For numerical operations
• Pandas: For data manipulation
• Matplotlib/Seaborn: For data visualization
• Scikit-learn: For clustering algorithms and evaluation
• Jupyter Notebook: For interactive coding environment

Configuration

• Data File: Ensure the data file used for clustering is located in the specified path or modify the notebook to point to the appropriate location.
• Python Environment: Use a virtual environment to manage dependencies and avoid conflicts.

Project Details

The notebook provides a comprehensive guide through:

• Data Loading and Preparation: Reading and preprocessing the dataset.
• Clustering Algorithms:
  • K-Means Clustering: Implementation with a visualization of the elbow method to determine the optimal number of clusters.
  • Hierarchical Clustering: Dendrograms for visual insight into cluster formation.
  • DBSCAN: Density-based clustering for identifying clusters of varying shapes and handling noise.
• Evaluation Metrics: Includes silhouette score and cluster visualization for analysis.

This project is licensed under the MIT License.