Unsupervised Learning: Clustering & PCA

Overview

This project focuses on unsupervised learning techniques, particularly Principal Component Analysis (PCA) and K-Means Clustering, to analyze country-level socio-economic and health indicators. The goal is to identify patterns and determine which countries are in dire need of aid.

Dataset

The dataset used in this project is Country-data.csv, which includes various economic, health, and social indicators for different countries.

Features in the dataset:

• country: Name of the country
• child_mort: Child mortality rate per 1000 births
• exports: Exports as a percentage of GDP
• health: Health expenditures as a percentage of GDP
• imports: Imports as a percentage of GDP
• income: Per capita income in USD
• inflation: Inflation rate
• life_expec: Average life expectancy
• total_fer: Total fertility rate
• gdpp: GDP per capita in USD

Project Structure

• Unsupervised_Learning.ipynb: Jupyter Notebook containing data preprocessing, PCA, and clustering implementation.
• Country-data.csv: The dataset used for analysis.
• README.md: Documentation for the project.

Implementation Steps

1. Data Preprocessing

• Load the dataset and check for missing values.
• Perform exploratory data analysis (EDA) and visualize distributions using boxplots.
• Identify and remove outliers using the Z-score method.
• Standardize the dataset before applying PCA.

2. Principal Component Analysis (PCA)

• Standardize the dataset using StandardScaler.
• Apply PCA to reduce dimensionality while retaining maximum variance.
• Select the optimal number of components by analyzing cumulative variance explained.
• Transform the dataset using Incremental PCA with the selected components.

3. Clustering using K-Means

• Apply K-Means clustering on the PCA-transformed data.
• Use the elbow method to determine the optimal number of clusters.
• Assign cluster labels to each country.
• Identify countries that are in dire need of aid based on cluster assignments.

Results & Insights

• PCA helped reduce the dimensionality while preserving important variance.
• K-Means clustering grouped countries into four distinct clusters.
• Countries in Cluster 1 were identified as those most in need of aid, based on factors like low GDP per capita, high child mortality rates, and low life expectancy.

Dependencies & Setup

Requirements

• Python 3.x
• Jupyter Notebook
• Required libraries: numpy, pandas, matplotlib, seaborn, sklearn, scipy

Installation

1. Clone the repository:

   git clone https://github.com/your-username/your-repo.git
   cd your-repo

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook:

   jupyter notebook Unsupervised_Learning.ipynb

Acknowledgments

• The dataset was used for educational purposes in exploring PCA and K-Means Clustering.
• Libraries such as Scikit-Learn, Pandas, and Seaborn were used for analysis and visualization.

License

This project is open-source and available under the MIT License.