# Data Mining (DM) course project - Part2. Clustering

## Dataset

***Link: [Unsupervised Learning on Country Data](https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data)***

### Objective:
To categorise the countries using socio-economic and health factors that determine the overall development of the country.

### Dataset dictionary

| Column Name | Description                                                                                                      |
| ----------- | ---------------------------------------------------------------------------------------------------------------- |
| country     | Name of the country                                                                                              |
| child_mort  | Death of children under 5 years of age per 1000 live births                                                      |
| exports     | Exports of goods and services per capita. Given as %age of the GDP per capita                                    |
| health      | Total health spending per capita. Given as %age of GDP per capita                                                |
| imports     | Imports of goods and services per capita. Given as %age of the GDP per capita                                    |
| Income      | Net income per person                                                                                            |
| Inflation   | The measurement of the annual growth rate of the Total GDP                                                       |
| life_expec  | The average number of years a new born child would live if the current mortality patterns are to remain the same |
| total_fer   | The number of children that would be born to each woman if the current age-fertility rates remain the same.      |
| gdpp        | The GDP per capita. Calculated as the Total GDP divided by the total population.                                 |

## Contents

1. Dataset overview
1. Feature engineering
1. Data preprocessing
1. Dimensionality reduction using:
    - Manual approach depending on data analysis
    - PCA
1. Clustering using K-Means
1. Clusters modeling
    - 3D views
    - Boxplots showing data distribution
    - Real-world maps