Unsupervised Learning - Cluster Streamlit Dashboard

Link to Dashboard

Libraries Used:

1. Duckdb
2. Matplotlib
3. Seaborn
4. Numpy
5. Pandas
6. os
7. plotly
8. pickle
9. UMAP
10. Streamlit

Tech Stack:

1. Python
2. SQL

Problem Statement

HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. HELP International have been able to raise around $ 10 million. This money now needs to be allocated strategically and effectively. Hence, inorder to decide the selection of the countries that are in the direst need of aid, data driven decisions are to be made. Thus, it becomes necessary to categorise the countries using socio-economic and health factors that determine the overall development of the country. Thus, based on these clusters of the countries depending on their conditions, funds will be allocated for assistance during the time of disasters and natural calamities. It is a clear cut case of unsupervised learning where we have to create clusters of the countries based on the different feature present.

Aim

To cluster countries based on numerical features.
It is an Unsupervised Learning problem statement.

Dataset Attributes

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.

Notebook Contents

Dataset Information
Exploratory Data Analysis (EDA)
Summary of EDA
Feature Engineering
Modeling
Conclusion