Predicting Country Happiness¶

Motivation:¶

Problem¶

Different countries have different factors that decide the happiness ratings, and if we can detect which aspects of a country contribute the most to a positive rating from its residents, we can commit to more resources to better certain aspects of lives

Solution¶

This Dataset gives us multiple describing factors of a country and its corresponding happiness score The goal of this project is to identify which factors contribute the most to a high happiness score for a country, which we can find by comparing correlations for various factors

Impact¶

If successful, this work can help to understand which aspects should be more resourced when trying to elevate the quality of life in a developing nation. More resources can be allocated to specific drivers of a country, and it can help speed up the growth of a country to make it a more desirable place to reside

Dataset¶

Detail¶

We will use a Kaggle Dataset of World Happiness of 2019, to observe the following features for each country:

  • country
  • Happiness Rank (all countries ranked in descending order for happiness score)
  • Economy (GDP per Capita)
  • Family
  • Health (Life Expectancy)
  • Freedom (survey)
  • Generosity (survey)
  • Trust (Government Corruption)
In [1]:
import pandas as pd

df = pd.read_csv('happiness_2019.csv')
df.head(8)
Out[1]:
Overall rank Country or region Score GDP per capita Social support Healthy life expectancy Freedom to make life choices Generosity Perceptions of corruption
0 1 Finland 7.769 1.340 1.587 0.986 0.596 0.153 0.393
1 2 Denmark 7.600 1.383 1.573 0.996 0.592 0.252 0.410
2 3 Norway 7.554 1.488 1.582 1.028 0.603 0.271 0.341
3 4 Iceland 7.494 1.380 1.624 1.026 0.591 0.354 0.118
4 5 Netherlands 7.488 1.396 1.522 0.999 0.557 0.322 0.298
5 6 Switzerland 7.480 1.452 1.526 1.052 0.572 0.263 0.343
6 7 Sweden 7.343 1.387 1.487 1.009 0.574 0.267 0.373
7 8 New Zealand 7.307 1.303 1.557 1.026 0.585 0.330 0.380

Machine Learning application¶

We will use correlation values for various combinations of contributing factors to predict which factors contribute to high happiness levels within different countries, to help deveoping countries determine resource allocation when trying to have a higher quality of life.

We will also use a machine learning model to be able to predict happiness score based on the rest of the countries factors.