This study 🧠 predicts national 😊 happiness scores by evaluating key 💰 socio-economic indicators, including 🏭 economic output, 👨👩👧👦 social support, 🏥 life expectancy, 🗽 individual freedoms, ⚖️ governmental integrity, and 🤲 societal generosity. Data from the World Happiness Report, published by the 🏢 United Nations Sustainable Development Solutions Network, enables predictive modeling through the ⚙️ H2O AutoML platform, revealing complex variable interactions.
The research explores several key 🔍 questions:
- 📊 How do 💰 socio-economic predictors explain national 😊 happiness?
- 🧩 Are core statistical assumptions (e.g., multicollinearity) violated?
- 🔗 How do predictors interact in complex multivariate contexts?
- ⭐ Which variables are the most significant contributors to 😊 happiness?
- 📈 Does regularization enhance model performance?
- ⚙️ What hyperparameters optimize predictive accuracy?
- 🌐 How generalizable is the model across diverse geopolitical contexts?
The study begins with 📚 dataset evaluation, followed by 🤖 machine learning model development using ⚙️ H2O AutoML. Predictive performance is assessed using advanced 🔬 statistical metrics.
- 😊 Happiness Score: Self-reported life satisfaction on a 0-🔟 scale, with 🔟 indicating maximum well-being.
- 🌍 Country: Nation being evaluated.
- 📍 Region: Geopolitical classification.
- 🏆 Happiness Rank: Relative ranking by happiness score.
- 📐 Standard Error: Measurement variability.
- 💰 Economy (GDP per Capita): National economic output.
- 👨👩👧👦 Family: Social and familial support.
- 🏥 Health (Life Expectancy): Public health indicators.
- 🗽 Freedom: Perceived personal autonomy.
- 🚫 Trust (Government Corruption): Institutional integrity and absence of corruption.
- 🤲 Generosity: Charitable behavior and social goodwill.
- 🏚️ Dystopia Residual: Minimum theoretical happiness benchmark.
Model performance is evaluated using several key 📈 statistical measures:
- 📉 Mean Squared Error (MSE): Average prediction error.
- 📊 Root Mean Squared Error (RMSE): Standard deviation of prediction errors.
- 📐 Mean Absolute Error (MAE): Average prediction magnitude.
- 🔢 Root Mean Squared Log Error (RMSLE): Log-scaled prediction error.
- 📈 R-squared (R²): Explained variance proportion.
The following features significantly impact 😊 happiness prediction:
- 🏚️ Dystopia Residual: Most critical determinant.
- 💰 Economy (GDP per Capita): Key economic indicator.
- 🏥 Health (Life Expectancy): Vital well-being metric.
- 🤲 Generosity: Reflects societal altruism.
- 🚫 Trust (Government Corruption): Indicates public trust.
- 📊 Predictive Significance: All variables meaningfully impact 😊 happiness.
- 📐 Assumption Validity: Statistical tests confirm model assumptions.
- 🧩 Multicollinearity: Managed through dimensionality reduction and regularization.
- 🛠️ Regularization Impact: Enhances stability while reducing overfitting.
The project used ⚙️ H2O AutoML for 🤖 model training, selecting a Generalized Linear Model (GLM) as the best-performing algorithm.
- 📉 MSE: 0.0006774
- 📊 RMSE: 0.0260
- 📐 MAE: 0.01989
- 🔢 RMSLE: 0.0049
- 📈 R²: 0.9995
- Slightly lower scores on unseen data (📈 R² = 0.99), indicating strong generalization.
The project follows best practices in 📊 data science, 💾 software engineering, and 🎓 academic research. The codebase is modular, scalable, and well-documented to ensure 📑 reproducibility and 🔬 methodological transparency.
The ⚙️ H2O AutoML framework demonstrates exceptional performance in predicting 😊 happiness scores using 💰 socio-economic indicators. The findings reveal complex relationships among economic, social, and political factors, offering significant insights for evidence-based 🌍 policy development aimed at improving global well-being.
- ⚙️ H2O.AI: https://www.h2o.ai/
- 🎥 YouTube: H2O Channel https://www.youtube.com/user/0xdata