lifesat-ml 0.1.0

Life Satisfaction Prediction using Machine Learning and SHAP

What Drives Life Satisfaction?

A Machine Learning & Explainable AI Study

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Project Overview

This project explores the key factors that influence life satisfaction using a large-scale social survey dataset.(TGSS2024 https://www.tgss.org.tr/iletisim?type=dataset)

By combining machine learning models with explainable AI (SHAP), the goal is not only to predict life satisfaction but also to understand why people feel more or less satisfied with their lives.

This project includes a command-line interface (CLI) that allows you to run the full machine learning pipeline without modifying code.

You can:

• Train different models
• Save and load trained models
• Export results as JSON
• Control plot generation
• Reproduce experiments easily

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Objectives

• Predict individual life satisfaction (lifesat)
• Identify the most important social, economic, and psychological factors
• Provide interpretable insights using explainable AI techniques

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Dataset

The dataset contains hundreds of variables related to:

• Demographics (age, gender, education)
• Social trust and relationships
• Work and income
• Religion and beliefs
• Health and well-being
• Political and social attitudes

⚠️ The dataset required extensive preprocessing due to:

• Special missing values (-90, -88, -99)
• High dimensionality (500+ columns)

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Data Preprocessing

Key steps:

• Converted special values (-90, -88, -99) → NaN
• Removed columns with high missing rates
• Imputed remaining missing values using median
• Reduced dimensionality through feature engineering

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Feature Engineering

Raw survey data was transformed into meaningful indices:

• Social Trust Index → trust in people, fairness, neighbors
• Discrimination Index → perceived discrimination across groups
• Work Stress Index → stress, fatigue, work-life balance
• Religiosity Score → religious practices and beliefs
• Social Activity Score → frequency of social interactions
• Economic Satisfaction → income and job satisfaction

This reduced 500+ variables into a compact, interpretable feature set.

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How To Run

All commands should be executed from the project root:

python -m src.cli

Available Models

You can choose between:

• linear → Linear Regression
• rf → Random Forest (default)
• xgb → XGBoost

CLI Usage Examples

🔹 Train a model

python -m src.cli --model-type rf

🔹 Train and save model

python -m src.cli --model-type rf --save-model

🔹 Save results as JSON

python -m src.cli --save-json

🔹 Save both model and results

python -m src.cli --model-type xgb --save-model --save-json

🔹 Load a trained model

python -m src.cli --load-model --model-path models/model.pkl

🔹 Load results from JSON

python -m src.cli --load-json

🔹 Disable plots

python -m src.cli --no-plot

🔹 Custom output paths

python -m src.cli \
  --model-type xgb \
  --save-model \
  --save-json \
  --model-path models/xgb_model.pkl \
  --json-path models/xgb_results.json \
  --output-dir results/

How It Works

• Loads dataset from data/
• Cleans and preprocesses data
• Trains or loads a model
• Evaluates performance (MAE)

Saves:

• Model (.pkl)
• Results (.json)
• Plots (.png)
• Output Files
• Models
• models/model.pkl
• Results (JSON)
• models/results.json

Example:

{
    "model_type": "rf",
    "mae": 0.85
}

• Plots results/lifesat_distribution.png

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Exploratory Data Analysis

Feature Importance

Life Satisfaction Distribution

Life Satisfaction by Gender

Life Satisfaction vs Income

Life Satisfaction vs Social Trust

Life Satisfaction vs Work Stress

Life Satisfaction by Age

SHAP Summary

SHAP Feature Importance

SHAP Individual Explanation

Machine Learning Models

Three models were trained and evaluated:

• Linear Regression (baseline)
• Random Forest Regressor
• XGBoost Regressor

Model Performance (MAE)

Model	MAE
Linear Regression	0.7439083518939844
Random Forest	0.7508413001912044
XGBoost	0.8352283612036112

👉 Tree-based models outperformed linear regression, indicating non-linear relationships in the data.

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Feature Importance

Random Forest Feature Importance

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Explainability with SHAP

To understand model predictions, SHAP (SHapley Additive exPlanations) was used.

SHAP Summary Plot

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SHAP Feature Importance

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Key Findings

• Social trust is one of the strongest predictors of life satisfaction
• Work stress has a significant negative impact
• Economic satisfaction contributes positively but less than social factors
• Social activity is strongly associated with higher well-being

👉 Overall, social and psychological factors outweigh purely economic ones

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Conclusion

This project demonstrates that:

Life satisfaction is driven more by human relationships and trust than by material factors alone.

By combining machine learning with explainability, we can uncover meaningful insights about society—not just make predictions.

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Technologies Used

• Python
• pandas, numpy
• matplotlib, seaborn
• scikit-learn
• XGBoost
• SHAP

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📁 Project Structure

life-satisfaction-ml/
│
├── data/
├── notebooks/
├── results/
├── models/
├── src/
├── requirements.txt
└── README.md

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Future Improvements

• Hyperparameter tuning
• Cross-validation
• Deploying as a web app (Streamlit)
• Interactive dashboard (Power BI / Tableau)

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Author

Cetin ERDEM

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⭐ If you found this project interesting

Feel free to ⭐ the repo and connect!