This repository contains the code and resources for predicting happiness levels in Europe using data from the European Social Survey 10, conducted between 2020 and 2022. The goal of this project is to explore factors that influence happiness and build a predictive model to forecast happiness levels across different European countries.
The dataset used in this project is the European Social Survey 10 (ESS10) data. The ESS is a biennial survey that collects data on various aspects of social attitudes, beliefs, and behaviors across Europe. The dataset includes information on demographics, socio-economic factors, and self-reported happiness levels of respondents from multiple European countries.
Data Preprocessing: Cleaning and preparing the dataset for analysis, including handling missing values, encoding categorical variables, and scaling numerical features. Exploratory Data Analysis (EDA): Exploring the relationships between different variables and happiness levels through visualizations and statistical analysis. Feature Engineering: Creating new features or transforming existing ones to improve model performance. Model Building: Training and evaluating machine learning models to predict happiness levels based on the selected features. Model Evaluation: Assessing the performance of the trained models using appropriate evaluation metrics such as accuracy, MAE. Model Interpretation: Analyzing the importance of different features in predicting happiness and interpreting the model results.
The results of this project include insights into the factors influencing happiness levels in Europe and the predictive performance of the developed models. These findings can be used to better understand societal well-being and inform policies aimed at improving happiness and quality of life across European countries.
European Social Survey (ESS) for collecting and providing the valuable dataset.