Global Happiness Trends Analysis
This project explores what drives happiness across countries worldwide using the World Happiness Report dataset. Conducted with Python in Kaggle, the analysis examines how economic factors, social support, health, and freedom influence national happiness levels. The study reveals key patterns and correlations that help explain why some countries consistently rank higher in happiness. These insights were then transformed into an interactive Tableau dashboard, making complex data accessible to a broader audience interested in understanding global well-being trends.
Project Overview
- Project: Data Cleaning and Analysis
- Date: November 2025
- Category: Data Analysis
- Tools: Python, Kaggle
- Role: Data Analyst
Project Details
Goal & context. This Python-based analysis uses World Happiness Report data to uncover what drives national happiness and how it varies across countries and time. The work was done in Kaggle with the aim of producing clear, validated insights that could later be turned into an interactive Tableau dashboard for a broad audience.
Data cleaning & preparation. Raw happiness and socio-economic data were loaded with pandas; missing values were identified and handled (imputation or exclusion depending on the variable). Country names and year formats were standardized so that multi-year and cross-country comparisons were consistent. Column types were checked and corrected (numeric, categorical, dates) to support both statistical summaries and visualizations.
Exploratory analysis & validation. Exploratory data analysis (EDA) was used to inspect distributions, outliers, and basic summary statistics. Correlation analysis and simple visualizations (e.g., scatter plots, time series) were used to explore how GDP per capita, social support, life expectancy, freedom, generosity, and corruption perception relate to happiness scores. Regional and time-based patterns were examined to ensure findings were robust before being passed to the dashboard.
Visualization & documentation. Key results were visualized in the notebook (e.g., trend lines, correlation heatmaps, bar charts) using libraries such as matplotlib/seaborn. The narrative was structured so that each insight was justified by the data and clearly stated, making it straightforward to translate the analysis into a Tableau story.
Outcome. The analysis produced a documented, reproducible Python notebook that identifies the main drivers of global happiness and validates the insights that later power the Global Happiness Trends Tableau dashboard.
Analysis Overview
- Data Cleaning & Preparation
Processed raw World Happiness Report data by handling missing values, standardizing country names, and ensuring consistency across years. - Exploratory Data Analysis (EDA)
Explored global happiness trends using descriptive statistics and visualizations to understand overall patterns and distributions. - Correlation Analysis
Analyzed relationships between happiness scores and socio-economic factors such as GDP per capita, social support, life expectancy, freedom, generosity, and corruption perception. - Trend & Regional Analysis
Examined changes in happiness scores over time and compared regional patterns across different countries. - Insight Validation for Dashboard
Identified and validated key insights to ensure accurate and meaningful translation into the Tableau dashboard.
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