Well-being Indicators Analysis

Inspiration

We were inspired by the need to make sense of vast social data to identify key factors impacting well-being. With data being abundant but underused, we wanted to build a tool that uncovers meaningful patterns and offers insights for better decision-making.

What it does

Well-being Indicators Analysis takes a large dataset of global well-being metrics(OECD-wellbeing dataset.) and performs interactive analysis to:

• Identify trends and correlations across indicators
• Visualize data through dynamic Plotly charts
• Provide summaries and actionable suggestions based on patterns found

It transforms complex data into simple, digestible insights.

How we built it

• Used the OECD-wellbeing dataset from the Analytics Vibeathon challenge
• Built visualizations and filtering logic entirely with Plotly, enabling interactive exploration of relationships between well-being indicators along with pattern identifications
• Generated summaries and suggestions based on observed patterns

Challenges we ran into

• Handling missing and inconsistent data
• Interpreting meaningful relationships without overfitting
• Designing a clean, interactive interface that makes insights clear for all users

Accomplishments that we're proud of

• Turned raw, complex data into an interactive, insight-driven platform
• Used only Plotly to power the entire filtering and analysis workflow
• Delivered clear, actionable findings from a massive dataset

What we learned

• Deeper skills in data cleaning, correlation analysis, and visualization
• The power of interactive tools like Plotly for storytelling with data
• How to communicate data insights in a user-friendly way

What's next for Well-being Indicators Analysis

• Refine the co-relation between parameter identifying so that we have better story/insights to tell to the users
• Enable user-uploaded data for custom analysis
• Explore predictive insights using machine learning

Built With

• plotly