Druvitha H K MSc Student – University of Leeds
This project explores global public opinion on climate change using the People's Climate Vote dataset. The analysis aims to identify patterns in climate awareness, policy support, and regional differences in environmental concern.
Using Python and data visualization techniques, the project demonstrates a full data science workflow from raw data preprocessing to interactive dashboards.
The analysis provides insights into how climate perception varies across different countries and populations.
- Explore global climate opinion data
- Identify key patterns in public support for climate action
- Clean and transform real-world survey data
- Produce meaningful visualizations and dashboards
- Demonstrate practical data analysis and storytelling
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- Python
- Pandas
- NumPy
- Jupyter Notebook
- Tableau
- Data Visualization
- Exploratory Data Analysis (EDA)
The dataset used in this project is based on the People's Climate Vote survey, which collects responses from individuals across multiple countries regarding their attitudes toward climate change and environmental policies.
The dataset includes:
- Country-level responses
- Public opinion on climate action
- Demographic indicators
- Climate awareness indicators
Import climate survey dataset from global public opinion data sources.
- Handle missing values
- Standardize variables
- Prepare dataset for analysis
Analyze relationships between climate concern, geography, and demographic factors.
Create visual representations to understand patterns in climate perception across different regions.
Build interactive dashboards using Tableau to communicate insights effectively.
data/ Contains raw and cleaned datasets used for analysis.
notebooks/ Jupyter Notebook containing Python code for data preprocessing and analysis.
tableau_dashboard/ Tableau files used to build interactive dashboards.
README.md Project documentation and overview.
- Climate concern varies significantly between countries.
- Younger populations tend to support stronger climate action.
- Public opinion reveals regional differences in climate awareness and policy support.
- Data cleaning and preprocessing
- Exploratory data analysis
- Data visualization and dashboard creation
- Analytical storytelling with data
- Working with real-world datasets
- Apply machine learning models to predict climate opinion trends.
- Build an interactive web dashboard.
- Expand the analysis with additional demographic data.
- Real-world climate dataset
- Data cleaning using Python
- Exploratory Data Analysis (EDA)
- Tableau dashboard for visualization
- End-to-end data science workflow
This project is released under the MIT License.