Peoples' Climate Vote 2024 – Data Science Analysis

This repository contains a full data science analysis of the UNDP Peoples’ Climate Vote 2024 dataset, focusing on global climate attitudes, renewable energy urgency, and support for strengthening climate commitments.

Project Overview

• Dataset: UNDP Peoples’ Climate Vote 2024 (73 countries)
• Analysis Scope:
  • Exploratory Data Analysis (EDA) on age-based differences
  • Predictive modeling of climate commitment support
  • Country-level clustering of climate attitudes

Methods & Techniques

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Logistic Regression & Random Forest
• K-Means clustering
• Data visualization with Matplotlib & Seaborn

Key Insights

• Younger age groups consistently show stronger urgency for renewable energy transition.
• Demographic factors (country, age) outperform attitudinal variables in prediction.
• Global climate attitudes are relatively consistent across regions.

Repository Structure

• data/ – Raw survey dataset
• notebooks/ – Jupyter notebooks for analysis and modeling
• reports/ – Final academic reports (PDF)
• requirements.txt – Python dependencies

Tech Stack

• Python 3.10
• pandas, numpy
• matplotlib, seaborn
• scikit-learn

Disclaimer

This project is shared for educational and portfolio purposes only.