Analyzing Plastic Waste Patterns with Data Science

Plastic pollution is one of the most pressing environmental challenges, affecting oceans, wildlife, and human health. With millions of tons of plastic produced annually, understanding waste generation, recycling rates, and pollution hotspots is crucial. Traditional analysis often lacks real-time insights and geographical granularity. Data science enables the extraction of patterns from large-scale waste data, helping formulate targeted policies and drive sustainability initiatives worldwide.

By collecting datasets on plastic production, recycling rates, ocean waste accumulation, and country-wise plastic consumption, data science techniques can uncover trends, predict future waste patterns, and suggest interventions. Machine learning models like clustering (k-means), regression, and time series forecasting help visualize global waste hotspots, evaluate the impact of recycling policies, and optimize plastic reduction strategies based on data-driven evidence.

Datasets containing global and national plastic production, consumption, recycling rates, and ocean plastic waste are collected. Preprocessing involves cleaning, normalizing, and structuring data across different regions and time periods. Machine learning models and visualization tools then help identify key contributors, predict future waste trends, and recommend optimized waste management strategies. Dashboards are created for real-time monitoring of plastic pollution indicators worldwide.

• Collect plastic waste datasets from sources like Our World in Data, Ocean Conservancy, or environmental research reports.
• Clean, merge, and normalize datasets to create a unified, comprehensive database across regions and years.
• Apply clustering to group countries or regions based on plastic waste generation and recycling behavior patterns.
• Use regression and time series analysis to predict future plastic waste trends and the impact of current policies.
• Visualize data through interactive dashboards highlighting plastic waste statistics, reduction progress, and priority action areas.

• Our World in Data – Plastic Pollution Dataset
• OECD Global Plastics Outlook
• Ocean Conservancy – Plastic Waste Reports