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@article{209591,
        author = {Vaishnavi Jondhale and Asmita Musale and Rehuti Sutar},
        title = {E-Waste Prediction and Recycling Behavior Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {2707-2712},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209591},
        abstract = {Electronic waste (e-waste) is increasing due to the growing use, replacement, and disposal of electronic devices such as mobile phones, laptops, computers, televisions, and other electronic products. Improper disposal of these devices can result in environmental problems and loss of recyclable materials.
This research proposes a data-driven study to understand the factors associated with e-waste generation and recycling behaviour. Data will be collected from individuals or households using a structured questionnaire. The collected data will be analyzed using statistical techniques to identify important patterns and relationships.
Machine Learning techniques will be applied for two prediction tasks. The first task will focus on predicting the amount of e-waste generated based on factors such as electronic device ownership, replacement frequency, and disposal behaviour. The second task will focus on predicting recycling behaviour using factors such as awareness, recycling knowledge, accessibility, and previous recycling practices. Excel will be used for initial data organization and analysis, Python will be used for statistical analysis and Machine Learning, and MongoDB will be used for structured data storage. The study aims to demonstrate how predictive analytics can be applied to understand and predict e-waste generation and recycling behaviour.},
        keywords = {E-Waste, Data Analytics, Recycling Behaviour, Machine Learning, Predictive Analytics, Statistical Analysis},
        month = {October},
        }