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@article{200035,
        author = {Rutuja Maruti Ghogare and Nikita Dinesh Surana and Prof. Jeevan Tonde},
        title = {Predicating Environmental Changes Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {40-53},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200035},
        abstract = {Environmental change prediction plays a vital role in supporting climate resilience, sustainable planning, and ecological management. With the growing availability of environmental datasets, machine learning (ML) offers promising techniques for modeling complex patterns and forecasting shifts in climate-related variables. This research investigates the performance of four supervised ML algorithms — Decision Tree, Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN) — in predicting environmental changes using historical data.
The study involved preprocessing the dataset through normalization and encoding, followed by training and testing each model using a consistent data split. Evaluation was conducted using standard classification metrics: accuracy, precision, recall, and F1-score. The comparative analysis revealed that tree-based models, particularly Decision Trees, demonstrated stronger predictive capabilities, likely due to their ability to capture nonlinear relationships and feature interactions.
The findings underscore the importance of algorithm selection in environmental forecasting tasks and highlight the potential of ML-driven approaches in enhancing predictive accuracy. By identifying the most effective model for this application, the research contributes to the development of intelligent systems for environmental monitoring and data-informed},
        keywords = {Machine learning, Supervised Models, Metrices. .},
        month = {May},
        }