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@article{177908,
        author = {Bijendra Tyagi and Akarsh Srivastav and Richa Pandey and Tanya Singh and Jalaj Vats},
        title = {Machine Learning GUI(Graphical User Interface)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4405-4411},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177908},
        abstract = {This ML GUI project uses attributes such as variance, skewness, kurtosis, and entropy for developing a user- friendly Machine Learning GUI to detect counterfeit banknotes. It combines comparative analysis of Support Vector Machines (SVM), Decision Trees (DT), and Multilayer Perceptrons (MLP) to ensure that the classification is accurate. Its features include data preprocessing, attribute visualization, model training, hyperparameter tuning, and performance evaluation by precision, accuracy, recall, and F1-score. Tools like confusion matrices and feature importance plots enhance the interpretability of models. Decision Trees are transparent, whereas SVM performs best when data are linearly separable, and MLP is more effective with complex, non-linear patterns. Mainly suited for financial institutions that need real-time fraud detection and educationalists interested in machine learning, the platform is built on Scikit-learn and TensorFlow, ensuring scalability and adaptability. This project empowers the user with actionable insights toward data-driven decision-making and effective fraud prevention by combining advanced ML techniques with an intuitive interface.},
        keywords = {},
        month = {May},
        }