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In my Bangla news categorization project, I utilized XGBoost for efficient pattern recognition, SVM for handling non-linear relationships, and an ensemble of Random Forest, AdaBoost, and Logistic Regression to collectively enhance precision. This diverse approach ensures robust and accurate classification of Bangla news articles.

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Utilizing XGBoost for pattern recognition, SVM for non-linear relationships, and an ensemble of Random Forest, AdaBoost, and Logistic Regression, my Bangla news categorization achieves precision through diverse modeling. This comprehensive approach ensures accurate classification, capturing linguistic nuances and cultural references in Bangla news articles.

Dataset Link: https://www.kaggle.com/datasets/furcifer/bangla-newspaper-dataset/data

Project done by: Md. Injamul Haque && Shakil Rana

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In my Bangla news categorization project, I utilized XGBoost for efficient pattern recognition, SVM for handling non-linear relationships, and an ensemble of Random Forest, AdaBoost, and Logistic Regression to collectively enhance precision. This diverse approach ensures robust and accurate classification of Bangla news articles.

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nlp sentiment-analysis text-classification svm ml logistic-regression xgboost-classifier adaboostclassifier

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