Comparing Machine Learning Models ⚖️

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This presentation explores the challenge of identifying fake reviews in e-commerce using machine learning. Starting with an overview of the dataset (40,412 reviews) and the impact of fraudulent reviews, it defines detection challenges and proposes automated ML solutions. Methodologies include data preprocessing (text cleaning, TF-IDF encoding) and model selection (Logistic Regression, SVM, and Random Forest). Results highlight SVM’s superior accuracy and robustness, leading to its selection...