A Comparative Analysis of Machine Learning Models: SVM, Naïve Bayes, Random Forest, and LSTM in Predictive Analytics

Machine learning models have recently developed into a crucial predictive analytics tool, driving progress in a variety of industries. Support Vector Machine (SVM), Naive Bayes, Random Forest, and Long Short-Term Memory are four well-known machine learning algorithms that are thoroughly examined and compared in this work (LSTM). By assessing these models on many parameters, including accuracy, precision, recall, and computing efficiency, the study conducts an in-depth investigation. To ensure the validity and relevance of the findings, a broad dataset is used. The comparison analysis highlights the distinct advantages and disadvantages of each model, providing information about how well suited they are for various applications. The analysis explores efficiency and interpretability as well, offering helpful direction for scholars and practitioners. The work also advances the field of predictive analytics by providing actual data on the effectiveness of these algorithms, paving the path for more in-depth and efficient application of machine learning models across a range of fields.

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