With the development of technology, the importance of recommendation algorithms in online transactions is gradually increasing. The development of machine learning is essential to improve the performance of recommendation algorithms. Therefore, a suitable model is urgently needed for recommendation algorithms. In order to evaluate the performance of classical machine learning algorithms against newer machine learning algorithms, four models were selected, namely Decision Tree model, Random Forest model, Gradient Boosting Decision Tree model and XGBoost model. They were subjected to regression analysis on the same dataset, and the final scores were derived from the four parameters r2_score, MSE, MAE, and expected variance. The article selects a set of recommendation dataset from Amazon platform and processes the noisy data in the dataset. The data is processed by three methods, namely direct deletion, character digitisation and natural language processing, to obtain a valid dataset of 19, 676 items, and divided into training and prediction sets according to 7:3. Next, the models included in the sklearn library in PYTHON were used for regression analysis to obtain the performance scores of each model. The Decision Tree model had the lowest score of 0.70584 and the Gradient Boosting Decision Tree model had the highest score of 0.85281. In addition, in general, the Decision Tree model scored much worse than the other three models, while the other three models scored more similarly.

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