Analytic-Cup-TUM: The business model of an organization is to develop and leverage large language models to suggest new recipes to their customers based on a monthly fee. My task is to develop a model that can predict the outcome of the column 'Like', which indicates whether a customer likes a recipe (your model predicts Like=1) or dislikes it (your model predicts Like=0). Logistic regression, random forest, and gradient boosting have been used in my data pipeline, and apply GridSearchCV in my pipeline to find the most suitable parameter for all three methods and then fit the training data on the best-suited method and get the best accuracy.