- A recommender system’s primary function is tomake recommendations to users. So, in this study, we developed a “Food Recommendation System” that suggests foods based on the age provided and a rating greater than 4. This work connects users to recipe ideas, ingredient lists, and cooking times, serving as a hub for kitchen-related information.This study has the dataset with 12 features, namely name of the dish,style of cooking,category, calorie count, flavor profile, cooking skills, course, healthy, preparation time, ingredient count, rating and age, where data cleansing is done and is divided into 80/20 training and test data, respectively. Decision trees, random forest, K-Nearest Neighbour, and logistic regression models were applied to the training dataset, and the expected value for the test dataset was then produced. For each of these models, a combined confusion matrix and categorization report were created.Based on the confusion_matrix and classification_report numbers, validate the results with Precision, Recall, F1-score, and Accuracy for each model.Calculated the misclassification rate for each algorithm and showed the most accurate model.This dataset consists of 488 records.

Keywords : Decision Tree,K-Nearest Neighbour,Logistic Regression, and Random Forest.