Authors :
M.S.N.V. Jitendra; Maddula Lakshmi Jyosna; Sai Sri Varsha Veeraghanta; Shanmuk Srinivas A;, K Bhargav
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/445DHyf
Abstract :
- 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.
- 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.