Abstract:
This research applies machine learning methods to estimate and present the calorie
content of traditional Bangladeshi cuisine, empowering individuals to make informed
dietary choices and encouraging healthier eating habits. The study involves a
meticulously curated dataset of ingredients and recipes for training machine learning
models. Three models, Linear Regression, Decision Tree, and Random Forest
regressions are evaluated based on their performance using metrics such as Mean
Squared Error (MSE) and R-squared (R²). Computational tools consolidate ingredient
calorie data, integrate datasets, and compute total calories for each recipe. Data is split
into training and testing sets, features are engineered, and models are trained and
evaluated. Visualization metrics compare model accuracy, including scatter plots and
bar charts. The analysis identifies Linear Regression as the top-performing model,
achieving the lowest MSE of 15.75 and the highest R² score of 0.9999, indicating high
predictive accuracy. The research adheres to ethical data practices and proposes a
sustainability plan to mitigate the environmental impact of computational processes.
By integrating machine learning into restaurant menus, this study offers an effective
means of providing caloric information, thus contributing to global health goals focused
on preventing diet-related health issues. Additionally, the research suggests
opportunities for future studies to examine the societal acceptance and adoption of such
technologies within Bangladeshi culture. This innovation has the potential to empower
consumers to make healthier dietary decisions and foster a culture of healthy eating,
thereby enhancing public health.
