International Journal of Scientific Research and Engineering Development

The increasing prevalence of unhealthy eating habits due to emotional factors has become a significant concern in modern lifestyles. This paper presents the design and implementation of a Smart Diet Planner with Emotional Eating Detection, a desktop-based application developed using Python and Tkinter. The system aims to identify emotional eating patterns by analyzing user- provided textual inputs through techniques from Natural Language Processing and Machine Learning. The proposed system utilizes a text classification model based on Term Frequency–Inverse Document Frequency and Logistic Regression to detect user emotions across multiple categories such as stress, sadness, anxiety, and mindfulness. Based on the detected emotional state and user-entered dietary data, the application identifies patterns of emotional eating and provides personalized diet recommendations. Additionally, the system integrates health metrics such as Body Mass Index (BMI), Basal Metabolic Rate (BMR), and Total Daily Energy Expenditure (TDEE) to generate goal-oriented nutritional guidance. Features such as calorie tracking, mood history visualization using graphical analysis, and weekly behavioral reports enhance user awareness and promote healthier decision- making. The application operates entirely ofline, ensuring accessibility and ease of use without dependency on cloud infrastructure. The results demonstrate that integrating emotion detection with diet planning can significantly improve awareness of eating behaviors and support the development of healthier lifestyle habits.

Zaid Sajid Tukdi, Anjali Vishal Salve, Israr Shafi Shaikh,"Smart Diet Planner with Emotional Eating Detection" International Journal of Scientific Research and Engineering Development, V9(2): Page(2324-2327) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.