• Daggupati Giridhar Praveen
• Dr. B. Sai Jyothi
• Battula Sai Mahesh
• Baipeneedi Deep Sai Kumar
• Chalamala Yogendra Babu

This research paper presents an intelligent system that leverages machine learning techniques to provide personalized recommendations for daily calorie intake and suggest a combination of food items that collectively meet the recommended calorie requirements. The proposed system comprises two main components, a calorie prediction model and a food recommendation model. The calorie prediction model employs regression techniques to estimate the appropriate daily calorie intake for an individual based on their height, weight, age, gender, and other relevant factors. This estimation is grounded in the calculation of Body Mass Index (BMI) and Basal Metabolic Rate (BMR), ensuring accurate and personalized calorie recommendations. Once the recommended calorie intake is determined, the food recommendation model utilizes sequence prediction and combinatorial optimization approaches to suggest an optimal combination of food items that collectively match the target calorie requirement.

Diet and Calorie Recommendation, Linear Regression, Random Forest regressor, Gradient Boosting, Personalized health and wellness.

"Optimal Diet and Fitness Suggester using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k353-k359, March-2024, Available :http://www.jetir.org/papers/JETIR2403A45.pdf

"Optimal Diet and Fitness Suggester using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk353-k359, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A45.pdf