International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 05 | May 2025
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
NutriFit: Smart Nutrition and Personalized Meal Planning System Rahul Mahale, Manas Vyas, Suchipriya Malge 1Student, Dept of E&TC Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research ,
Wagholi, Pune, Maharashtra, India
2 Student, Dept of E&TC Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research ,
Wagholi, Pune, Maharashtra, India
3 Professor, Dept of E&TC Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research ,
Wagholi, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper presents NutriFit, an intelligent
leveraging machine learning techniques and data-driven insights. It utilizes a robust dataset of recipes that contain comprehensive nutritional information such as calories, fat content, carbohydrates, protein, and other essential nutrients.
nutrition and meal planning system designed to address the challenges individuals face in creating personalized and balanced meal plans. NutriFit integrates user- specific data such as age, weight, height, activity levels, and dietary preferences to generate personalized meal recommendations. By leveraging machine learning algorithms, the system provides precise recommendations tailored to users' health goals, such as weight loss, weight gain, or maintaining weight. This paper discusses the problem NutriFit addresses, the architecture of the solution, and future potential improvements
By using this data, NutriFit generates personalized meal plans that are tailored to the individual’s specific dietary needs and preferences. The platform’s core functionality revolves around a machine learning model, specifically a nearest neighbors’ algorithm, which enables it to recommend meals based on user inputs like age, weight, height, gender, and activity level. The system can also incorporate specific dietary preferences or restrictions, such as low-carb, vegan, or high-protein diets, ensuring that users receive recommendations that align with their health goals. The backend of NutriFit employs advanced algorithms that process user inputs and provide tailored meal recommendations. The machine learning model uses nutritional values to identify recipes that best match the user’s dietary requirements.
Key Words: Nutrition, Meal Planning, Personalized Recommendations, Machine Learning, Health Goals.
1.INTRODUCTION In today’s fast-paced world, individuals are increasingly becoming more health-conscious and aware of the significant role that nutrition plays in overall well-being. Poor dietary habits have been linked to a wide range of health issues such as obesity, cardiovascular diseases, diabetes, and other chronic conditions. Despite the plethora of dietary guidelines available, many individuals struggle to maintain a balanced diet that aligns with their specific health needs, lifestyle, and personal preferences. Generic meal plans, while helpful, often fall short in offering the personalization necessary to meet individual goals, preferences, and nutritional requirements.
In the following sections, this paper will delve into the problem NutriFit aims to solve, the methodology and system architecture used to develop the platform, and the results achieved through this innovative approach to personalized meal planning.
2. LITERATUR REVIEW Managing nutrition and maintaining a balanced diet can be overwhelming and time-consuming, especially when considering individual dietary needs and preferences. Traditional meal planning methods often lack the personalization required to meet specific Health goals, leading to inefficiencies and dissatisfaction. With the rise of technology particularly Artificial Intelligence (AI), personalized nutrition has become more accessible and precise This paper reviews the integration of AI into meal planning through NutriFit, highlighting its impact customizing nutrition, addressing common dietary challenges, and the potential outcomes of AI-driven recommendations.
Personalization is key when it comes to effective nutrition management. Every individual has unique dietary needs based on factors like age, gender, activity level, medical conditions, and body composition. The need for personalized meal planning has grown significantly with increasing awareness about the diversity of nutritional requirements. For instance, an active male athlete would have vastly different dietary needs compared to a sedentary office worker or someone trying to manage diabetes. Therefore, a one-size-fits-all approach is often ineffective, leading to frustration and poor adherence to dietary plans. This is where NutriFit: Smart Nutrition & Personalized Meal Planning System comes into play. NutriFit offers an innovative solution to the challenges of personalized nutrition by
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