The inspiration for this project comes from the growing awareness of the importance of healthy eating habits in maintaining good health and preventing chronic diseases. With the rise of lifestyle-related diseases such as obesity, diabetes, and heart disease, there is a need for personalized dietary recommendations to help people make healthier food choices and improve their overall health outcomes. We were also inspired by the increasing availability of data on nutrition and health outcomes.
In this project, we aim to create an intelligent system that can recommend personalized diets for individuals based on specific parameters like age,type of diet(vegan/non-vegan),weight and height. The system will use machine learning algorithms to analyze these parameters to generate a results if they are healty or not and diet which they have to maintain. The system will also take into account factors such as calorie intake, macronutrient ratios, and specific nutrient requirements to ensure that the recommended diet is both healthy and sustainable. We will train our system using a large dataset of nutrition information, including food composition data and dietary guidelines.The ultimate goal of this project is to provide individuals with a convenient and effective way to manage their diet and improve their overall health and well-being.
In this project, we have used two popular machine learning algorithms, KMeans and Random Forest, to build a diet recommendation system. KMeans algorithm has been used to cluster individuals based on their dietary preferences, while Random Forest has been used to predict the most suitable diet plan for each cluster. The system takes input from the user regarding their age, gender, weight, height, physical activity level, and dietary restrictions. Based on this information, the system uses KMeans to cluster the user into a specific group based on their dietary preferences. Then, Random Forest algorithm is used to suggest a personalized diet plan for each cluster, taking into account the individual's nutritional requirements and dietary restrictions. Overall, this diet recommendation system provides a practical and data-driven approach to help individuals make informed decisions about their diets. By leveraging the power of machine learning algorithms, we can provide personalized recommendations that are tailored to each individual's unique needs and preferences.
✅Train the model using Intel oneDAL to get better results and faster computation(Intel oneAPI Data Analytics Library (oneDAL))
✅Personalized dietary recommendations are important: Traditional dietary advice may not be effective for everyone. Personalized dietary recommendations can be more effective in improving health outcomes and preventing chronic diseases.
✅Supervised learning approach: We used a supervised learning approach to train our machine learning model using a dataset of nutrition information and health outcomes. The dataset was preprocessed and cleaned, and relevant features were extracted using feature engineering techniques.
✅Machine learning algorithms: We used various machine learning algorithms such as decision trees, random forests, or neural networks to train our model.
✅Dataset: We used a dataset of nutrition information, including food composition data, dietary guidelines, and medical research on the effects of different diets on health outcomes.
✅Evaluation metrics: We used metrics such as accuracy, precision, recall, and F1-score to evaluate the performance of our model.
✅Ethical considerations: We focused on incorporating ethical considerations, such as data privacy and bias, to ensure that our recommendations are fair and unbiased.
These are only a few illustrations of the expertise and abilities we most likely acquired while working on this project.