Feature Added: Diet Recommendation System with Machine Learning for Calorie and Dietary Type Predictions #752
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Related Issues or bug
Fixes: #746
Proposed Changes
🍽️ Machine Learning Models: Integrated Random Forest Regressor and Classifier to accurately predict calorie needs and dietary types based on user inputs, enhancing the dietary recommendation process.
📊 Synthetic Dataset: Created a synthetic dataset that simulates user profiles with features like Age, Gender, Weight, and Activity Level, ensuring effective model training and better prediction accuracy.
🔍 Feature Engineering: Implemented one-hot encoding for categorical variables to improve the models' ability to learn from diverse input data.
🥗 Dynamic Diet Recommendations: Developed a system that provides personalized meal plans based on predicted dietary types, including options for balanced, high-protein, and low-carb diets.
🛠️ Tech Stack: Utilized Python along with essential libraries such as Pandas, NumPy, and Scikit-learn for data handling and machine learning functionalities.
🔮 Future Updates: Plans to integrate real-world datasets for improved accuracy, enhance user feedback mechanisms to refine recommendations, include a wider variety of dietary preferences (e.g., vegetarian, gluten-free), and develop a user-friendly interface for easier interaction with the system.
Additional Info
Screenshots
Updated
README.md :
LICENSE.md: