Description

• Implement machine learning models for accurate calorie estimation and dietary type recommendations.

Fixes #1612

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.

Screenshots

Updated

Screenshot 2024年11月04日 024248

README.md :

image

LICENSE.md:

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Type of change

• Added a new machine learning frameworks, libraries or software.
• Documentation update

Checklist:

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings