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Diet Recommendation for Pregnant Women based on several factors [machine learning] [data analytics] #766
Is your feature request related to a problem? Please describe.
Develop a pregnancy diet recommendation system that suggests suitable food items for pregnant women based on their personal characteristics, dietary restrictions, nutritional needs, and pregnancy stage. The system should handle multi-label classification, as pregnant women may have multiple dietary preferences and nutritional requirements during different stages of pregnancy.
Describe the solution you'd like
The solution involves collecting a comprehensive dataset with pregnant women's characteristics, pregnancy stage, dietary restrictions, nutritional needs, and food items with nutritional information. The data will be preprocessed to handle missing values, encode categorical variables, and scale numerical features. Multiple multi-label classification models, including Binary Relevance, Classifier Chains, Label Powerset, and Random Forest, will be evaluated and trained on the dataset. Hyperparameter tuning will be performed to enhance model performance.
Describe alternatives you've considered
No response
Additional context
I am a GSSOC contributor
This project is already been worked on so will be submitted within a week of assigning
Code of Conduct
[X] I agree to follow this project's Code of Conduct
Is your feature request related to a problem? Please describe.
Develop a pregnancy diet recommendation system that suggests suitable food items for pregnant women based on their personal characteristics, dietary restrictions, nutritional needs, and pregnancy stage. The system should handle multi-label classification, as pregnant women may have multiple dietary preferences and nutritional requirements during different stages of pregnancy.
Describe the solution you'd like
The solution involves collecting a comprehensive dataset with pregnant women's characteristics, pregnancy stage, dietary restrictions, nutritional needs, and food items with nutritional information. The data will be preprocessed to handle missing values, encode categorical variables, and scale numerical features. Multiple multi-label classification models, including Binary Relevance, Classifier Chains, Label Powerset, and Random Forest, will be evaluated and trained on the dataset. Hyperparameter tuning will be performed to enhance model performance.
Describe alternatives you've considered
No response
Additional context
I am a GSSOC contributor This project is already been worked on so will be submitted within a week of assigning
Code of Conduct