Project Goal: Estimate obesity levels using multiclass classification machine learning algorithms such as Decision Tree, Knn, Logistic Regression and multiclass methods like "One-vs-One" (OvO) ve "One-vs-Rest" (OvR).
Input Data: Daily Living Habits: Eating patterns, physical activity levels, smoking, family history of overweight. Demographic Features: Height, weight, age.
Calculated Variables:
- BMI (Body Mass Index)
- BMR (Basal Metabolic Rate)
- Ideal Weight
- Daily Calorie Intake
Recommendations: Meal Recommendations: Based on DCI, diet type and cuisine preferences. Alternative Meals: Provided using Content-Based Filtering with Nutritional Similarity algorithm.
INFORMATION ABOUT WEIGHT WISE DATASETS
The datasets used are ObesityDataSet_raw_and_data_sinthetic.csv and All_Diets.csv from Kaggle.
Obesity Dataset:
The dataset was obtained from Kaggle and was originally collected by the research team of Dr. Paulo Cortez and Prof. Ana Almeida from the University of Minho, Portugal. Citation : P. Cortez and A. Almeida. "Predicting Obesity Type Based on Genetic and LifeStyle Factors." In Proceedings of the 5th International Workshop on Knowledge Discovery in Databases. Porto, Portugal, 2005.
All Diets Dataset:
The file All_Diets.csv contains recipes from different diets and cuisines, all with the aim of providing healthy and nutritious meal options. Collaborator is The Devastator (Owner).
Columns of “ObesityDataSet_raw_and_data_sinthetic.csv” Dataset
- Gender: Male or Female
- Age: Age of the individual in years
- Height: Height of the individual in meters
- Weight: Weight of the individual in kilograms
- Family_history_with_overweight: Has the individual a family history of overweight or obesity? Yes or No
- FAVC: Does the individual consume high caloric food frequently? Yes or No
- FCVC: How often does the individual consume vegetables? 1 (never) to 3 (always)
- NCP: How many main meals does the individual have daily? 1 to 3
- CAEC: Does the individual monitor the calories they eat? Sometimes, Frequently, Always or No
- SMOKE: Does the individual smoke? Yes or No
- CH2O: How much water does the individual drink daily? 1 (less than a liter), 2 (1 to 2 liters), or 3 (more than 2 liters)
- SCC: Does the individual monitor the calories they burn? Yes or No
- FAF: How often does the individual engage in physical activity? 0 (never) to 3 (always)
- TUE: How many hours does the individual spend sitting on a typical day? 0 (less than an hour), 1 (1 to 2 hours), or 2 (more than 2 hours)
- CALC: Does the individual take extra calories? Always, Sometimes or No
- MTRANS: Transportation method used by the individual: Automobile, Bike, Motorbike, Public Transportation or Walking
- NObeyesdad: Obesity level of the individual, classified into: Insufficient_Weight, Normal_Weight, Overweight_Level_I, Overweight_Level_II, Obesity_Type_I, Obesity_Type_II or Obesity_Type_III
Columns of “All_Diets.csv” Dataset
- Diet_type: The type of diet the recipe is for.
- Recipe_name: The name of the recipe.
- Cuisine_type: The cuisine the recipe is from.
- Protein(g): The amount of protein in grams.
- Carbs(g): The amount of carbs in grams.
- Fat(g): The amount of fat in grams.
- Extraction_day: The day the recipe was extracted.