Global Food Nutrition Dataset

Data Format:
The dataset is provided as a CSV file, facilitating easy import into data analysis tools.

Column Descriptions:

1. Food: Name of the food item.
2. Caloric Value: Energy in kcal per 100 grams.
3. Fat (g): Total fats per 100 grams.
4. Saturated Fats (g): Saturated fats per 100 grams.
5. Monounsaturated Fats (g): Monounsaturated fats per 100 grams.
6. Polyunsaturated Fats (g): Polyunsaturated fats per 100 grams.
7. Carbohydrates (g): Total carbohydrates per 100 grams.
8. Sugars (g): Sugars per 100 grams.
9. Protein (g): Protein per 100 grams.
10. Dietary Fiber (g): Fiber content per 100 grams.
11. Cholesterol (mg): Cholesterol per 100 grams.
12. Sodium (mg): Sodium per 100 grams.
13. Water (g): Water content per 100 grams.
14. Vitamin A (µg): Vitamin A per 100 grams.
15. Vitamin B1 (Thiamine) (mg): Thiamine per 100 grams.
16. Vitamin B11 (Folic Acid) (µg): Folic Acid per 100 grams.
17. Vitamin B12 (µg): Vitamin B12 per 100 grams.
18. Vitamin B2 (Riboflavin) (mg): Riboflavin per 100 grams.
19. Vitamin B3 (Niacin) (mg): Niacin per 100 grams.
20. Vitamin B5 (Pantothenic Acid) (mg): Pantothenic Acid per 100 grams.
21. Vitamin B6 (mg): Vitamin B6 per 100 grams.
22. Vitamin C (mg): Vitamin C per 100 grams.
23. Vitamin D (µg): Vitamin D per 100 grams.
24. Vitamin E (mg): Vitamin E per 100 grams.
25. Vitamin K (µg): Vitamin K per 100 grams.
26. Calcium (mg): Calcium per 100 grams.
27. Copper (mg): Copper per 100 grams.
28. Iron (mg): Iron per 100 grams.
29. Magnesium (mg): Magnesium per 100 grams.
30. Manganese (mg): Manganese per 100 grams.
31. Phosphorus (mg): Phosphorus per 100 grams.
32. Potassium (mg): Potassium per 100 grams.
33. Selenium (µg): Selenium per 100 grams.
34. Zinc (mg): Zinc per 100 grams.
35. Nutrition Density: Nutrient richness per calorie.

Use Cases:

1. Nutritional Pattern Analysis: Identify patterns in food consumption and nutritional impacts using machine learning.
2. Diet Recommendation Systems: Suggest dietary adjustments integrating broader dietary data.
3. Predictive Health Modeling: Forecast health impacts based on food consumption patterns.
4. Ingredient Optimization: Formulate new recipes predicting nutritional content based on ingredients.
5. Consumer Behavior Analysis: Predict preferences based on nutritional information and demographics.
6. Quality Control: Ensure quality and consistency in food manufacturing.
7. Text Analysis for Marketing: Extract consumer sentiments related to nutritional aspects from reviews.
8. Supply Chain Optimization: Optimize supply chains by predicting demand based on health trends.
9. Educational Tools: Develop interactive applications to teach nutrition.
10. Integration with Fitness Apps: Provide insights into dietary goals by integrating consumption data into fitness apps.