Nandhini3737/standardized-food-nutrition-dataset

Daily Food & Nutrition Dataset

A comprehensive dataset containing nutritional information for 587 common food items with detailed macronutrient breakdowns.

Dataset Overview

This dataset provides detailed nutritional composition for a wide variety of foods, including calories, protein, carbohydrates, fat, fiber, sugars, sodium, and cholesterol content. Each food item is categorized and associated with meal types for easy filtering and analysis.

Dataset Statistics:

• —📊 Total Food Items: 587 unique foods
• —🏷️ Categories: 46 food categories
• —🍽️ Meal Types: 5 (Breakfast, Lunch, Dinner, Snack, Side)
• —📈 Features: 12 nutritional & categorical attributes
• —✅ Data Quality: No missing values, validated nutrition logic

Features/Columns

Data Summary

Nutrition Statistics (Averages):

• —Average Calories: 145.2 kcal
• —Average Protein: 6.3g
• —Average Carbohydrates: 15.0g
• —Average Fat: 6.5g
• —Average Fiber: 2.4g

Top Food Categories:

• —Protein/Meat (25+ items)
• —Grain (20+ items)
• —Vegetable (18+ items)
• —Dairy (15+ items)
• —Fruit (14+ items)
• —Meal/Processed (12+ items)
• —And 40+ more categories

Data Cleaning & Preprocessing

✅ Quality Assurance:

• —Removed 54 exact duplicate entries
• —Removed 58 duplicate food items (keeping first occurrence)
• —Validated all numeric values (no negative values)
• —Verified nutrition logic (Sugars ≤ Carbohydrates, Fiber ≤ Carbohydrates)
• —Confirmed no missing values

Original source: ~650 rows → Cleaned: 587 unique foods

Use Cases

This dataset is ideal for:

1. 1.Nutrition Analysis
2. 2.Analyze nutritional content across food categories
3. 3.Calculate daily caloric and macro intake
4. 4.Study sodium and cholesterol patterns

1. 1.Meal Planning
2. 2.Create balanced meal plans
3. 3.Filter foods by nutritional criteria
4. 4.Track daily nutrient goals

1. 1.Machine Learning
2. 2.Food classification by nutritional profile
3. 3.Nutritional recommendation systems
4. 4.Dietary pattern analysis
5. 5.Calorie prediction models

1. 1.Health & Fitness Applications
2. 2.Nutrition tracking apps
3. 3.Fitness and diet planning software
4. 4.Personalized nutrition recommendations

1. 1.Data Analysis & Visualization
2. 2.Nutrition data exploration
3. 3.Statistical analysis of food groups
4. 4.Comparative nutritional studies

Example Usage

Load the Dataset

import pandas as pd

# Load the cleaned dataset
df = pd.read_csv('daily_food_nutrition_UNIQUE_FOODS.csv')

print(f"Dataset shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")

Filter by Meal Type

# Get all breakfast items
breakfast_foods = df[df['Meal_Type'] == 'Breakfast']
print(f"Breakfast foods: {len(breakfast_foods)}")

# High-protein foods (>20g)
high_protein = df[df['Protein (g)'] > 20]
print(f"High-protein foods: {high_protein[['Food_Item', 'Protein (g)']].head()}")

Analyze by Category

# Average nutrition by category
category_stats = df.groupby('Category')[['Calories (kcal)', 'Protein (g)', 'Fat (g)']].mean()
print(category_stats.head(10))

# Foods in protein category
proteins = df[df['Category'].str.contains('Protein', na=False)]
print(f"Protein foods: {len(proteins)}")

Nutritional Filtering

# Low-calorie foods (<100 kcal)
low_cal = df[df['Calories (kcal)'] < 100]
print(f"Low-calorie foods: {low_cal[['Food_Item', 'Calories (kcal)']].head(10)}")

# Low-sodium foods (<200mg)
low_sodium = df[df['Sodium (mg)'] < 200]
print(f"Low-sodium foods: {len(low_sodium)}")

Data Format

CSV Structure:

• —Delimiter: Comma (,)
• —Header: Yes (first row contains column names)
• —Encoding: UTF-8
• —No missing values

Sample rows:

Scrambled Eggs (2 large),Protein/Dairy,180,12.0,2.0,14.0,0.0,1.0,180,370,Breakfast,250
Whole Wheat Toast (1 slice),Grain,80,4.0,14.0,1.0,2.0,2.0,140,0,Breakfast,0
Salmon (4oz grilled),Protein/Fish,230,25.0,0.0,14.0,0.0,0.0,60,60,Dinner,250

Original Source

This dataset is based on the publicly available Daily Food and Nutrition Dataset from Kaggle:

• —📍 Source: https://www.kaggle.com/datasets/adilshamim8/daily-food-and-nutrition-dataset
• —Creator: Adil Shamim

License

This dataset is provided under the CC-BY-4.0 License (Creative Commons Attribution 4.0 International). You are free to use, modify, and distribute this dataset as long as you provide attribution to the original creator.

Citation

If you use this dataset, please cite the original source:

@dataset{shamim2024daily,
  title={Daily Food and Nutrition Dataset},
  author={Shamim, Adil},
  url={https://www.kaggle.com/datasets/adilshamim8/daily-food-and-nutrition-dataset},
  year={2024},
  publisher={Kaggle}
}

Limitations & Considerations

1. 1.Portion Sizes: Values based on standard portions; actual nutrition may vary
2. 2.Preparation Method: Data is for raw/standard preparation; cooking affects nutrition
3. 3.Brand Variation: Different brands may have slightly different nutrition profiles
4. 4.Geographic Differences: Nutritional content may vary by region and source

Contributing

Found an issue or want to improve the dataset?

• —Report data quality issues
• —Suggest new food items
• —Request additional nutritional metrics

Support & Questions

For questions about this dataset:

• —Check the Kaggle original dataset page
• —Review the data preview above
• —Analyze the CSV structure provided

Dataset Version: 1.0 (Cleaned) Last Updated: July 2026 Prepared for: Hugging Face Hub