| --- |
| datasets: |
| - processed_recipes |
| language: |
| - en |
| tags: |
| - recipes |
| - cooking |
| - food |
| - difficulty-prediction |
| - nlp |
| license: other |
| task_categories: |
| - text-classification |
| - text-retrieval |
| - other |
| pretty_name: Processed Recipes Dataset |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
|
|
| # Processed Recipes Dataset |
|
|
| ## Dataset Description |
|
|
| - **Source**: Scraped from [Food Network](https://www.foodnetwork.com/) |
| - **Processed by**: Cleaned and parsed for use in recipe difficulty prediction |
| - **Format**: Parquet (`.parquet`) |
| - **Size**: 1 file (`processed_recipes.parquet`) |
|
|
| This dataset contains structured recipe information scraped from Food Network. |
| It was originally collected and processed to train a model for **recipe difficulty prediction**, but it can also be used for other tasks such as: |
|
|
| - Ingredient analysis |
| - Cooking time prediction |
| - Recipe recommendation systems |
| - Natural language processing on cooking instructions |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| ### Columns |
|
|
| - **title** *(string)*: Recipe title |
| - **level** *(string)*: Difficulty level (e.g., Easy, Intermediate, Advanced) |
| - **clean_ingredients** *(string)*: Comma-separated list of cleaned ingredients |
| - **ingredients_count** *(int)*: Number of ingredients in the recipe |
| - **directions_count** *(int)*: Number of steps in the recipe directions |
| - **unique_techniques** *(string)*: Comma-separated list of unique cooking techniques detected |
| - **equipment** *(string)*: Comma-separated list of equipment mentioned |
| - **has_precise_timing** *(bool)*: Whether the recipe specifies precise timing (True/False) |
| - **total_minutes** *(int)*: Total time in minutes (0 if not specified) |
| - **active_minutes** *(int)*: Active cooking time in minutes (0 if not specified) |
| - **prep_minutes** *(int)*: Preparation time in minutes |
| - **cook_minutes** *(int)*: Cooking time in minutes |
|
|
| --- |
|
|
| ### Example Row |
|
|
| ```text |
| title: 100-Calorie Ham and Cheese Individual Frittatas |
| level: Easy |
| clean_ingredients: nonstick cooking spray, olive oil, ham steak, sliced mushrooms, ... |
| ingredients_count: 9 |
| directions_count: 5 |
| unique_techniques: preheat, reduce, beat, come, insert, scoop, set, heat |
| equipment: center, large bowl, large nonstick skillet medium |
| has_precise_timing: True |
| total_minutes: 0 |
| active_minutes: 0 |
| prep_minutes: 15 |
| cook_minutes: 45 |
| ``` |
|
|
| --- |
|
|
| ## Intended Uses |
|
|
| - **Primary use**: Training models for recipe difficulty prediction |
| - **Other possible uses**: |
| - Ingredient-based clustering |
| - Cooking time estimation |
| - Recipe recommendation |
| - NLP tasks on cooking instructions |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Recipes are scraped from Food Network and may not represent all cuisines or cooking styles. |
| - Some time values (`total_minutes`, `active_minutes`) may be missing or set to `0` if not provided. |
| - Ingredient and equipment parsing may not be perfect. |
|
|
| --- |
|
|
| ## License |
|
|
| ⚠️ **Note**: The dataset is derived from Food Network content. Please ensure compliance with their terms of service before using this dataset for commercial purposes. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this dataset in your research or project, please cite it as: |
|
|
| @dataset{processed_recipes, |
| title = {Processed Recipes Dataset}, |
| author = {Saadman Rahman}, |
| year = {2025}, |
| url = {https://huggingface.co/datasets/SDMN2001/recipe_difficulties} |
| } |
|
|
