https://github.com/burhanahmed1/recipe-recommendor-using-pyspark
A smart recipe recommendation system that suggests recipes based on ingredient similarities. This project is done in PySpark
https://github.com/burhanahmed1/recipe-recommendor-using-pyspark
data-analysis data-science datawrangling education learning-python machine-learning machine-learning-algorithms nltk-python numpy pandas pyspark python python-project reccomendersystem recommendation-system
Last synced: 8 months ago
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A smart recipe recommendation system that suggests recipes based on ingredient similarities. This project is done in PySpark
- Host: GitHub
- URL: https://github.com/burhanahmed1/recipe-recommendor-using-pyspark
- Owner: burhanahmed1
- License: mit
- Created: 2024-07-12T14:53:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-12T15:13:54.000Z (over 1 year ago)
- Last Synced: 2025-01-20T16:53:42.537Z (10 months ago)
- Topics: data-analysis, data-science, datawrangling, education, learning-python, machine-learning, machine-learning-algorithms, nltk-python, numpy, pandas, pyspark, python, python-project, reccomendersystem, recommendation-system
- Language: Jupyter Notebook
- Homepage:
- Size: 701 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Recipe Recommendation System
This repository contains a recipe recommendation system that utilizes machine learning techniques to suggest recipes based on ingredient similarities. The system is built using a Jupyter notebook and a dataset of recipes.
## Repository Structure
- **Recommendor.ipynb**: Jupyter notebook containing the code for the recipe recommendation system. This notebook includes:
- Data loading and preprocessing.
- Feature extraction using ingredient lists.
- Calculation of cosine similarities between recipes to generate recommendations.
- **recipes_combined_dataset.csv**: Dataset containing 9,999 recipes with their ingredients. The columns in this dataset include:
- `recipeNames`: Names of the recipes.
- `ingredients`: Ingredients for each recipe.
- `all_ingredients`: A consolidated list of all ingredients for each recipe.
- Several unnamed columns with additional ingredient information.
## Installation
To run the Jupyter notebook and explore the recommendation system, you will need the following dependencies:
- Python
- Jupyter Notebook
- pandas
- numpy
- scikit-learn
- nltk
## Usage
1. Clone the repository:
```bash
git clone https://github.com/burhanahmed1/Recipe-Recommendor-using-PySpark.git
cd recipe-recommendation-system
```
2. Open the Jupyter notebook:
```bash
jupyter notebook Recommendor.ipynb
```
3. Run the cells in the notebook to load the dataset, preprocess the data, and generate recipe recommendations.
## Dataset
The dataset used in this project is a collection of recipes with their ingredients. It contains 9,999 entries with the following columns:
+ `recipeNames:` The name of the recipe.
+ `ingredients:` A list of ingredients for the recipe.
+ `all_ingredients:` A concatenated string of all ingredients for easier processing.
Several unnamed columns with additional ingredient information.
## Project Overview
The main goal of this project is to develop a recommendation system that can suggest recipes based on ingredient similarities. By calculating cosine similarities between recipes, the system can identify and recommend recipes with similar ingredient profiles.
## License
This project is licensed under the MIT License.
## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue if you have any suggestions or improvements.
## Acknowledgments
Special thanks to the dataset providers and the open-source community for their valuable resources and tools.