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: 6 days 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 (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-12T15:13:54.000Z (4 months ago)
• Last Synced: 2024-07-12T18:37:03.962Z (4 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: 0
• 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.