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:

git clone https://github.com/burhanahmed1/Recipe-Recommendor-using-PySpark.git
cd recipe-recommendation-system

2. Open the Jupyter notebook:

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.