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https://github.com/ghufranbarcha/dish-recommender-website

This project is a web application that recommends recipes based on the ingredients provided by the user. It utilizes machine learning techniques to suggest recipes that match the input ingredients, providing users with personalized cooking ideas.
https://github.com/ghufranbarcha/dish-recommender-website

cosine-similarity gensim html-css-javascript nlp-machine-learning python recommender-system

Last synced: 26 days ago
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This project is a web application that recommends recipes based on the ingredients provided by the user. It utilizes machine learning techniques to suggest recipes that match the input ingredients, providing users with personalized cooking ideas.

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README

        

# Recipe Recommender Website
## Overview
This project is a web application that recommends recipes based on the ingredients provided by the user. It utilizes machine learning techniques to suggest recipes that match the input ingredients, providing users with personalized cooking ideas.


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## Features
- Input Ingredients: Users can input a list of ingredients they have.
- Recipe Recommendations: Based on the provided ingredients, the website recommends recipes from a dataset.
- Recipe Details: Users can view details such as recipe names, ingredients, total time required, and images for each recommended recipe.
- Responsive Design: The website is designed to be responsive and accessible across various devices.
## Technologies Used
- Flask: Python web framework used for server-side development.
- HTML/CSS: Frontend development for user interface and styling.
- JavaScript: Enhancing interactivity and handling frontend logic.
- Gensim: Library used for natural language processing and word embeddings.
- Pandas: Data manipulation library for handling recipe dataset.
- scikit-learn: Library used for TF-IDF vectorization and cosine similarity calculation.
## Installation
- Clone the repository:
`git clone https://github.com/GhufranBarcha/Dish-Recommender-website.git`
- Navigate to the project directory:
`cd recipe-recommender`
- Install dependencies:
`pip install -r requirements.txt`
- Run the Flask application:
`python app.py`