Recipe Search Engine

Objective:

To develop a search engine that efficiently retrieves recipes from a large dataset based on user queries involving recipe titles and ingredients. The goal is to enhance the cooking experience for users by providing a streamlined method for discovering new recipes.

Methodology:

• Dataset Acquisition: Sourced a comprehensive dataset from Kaggle, encompassing over 2 million recipes collected from various websites.
• Pre-processing: Processed the data to extract titles, ingredients, directions, and URLs. Applied tokenization, normalization, and stop word removal using NLTK to prepare the data for indexing.
• Indexing: Built an index to facilitate fast and accurate search operations.
• Query Processing: Developed functions to handle user queries, including extraction and preparation of search terms.
• Result Retrieval: Implemented a ranking algorithm to sort search results by relevance, ensuring users receive the most pertinent recipes.
• Website Design: Created a user-friendly web interface using Django, integrating the search engine with an intuitive design to enhance user experience.
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Contribution:

• Development: Designed and implemented the search engine using Django, ensuring a user-friendly interface and efficient search capabilities.
• Data Handling: Enhanced the dataset through meticulous pre-processing to improve search accuracy and relevance.
• Search Optimization: Developed a robust indexing and query handling system to deliver relevant search results swiftly.

Deliverables:

A fully functional Django-based website for recipe search, featuring:

• A search bar for querying recipes by title and ingredients.
• Results displayed with recipe titles, ingredients, directions, and URLs based on a ranking system to present results based on relevance to user queries.

Code

Github Repository: Recipe Search Engine