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Restaurant Recommendation System

Overview

The Restaurant Recommendation System is an end-to-end pipeline designed to provide optimized suggestions based on various parameters such as cost, ratings, cuisine, and location. This project serves to address issues faced by students and culinary entrepreneurs by factoring in multiple constraints and utilizing real-time data collection. When implemented in real life, it becomes multifaceted and beneficial.

Techniques Used

  • Exploratory Data Analysis (EDA): Analyzed and visualized data to gain insights into restaurant attributes and user preferences.
  • Collaborative Filtering: Technique used to make automatic predictions about the interests of a user by collecting preferences from many users.
  • Cosine Similarity: Measure used to determine how similar two items are based on their word vectors.
  • K-Means Clustering: Applied to group similar restaurants based on various features.
  • Feature Engineering: Process of selecting and transforming variables to create new features to improve model performance.

Recommendation Engine Types

  1. Reviews-Based Recommendation: Utilizes user reviews and sentiments to recommend restaurants based on similar preferences.
  2. Cost-Based Recommendation: Recommends restaurants based on cost preferences provided by the user.
  3. Location-Based Recommendation: Suggests restaurants based on user's location and proximity to various dining options.
  4. Collaborative Filtering Recommendation: Recommends restaurants based on user behavior and preferences learned from similar users.

Implementation

  • TF-IDF (Term Frequency-Inverse Document Frequency): Used to convert text reviews into numerical vectors for analysis and recommendation.
  • Real-Time Data Collection: Integration of real-time data collection mechanisms to ensure the system remains updated with the latest restaurant information and user preferences.

Benefits

  • Personalized Recommendations: Users receive tailored restaurant suggestions based on their unique preferences and constraints.
  • Enhanced User Experience: Provides a seamless and efficient way for users to discover new dining options.
  • Support for Culinary Entrepreneurs: Helps culinary entrepreneurs in optimizing their restaurant offerings based on user feedback and preferences.
  • Educational Grant: Received a $1000 educational grant and Google Kaggle LLC goodies for being selected as a Kaggle BIPOC Mentee from a global pool of applicants, acknowledging the contributions to the data science community.

Future Enhancements

  • Integration of more recommendation engine types to further enhance the diversity of suggestions.
  • Incorporation of deep learning models for more accurate and personalized recommendations.
  • Expansion to mobile applications and web platforms to reach a wider audience.

Conclusion

The Restaurant Recommendation System demonstrates the power of data-driven approaches in providing personalized and optimized suggestions. By leveraging techniques such as collaborative filtering, cosine similarity, and feature engineering, the system offers valuable insights and recommendations to users and culinary entrepreneurs alike. With continuous improvement and innovation, the system can play a significant role in enhancing the dining experience and supporting the culinary industry.