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Taste Match is an end-to-end project that utilizes advanced natural language processing techniques. It expertly recommends restaurants by analyzing your favorite dining spots, ensuring personalized and accurate suggestions

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TasteMatch: Discover Your Next Favorite Restaurant

Live Demo

Experience TasteMatch in action! Visit the TasteMatch Streamlit App to see how our innovative solution transforms the way you discover and enjoy new restaurants.

Sample.output.mov

Project Overview

TasteMatch is a cutting-edge application designed to revolutionize the way you explore and discover new restaurants. Leveraging advanced Natural Language Processing (NLP) and Word2Vec algorithms, TasteMatch analyzes extensive restaurant reviews to offer you personalized dining recommendations. Say goodbye to the uncertainty of trying new places and hello to a world of culinary adventures tailored to your taste!

Technology Used

  • Natural Language Processing (NLP): Employs NLP techniques to analyze restaurant reviews.
  • Word2Vec Models: Converts review text into meaningful vector representations.
  • Python The backbone programming language for developing the NLP models and backend logic.
  • Streamlit: Creates an engaging, interactive web application interface.
  • Pandas & NumPy: Used for data handling and numerical calculations.
  • Gensim & NLTK: Integral for model training and text processing in the NLP pipeline.
  • Docker: Facilitates easy deployment and scaling of the application.

Objectives

  • Customized Restaurant Recommendations: Offer users tailored dining suggestions.
  • Utilizing User Review Data: Leverage NLP for a thorough analysis of user reviews for accurate recommendations.
  • User Experience Enhancement: Develop an easy-to-use and engaging app interface for seamless restaurant discovery.

Learning Outcomes

  • Applied NLP: Gained real-world experience in the application of NLP and Word2Vec models.
  • Data Processing Expertise: Enhanced skills in cleaning and preparing data for NLP.
  • Streamlit and Docker Proficiency: Acquired experience in web app development and application containerization.

Getting Started

Prerequisites

  • Python 3.x
  • Pandas, NumPy, Gensim, NLTK
  • Streamlit for the web app interface

Installation

Clone the repository to your local machine:

git clone [https://github.com/yourusername/tastematch.git](https://github.com/sathishprasad/Restauarant-Recommendation.git)

Install the required packages:

pip install -r requirements.txt

Usage

  1. Run the Streamlit app:
streamlit run app.py
  1. Select your favorite restaurant from the dropdown menu.
  2. Explore the list of recommended restaurants based on similar user reviews.

Happy Dining Exploration with TasteMatch!

About

Taste Match is an end-to-end project that utilizes advanced natural language processing techniques. It expertly recommends restaurants by analyzing your favorite dining spots, ensuring personalized and accurate suggestions

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