Restaurant Recommender

Overview

The Restaurant Recommender is a web application built using Flask that allows users to input their recently visited restaurants and receive recommendations for similar dining options. The application utilizes a dataset of restaurants from Google Place API, including their ratings and features, to provide personalized suggestions.

Features

• User-friendly interface for inputting restaurant preferences.
• Recommendations based on user input using a similarity algorithm.
• Integration with a dataset of restaurants, including ratings and various features.
• Responsive design using Bootstrap for a better user experience.

Technologies Used

• Flask: A lightweight WSGI web application framework for Python.
• Pandas: A data manipulation and analysis library for Python.
• Scikit-learn: A machine learning library for Python, used for calculating cosine similarity.
• FuzzyWuzzy: A library for string matching, used to find the closest restaurant names.
• Bootstrap: A front-end framework for developing responsive web applications.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/restaurant-recommender.git
   cd restaurant-recommender

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

5. Ensure you have the dataset c.csv in the notbooks directory.(fixing it now so that it will be automatically calculateed)

Usage

1. Run the Flask application:

   python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000/.

3. Input your recently visited restaurants and other details, then click "Get Restaurants" to receive recommendations.

Dataset

The application uses a CSV file (c.csv) containing restaurant data, including:

• Name
• Rating
• Price Level
• User Ratings Total
• Various features (e.g., bakery, bar, cafe, etc.) This dataset has been derived and cleaned from data originally calculated using latitude and longitude coordinates obtained from the Google Places API.

Future Improvements

• Location-based Recommendations:
  Currently, the application works only for the Charlottesville area. In the future, we plan to enable users to select their location (city or coordinates) for more personalized recommendations, making it applicable to a wider range of users.

• Enhanced Results Page:
  The recommendation page will be upgraded to include additional details like restaurant images, detailed reviews, and updated ratings. This will provide users with a more informative and visually appealing experience.

• Interactive Map Integration:
  To further enhance user experience, we aim to integrate a map feature that allows users to see the locations of recommended restaurants, making it easier for them to plan their visits.

• Better Search Functionality:
  The search functionality will be refined to support fuzzy matching for restaurant names and features, allowing for more accurate results even if the user misspells or partially enters restaurant names.

Acknowledgments

• Flask Documentation
• Pandas Documentation
• Scikit-learn Documentation
• FuzzyWuzzy Documentation
• Bootstrap Documentation