🍽️ Restaurant Recommendation System

Overview

The Restaurant Recommendation System is a content-based recommendation project that suggests top-rated restaurants based on a user's preferred cuisine. The system analyzes restaurant data and returns the best recommendations sorted by ratings.

Problem Statement

Choosing a restaurant from thousands of options can be difficult. This project simplifies the process by recommending highly rated restaurants that match the user's cuisine preferences.

Features

• Recommends restaurants based on cuisine preferences
• Displays top-rated restaurants
• Removes duplicate restaurant entries
• Handles missing values in the dataset
• Provides clean and sorted recommendations

Tech Stack

• Python
• Pandas
• NumPy
• Jupyter Notebook
• VS Code

Dataset Information

The dataset contains information such as:

• Restaurant Name
• Cuisine
• Dining Rating
• Delivery Rating
• Dining Votes
• Delivery Votes
• Place Name

Project Workflow

1. Load the dataset.
2. Preprocess the data.
3. Handle missing values and duplicates.
4. Accept cuisine input from the user.
5. Filter matching restaurants.
6. Sort restaurants by dining rating.
7. Display the top recommendations.

Sample Recommendation

Input: Cuisine: Beverages Output: Restaurant Name Rating Place Thali and More 4.7 C Scheme Toscano 4.7 Nungambakkam Urban Khichdi 4.6 Ravipuram Cafe 17 4.6 12th Square Building

Key Learnings

• Data preprocessing
• Data filtering and sorting
• Content-based recommendation systems
• Exploratory Data Analysis (EDA)
• Function development using Python and Pandas

Future Enhancements

• Add location-based recommendations
• Include price range filtering
• Build a web interface using Streamlit
• Deploy the project on the cloud

Author

Simran Singh