A Complete Solution for Predicting Restaurant Ratings Using Machine Learning: From Data Preprocessing to 96.2% Accuracy

This article introduces a complete restaurant rating prediction project covering the entire workflow from data preprocessing, feature engineering, model selection to tuning. Finally, using a Random Forest Regressor, it achieved an R² score of 96.2% on a real dataset. The project is maintained by Abhinav, a computer science student, and published on GitHub (Project name: Predict-Restaurant-Ratings, Link: https://github.com/Abhinav8640/Predict-Restaurant-Ratings), aiming to provide data-driven support for decision-making in the catering industry.

• Original Author/Maintainer: Abhinav (Computer Science student, AI and machine learning enthusiast)
• Source Platform: GitHub
• Original Project Name: Predict-Restaurant-Ratings
• Original Link: https://github.com/Abhinav8640/Predict-Restaurant-Ratings
• Release Date: June 1, 2026

In the catering industry, accurately predicting restaurant ratings is of great significance for operators to optimize services and investors to evaluate value. Traditional prediction relies on experience, while machine learning provides a new data-driven approach. The goal of this project is to build a regression model to predict the comprehensive rating of restaurants by analyzing features such as cuisine type, city, pricing, and number of votes, providing support for industry decision-making.

The project dataset contains multi-dimensional information:

Basic Information Dimension: Cuisine type, city, currency used, average cost for two, price range User Feedback Dimension: Number of votes (reflecting popularity), comprehensive rating (target variable) Service Feature Dimension: Whether reservation is supported, takeaway delivery, current delivery status Geographic Dimension: Latitude and longitude coordinates

These features cover key aspects of restaurant operations and provide rich input for model training.

Feature Engineering

Extract the main cuisine as a representative feature to simplify the complexity of multi-labels.

Data Cleaning

Remove irrelevant fields: restaurant ID/name, detailed address/area, rating color/text description (risk of data leakage), menu switch status.

Encoding Processing

• One-hot Encoding: City, currency, cuisine type (unordered categories)
• Label Encoding: Reservation support, takeaway, delivery status (binary features)

Feature Scaling

Apply standardization scaling to numerical features (average cost for two, number of votes, latitude and longitude) to eliminate the influence of dimensionality.

Algorithm Comparison

Choose Random Forest Regressor because it can capture non-linear relationships and feature interactions, and integrate multiple trees to reduce overfitting risk, which is better than linear regression.

Training Results

Evaluation Metric	Score
Mean Squared Error (MSE)	0.0864
R² Coefficient of Determination	0.9620

Result Interpretation: The R² score of 0.962 explains about 96.2% of the variance in ratings, indicating high prediction accuracy; the low MSE indicates small bias, which is significantly better than the linear regression benchmark.

The project uses the Python ecosystem toolchain:

• Data Processing: Pandas (structured data), NumPy (numerical computation)
• Machine Learning: Scikit-learn (preprocessing, model training, evaluation)
• Development Environment: Python 3.x

The code structure is clear, forming a complete pipeline from data loading to result evaluation, which is easy to reproduce and extend.

Application Scenarios

• New store location evaluation: Predict potential ratings
• Operation optimization: Identify key influencing factors
• Investment decision-making: Provide rating expectations

Improvement Directions

Model Level: GridSearchCV tuning, feature importance visualization, cross-validation Function Level: Support multi-cuisine classification, deploy web applications with Flask/Streamlit, build real-time API services

These directions can further enhance the project's practicality and performance.

This project demonstrates the full machine learning workflow: from business understanding to model deployment. Its successes lie in:

1. Systematic preprocessing (differentiated feature processing)
2. Reasonable model selection (Random Forest adapts to complex problems)
3. Clear evaluation metrics (R² + MSE verification)
4. Practical code structure (easy to extend)

For beginners, it is an excellent learning case that reflects the thinking mode of data science from business to application.