Choosing food from a restaurant menu can sometimes be difficult because customers have different tastes, dietary preferences, and budgets. This project is an AI-powered restaurant food recommendation system that uses Machine Learning to suggest suitable food items based on user preferences.

The application analyzes user inputs and recommends food items using a trained Machine Learning model. This project demonstrates how AI can improve user experience by providing personalized food recommendations.

Problem Statement

Customers often spend time searching through large restaurant menus to find suitable food items. This project aims to simplify the decision-making process by recommending dishes based on user preferences using Machine Learning.

Features

Food recommendation based on user preferences.

Machine Learning-based recommendation system.

Simple and user-friendly interface.

Quick recommendation results.

Personalized food suggestions.

Technology Stack

Programming Language

Python

Libraries and Frameworks

Scikit-learn

Pandas

NumPy

Streamlit

Machine Learning Model

Decision Tree Classifier

Dataset

The recommendation model was trained using a restaurant food dataset containing different food items and their attributes. Before training, the dataset was cleaned and preprocessed to improve the model's performance.

Project Workflow

Collect the restaurant food dataset.

Clean and preprocess the data.

Train the Decision Tree Classifier model.

Save the trained model.

Develop the user interface using Streamlit.

Accept user preferences as input.

Generate food recommendations.

Display the recommended food items.

Challenges Faced

One of the main challenges was preparing the dataset and selecting relevant features for generating meaningful recommendations. I also worked on improving data preprocessing and testing different user inputs to ensure consistent recommendations.

What I Learned

Through this project, I gained practical experience in Machine Learning, Scikit-learn, data preprocessing, feature selection, model training, Streamlit application development, and building recommendation systems.

Future Improvements

Improve recommendations using a larger dataset.

Implement collaborative and content-based recommendation techniques.

Include nutritional information and calorie details.

Provide personalized recommendations based on previous user choices.

Deploy the application on a cloud platform.

Conclusion

This project demonstrates how Machine Learning can be used to build a restaurant food recommendation system that provides personalized suggestions based on user preferences. It strengthened my understanding of data preprocessing, feature engineering, Scikit-learn, Decision Tree Classifier, Streamlit, and the complete workflow of developing and deploying a Machine Learning application.