Restaurants worldwide receive ratings from customers on Zomato, influenced by multiple factors such as cuisine type, price range, delivery options, and overall experience.
Understanding what drives these ratings is valuable for restaurant owners, analysts, and machine learning enthusiasts.

In this competition, your task is to predict the average restaurant rating (Aggregate rating) based on restaurant attributes such as cuisine, city, country, price range, and customer votes.

This is a regression problem, where you must build a model capable of estimating the rating value as accurately as possible.

The challenge is part of the ABES 24-Hour Data Science Challenge, designed to help students practice data cleaning, feature engineering, and predictive modeling on a real-world dataset.

Build a machine learning regression model to predict the Aggregate rating for each restaurant in the test dataset.