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Restaurant Insights & Recommendation System

Project Type: Dashboard-Based Data Analysis

Tool Used: Tableau and Excel

Objective: Analyze and visualize restaurant data to identify high-performing establishments and uncover key patterns in customer preferences, service availability, cost, and cuisine offerings.

Project Overview This project focuses on building a comprehensive, interactive dashboard solution for a restaurant consolidator looking to revamp its B2C portal. The objective is to identify 'star restaurants' based on multiple performance factors, visualize restaurant behavior across geographies, and deliver actionable insights through effective data storytelling. The analysis was conducted using a combination of two datasets: Restaurant Data: Includes information on ratings, votes, delivery options, cuisine types, cost, and location.

Country Code Data: Provides mapping between country codes and names.

Methodology Data Cleaning and Preparation: • Removed duplicate records and handled missing values • Merged datasets for meaningful country-level insights Exploratory Data Analysis: • Assessed restaurant distribution by city and country • Analyzed availability of online delivery and table booking • Identified most and least popular cuisines and service patterns • Compared customer engagement (votes) based on delivery status • Evaluated price ranges and rating distributions across service types Star Score Calculation: • A custom scoring metric combining average rating, number of votes, online delivery, and table booking availability • Used to rank and surface high-performing or 'star' restaurants Dashboard Design

Market Overview Dashboard • Restaurant count by city and country • Table booking and online delivery availability ratios • Rating text distribution and delivery-based engagement

Star Restaurants Dashboard • Top-rated restaurants based on custom Star Score • Restaurants with highest average cost • Max/min cuisine variety per restaurant • Price range segmentation

Cuisine and Cost Analysis Dashboard • Top 10 most popular cuisines • Most served cuisine by city • Distribution of cost for two • Comparative analysis of ratings across services and cost levels

Key Insights • Certain cuisines (e.g., North Indian) dominate the market across multiple cities • Online delivery and table booking options are positively correlated with higher votes and ratings • High pricing does not always result in higher customer ratings • A small number of international franchises have a strong multi-country presence

Conclusion This project delivers a scalable, visual, and interactive solution for understanding restaurant performance and customer behavior. By combining metric-based scoring with clear visual analysis, the dashboards provide decision-makers with powerful tools to identify standout restaurants, optimize services, and improve user experience on consumer platforms.

About

A restaurant consolidator is looking to revamp the B2C portal using intelligent automation tech. This requires a different matrix to identify the star restaurants and generate recommendations. To make sure an effective model can be achieved, it is important to understand the behavior of the data in hand.

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