FOOD DELIVERY RATINGS DASHBOARD

Abstract

The Food Delivery Ratings Dashboard is a data-driven application designed to analyze customer ratings and feedback from food delivery services. Food delivery platforms generate large amounts of data through customer orders, restaurant information, delivery experiences, ratings, reviews, order amounts, and delivery times. Analyzing this information can help understand customer satisfaction and identify factors that influence ratings. The proposed system collects and processes information such as restaurant name, food category, customer rating, review, delivery time, order value, location, and order date. Data preprocessing techniques are applied to remove duplicate records, handle missing values, standardize ratings, and prepare textual and numerical data for analysis. The cleaned data is then used to generate meaningful analytical insights. The dashboard provides important metrics such as average rating, total reviews, positive and negative ratings, restaurant-wise performance, and rating distribution. Users can analyze ratings based on restaurants, food categories, locations, delivery times, and different time periods. Interactive charts, graphs, tables, and KPI cards make the information easier to understand. The system can also identify highly rated and low-rated restaurants or food categories based on the available data. Trend analysis can show how customer ratings change over time, while delivery-time analysis can help examine the relationship between delivery performance and customer ratings. These insights can support restaurants and delivery-management teams in improving service quality. Overall, the Food Delivery Ratings Dashboard provides a centralized platform for transforming customer rating data into useful business insights. It reduces manual analysis and supports data-driven decision-making. Future enhancements can include sentiment analysis, rating prediction, complaint classification, personalized restaurant recommendations, real-time review monitoring, and Machine Learning-based customer satisfaction analysis.