A restaurant analytical tool + sales forecasting model
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Updated
Jul 4, 2020 - Jupyter Notebook
A restaurant analytical tool + sales forecasting model
Exploratory Data Analysis (EDA) on Bengaluru restaurant data to uncover insights into ratings, cuisines, cost, location, and dining trends. Built using Python, Pandas, Seaborn, and Matplotlib to understand customer behavior and food business patterns.
End-to-end Zomato restaurant data analysis across 15 countries using Excel, Power BI, Tableau & MySQL — covering SQL normalization, KPI dashboards, pricing analysis & geographic insights. Built during Ai Variant Internship.
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Analyzed restaurant data to uncover insights on ratings, cuisines, and pricing. Used Python (Pandas, Seaborn, Matplotlib) for EDA and visualizations. Highlights include top-rated cuisines, pricing trends, and location-based analysis to support business decisions.
This project focuses on analyzing global restaurant data to uncover meaningful insights into customer preferences, pricing trends, and service availability. The dataset includes information such as restaurant names, locations, cuisines, ratings, price ranges, and services offered (e.g., online delivery, table booking).
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