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.
How to scrape Just Eat restaurant data in Node.js using an Apify actor.
Professional Python scraper for extracting restaurant data from Balad map | Auto-categorizes landline & mobile numbers | JSON & Excel output | No login required
A data science solution for NYC Department of Health restaurant inspection with Excel and Tableau
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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.
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A SQL + visualization project analyzing India's restaurant landscape through the Swiggy dataset. Explores 61,425 restaurants across 8 cities using structured queries and presents the findings through an editorial-style interactive dashboard — built entirely in HTML, CSS, and JavaScript without any framework.
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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A restaurant analytical tool + sales forecasting model
Machine Learning through Yelp-Classified Restaurant Attributes
Performed beginner-level EDA on a restaurant dataset using Python. Analyzed top cuisines, city-wise ratings, price ranges, and online delivery impact using Pandas and Matplotlib. Includes 4 well-structured notebooks with visual insights.
Exploratory data analysis on pizza restaurant data from 5,050 orders using Python code, SQL queries, and Tableau visualizations.
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