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.
Professional Python scraper for extracting restaurant data from Balad map | Auto-categorizes landline & mobile numbers | JSON & Excel output | No login required
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.
How to scrape Just Eat restaurant data in Node.js using an Apify actor.
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.
A data science solution for NYC Department of Health restaurant inspection with Excel and Tableau
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India restaurant listings: ratings, cuisines and cost for two. Python, Node.js and cURL clients for the Swiggy Scraper on Apify, pay per result.
swiggy restaurant data extractor
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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).
US restaurants contact data
OpenRice scraper: export Hong Kong restaurants (ratings, prices, opening hours, GPS) to CSV or JSON. Python, no dependencies.
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.
Exploratory data analysis on pizza restaurant data from 5,050 orders using Python code, SQL queries, and Tableau visualizations.
Data analysis, visualization, and business insights from restaurant datasets using Python, Pandas, Matplotlib, and Seaborn.
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