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.
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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).
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DoorDash scraper for Python and the command line: restaurants delivering in a US city, with rating, exact review count, cuisine, price band, address, delivery fee, delivery time, promotions and popular menu items with prices, as JSON or CSV. Free and open source; sign in with your QuanticData account, no proxies or headless browser to run.
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