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.
A data science solution for NYC Department of Health restaurant inspection with Excel and Tableau
A restaurant analytical tool + sales forecasting model
Solution of Mock Paper 2023 NAVTTC IBA CICT
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.
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.
An aggregator of restaurant data.
A SQL-driven project that integrates menu and order data to reveal insights on dish performance, customer preferences, and spending trends. It informs pricing strategies, menu adjustments, and targeted promotions, ultimately enhancing the overall customer experience and driving business growth.
Analyzed 28,000+ UK restaurant records using MongoDB and PyMongo. Queried hygiene scores, location data, and customer ratings.
Discover hidden patterns in dining data — from popular cuisine pairings to geographic restaurant clusters
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