restaurant-analysis

Data Analysis Portfolio

This project delves into Swiggy's restaurant data, exploring various aspects and trends using Pandas and exploratory data analysis techniques.

Analysis of restaurant ratings in Mexico using Power BI, exploring consumer behavior, dining preferences, and service insights from 2012 data.

Zomato restaurant data analysis and Excel dashboard showing trends in cuisines, ratings, cost, delivery, and city-wise presence.

Zomato restaurant data analysis using Python, Pandas, NumPy, Matplotlib and Seaborn to explore restaurant trends, ratings, pricing, customer preferences, online delivery and business KPIs.

Restaurant Sales & Tipping Analysis using Python, Pandas & Matplotlib

Exploratory Data Analysis (EDA) of restaurant data using Python, Pandas, Matplotlib and Seaborn with business insights and visualizations.

This data science project aims to gather insights into various factors affecting restaurants

Pizza Sales Analysis Project: This project optimizes a pizza restaurant's operations by analyzing demand patterns, revenue, and efficiency, providing insights to enhance profitability, streamline production, and improve customer satisfaction.

Machine Learning project to analyze restaurant data, predict ratings, classify cuisines, and build a recommendation system.

Analyzing Zomato Bangalore restaurant data to uncover how cuisine, pricing, location, restaurant type, and online presence influence ratings and customer engagement.

Zomato Bangalore restaurant analysis using Python, SQL & Power BI | 51K+ restaurants | 11 SQL queries

Restaurant Data Analysis using Python, Pandas, Matplotlib, Seaborn, and Folium. Completed as part of the Cognifyz Technologies Data Analysis Internship.

Tableau dashboard analysing Zomato Bangalore restaurants

This project analyzes Zomato restaurant data—ratings, cuisines, and cost patterns—to uncover customer preferences, pricing trends, and restaurant performance across cuisines and locations, helping drive data-based decisions on menu strategy, pricing, and market positioning.

Data-driven market analysis of Swiggy restaurants to uncover top cuisines, delivery efficiency trends, and high-opportunity areas for new restaurant entry.

Performed exploratory data analysis on Bangalore Zomato’s restaurant dataset using Python, Pandas, and Matplotlib to uncover dining trends across major Indian cities. Cleaned and transformed raw data to extract insights. Visualized patterns in customer preferences and location-based popularity to support strategic decision-making of food service

Analyzing Zomato Bangalore restaurant data to uncover insights into the city's vibrant culinary scene. Explore trends in cuisine types, restaurant ratings, pricing, and more using data science techniques.

Exploratory Data Analysis of Zomato India Restaurants Dataset (200k+ records) — Insights on popularity, cuisine trends, price vs rating and service modes.

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).