Zomato Project.

Project Description: Analysis of Restaurant Preferences and Ordering Trends on Zomato
In this project, we explore and analyze various aspects of customer behavior and restaurant performance using Zomato's data. Our goal is to derive actionable insights that can help enhance customer experience and optimize restaurant offerings.

Objectives:
Restaurant Popularity Analysis:

Identify Popular Restaurant Types: Determine which types of restaurants receive the most votes from customers. This will help us understand which categories are most favored and could guide marketing strategies.
Vote Distribution by Restaurant Type:

Quantify Votes for Each Type: Calculate the total number of votes each type of restaurant has received. This will provide a clear picture of customer preferences across different restaurant categories.
Rating Trends:

Analyze Rating Distribution: Examine the ratings that the majority of restaurants have received. This will help identify the overall satisfaction level of customers and the general quality of dining experiences.
Couple Spending Patterns:

Average Spending Analysis: Analyze the average spending per order for couples who frequently order online. This insight will assist in understanding spending behaviors and potential revenue generation from this demographic.
Mode of Ordering Performance:

Evaluate Ratings by Ordering Mode: Compare the ratings received by online versus offline orders to determine which mode is preferred and delivers higher customer satisfaction.
Offline Ordering Trends:

Identify High-Order Restaurant Types: Find out which types of restaurants receive more offline orders. This information can be used to tailor promotions and offers for specific restaurant categories, enhancing customer engagement.
Methodology:
Data Collection:

Utilize Zomato’s API or available datasets to gather comprehensive data on restaurant types, votes, ratings, and ordering modes.
Data Cleaning and Preparation:

Clean the dataset to handle missing values, standardize categories, and ensure data accuracy.
Data Analysis:

Employ statistical and data visualization tools to aggregate votes, analyze ratings, and explore spending patterns.
Use tools like Python (Pandas, Matplotlib, Seaborn), R, or Excel for data processing and visualization.
Insights and Recommendations:

Generate insights based on the analysis and provide actionable recommendations for restaurant marketing strategies and customer engagement.
This project aims to provide a detailed understanding of customer preferences and behaviors, enabling Zomato to make data-driven decisions to improve user experience and offer targeted promotions.