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Determine certain matrices to identify the star restaurants and generate recommendations

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Determine certain matrices to identify the star restaurants and generate recommendations.
Project 3 Gradable

DESCRIPTION

Data Analysis is the process of creating a story using the data for easy and effective communication. It mostly utilizes visualization
methods like plots, charts, and tables to convey what the data holds beyond the formal modeling or hypothesis testing task.
Domain: Marketing

Read the information given below and also refer to the data dictionary provided separately in an excel file to build your
understanding.

Problem Statement

A restaurant consolidator is looking to revamp its B-to-C portal using intelligent automation tech. It is in search of different matrix to identify and recommend restaurants. To make sure an effective model can be achieved it is important to understand the behaviour of the data in hand.

Importing, Understanding, and Inspecting Data
1. Perform preliminary data inspection and report the findings as the structure of the data, missing values, duplicates, etc.
2. Based on the findings from the previous questions, identify duplicates and remove them

Performing EDA:
3. Explore the geographical distribution of the restaurants and identifying the cities with the maximum and minimum number of
restaurants
4. Restaurant franchising is a thriving venture. So, it becomes very important to explore the franchise with most national presence
5. What is the ratio between restaurants that allow table booking vs that do not allow table booking?
6. What is the percentage of restaurants providing online delivery?
7. Is there a difference in number of votes for the restaurants that deliver and the restaurant that don’t?

Performing EDA:
8. What are the top 10 cuisines served across cities?
9. What is the maximum and minimum number of cuisines that a restaurant serves? Also, which is the most served cuisine across
the restaurant for each city?
10. What is the distribution cost across restaurants?
11. Explore how ratings are distributed
12. Explain the factors in the data that may have an effect on ratings e.g. No. of cuisines, cost, delivery option, etc.

Dashboarding:
13. Visualize the variables using Tableau to help user explore data and create a better understanding of the restaurants to identify
the “star” restaurant
14. Demonstrate the variables associated with each other and factors to build a dashboard