Assignment | Predictive Analytics

MIS772 Predictive Analytics (2019 T2) Individual Assignment A1 / Workshops M1T1-M1T3
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MIS772 Predictive Analytics (2019 T2) Individual Assignment A1 / Workshops M1T1-M1T3
The case study that is considered for this research is ZOMATO. This is a restaurant searching as well as discovering
application for the food services in India. This application is used throughout India and all over the world. This
application helps its customer to order food from its nearby restaurants instantly.
The data that has been used for this research was collected using Zomato API. The data includes detailed
information about all restaurants registered by the application. The data includes the name of the restaurants, the
locations, the cuisines, ratings as well as all other related demographics that a customer wants for visiting a
restaurant or ordering food from there. There is a unique ID for all the restaurants registered in the application.
The intention behind this analysis is to study how the sales are affected by different factors like Location, Cuisine,
Ratings, etc and what is specifically dominating in an area concentrating more towards Indian market.
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MIS772 Predictive Analytics (2019 T2) Individual Assignment A1 / Workshops M1T1-M1T3
Data exploration and preparation in RapidMiner (one page)
For analyzing the data, only some of the demographics have been taken from all the data that has been gathered
from Zomato API. The demographics include url of the restaurant, address and the phone number of the restaurant
that needs to be removed.
Now considering two features from the collected data that is menu_item and the reviews_list.
The feature of menu_list includes the names of all the dishes that are available in restaurant. This will not impact the
analysis that is being carried out. The nalysis that is being carried out is highly mathematical driven. There are also
other features such as rest_type, listed_in (type), dish_liked and the cuisines that have been taken for this data
analysis. These features will provide a clear idea about the offers that the restaurants provides. There is no need to
study all the dishes that are available in those restaurant as because it is not required in the analysis. In the
review_list feature, all the reviews that the customers gives to the restaurant is included. The data that has been
taken is the data from the website of Zomato (Bangalore). This feature is also not required for this particular analysis
as there is no numerical data available in the review_list feature. Other features that are taken are rate and votes
which includes the necessary information about the analysis. In the rate feature, there is string data character as ‘/’.
This is omitted from the analysis. This ‘/’ character can be removed from the data by making the feature of data to
float.
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