Zomato Food Data Mining Analysis

MIS772 Predictive Analytics (2019 T2) Individual Assignment A2 / All Workshops
Assignment A2: Text + Clustering + Estimation
Student
Name
(as per record) Student No Student number
My other group members A2
Group No
As per CloudDeakin group
number
Team
Names
(as per record) Student Nos Student number
(as per record) Student number
(as per record) Student number
Exceptional Meets expectations Issues noted Improve Unacceptable
Exec
Report
Use this area to self-assess your submission
Explore
Attributes
Be realistic as we will find problems in your work that you may not be aware of
Discover
Relationships
Create
Models
Evaluate &
Improve
Provide
Solution
Research &
Extend
Brief
Comments
Read these notes as we are really trying to help you out!
Remember: If it is not in this report, it does not exist and does not get marked!
Assume that markers could miss some important aspects of your submission unless presented clearly, or when
you deviate from the structure of this template (for which you will be penalised). So be clear, number tables,
charts and screen shots used as evidence, annotate all visuals, cross-reference your analysis with evidence.
Use the A2 Word template to prepare this report. Submit it in PDF format to avoid its accidental reformatting.
Submit all RM processes (.RMP files only – not the whole project directory or data) in a separate ZIP archive.
Only work submitted via CloudDeakin assignment box will be marked (not via email or any other way).
Ensure that the report is readable and the font is no smaller than Arial 10 points. Include only the most relevant
and significant results for your analysis and recommendations.
You will be able to submit your work as many times until deadline. We will mark the last complete submission,
i.e. the report in PDF and the ZIP-ped RapidMiner processes.
Go over this checklist: Is this your document? Does it report your work and your work only? Is this the correct
unit, assignment, year and trimester? Is your name entered above? Is the group number included and is it
correct? Are names of your group members entered as well? Are all pages included? Are all report sections
within the required page limit?
Then after the submission – check these: Was it lodged on time? Has the PDF report been submitted? Has the
Zip archive of RMP files been submitted? Can you retrieve and reopen both back from your submission folder?
We will be checking your work for plagiarism! If any parts of your work (report, screen shots or RM
processes) bear any resemblance to another students’ work, or by you for another unit, or anything
written by others without acknowledgement (e.g. on the web), it will be treated as plagiarism.
Total
1 of 14

MIS772 Predictive Analytics (2019 T2) Individual Assignment A2 / All Workshops
Executive summary (one page)
The fundamental point of this task to build up the Zomato nourishment food information
mining investigation systems they can utilized for the quick digger programming execution. The
examining on the Zomato food dataset they can contains the diverse activity information fields they can
handled. This present venture's point is spins around building up an information mining technique, which
guarantees to help during the time spent deciding if the cafés are required to give a few administrations
in particular the online super requesting just as booking the table to its clients for the Bangalore
nourishment help (BFA). BFA alludes to an Indian organization which is associated with the Zomato
café search and the disclosure site. By and large, BFA causes the organizations to give the example
audits of about 48000 Zomato food order booking system they can contained the data fields, which
involves the accompanying characteristics of the data attributes list,
 Restaurant name
 Restaurant sort
 Contact Number
 Location
 Address
 Neighbourhood
 Rate of visit
 Menu
 Cuisine and kind of dinners
 Average supper cost for the couple.
 Number of votes cast
 Liked dishes
 Reviews
The point of BFA is to accumulate couple of initial bits of knowledge of the ordering
food and delivery service in Bangalore, for investigating and to tidy up the convey their audits so as to
inspect and make eatery table's classifier for their table booking, for requesting on the web and to
diminish the orders that aren't right. The apparatus named Rapid Miner is used for the advancement of
BFA's information mining technique, and during the time spent information mining this device is
profoundly basic. The eatery surveys are investigated and tidied up with the assistance of Set job,
Normalize, Selecting Attributes on a Rapid Mining information mining method analysis
implementation. .
2 of 14

MIS772 Predictive Analytics (2019 T2) Individual Assignment A2 / All Workshops
For creating and investigating a classifier for the BFA eatery for Booking a table administration and
online dinner requesting administration, the two groupings in particular the Random timberland and neural nets
arrangements are utilized to diminish an inappropriate order. In this way, this report will quickly talk about and
examine the previously mentioned zones. The breaking down on the outcome to be confirmed on the diagram
and graph position.
3 of 14

MIS772 Predictive Analytics (2019 T2) Individual Assignment A2 / All Workshops
Data exploration and relationships - Clustering in Rapid Miner (one page)
Here, a model is made on the Rapid Miner with the assistance of the given information of
the café. The model creation uses the Random Forest and the generalized linear regression classifiers. The
underneath chart portrays the creation model for these two classifiers.
For making a model, at first it is required to incorporate the read CSV administrator for perusing
the information of the eatery. Next, it is required to supplant the missing qualities with the assistance of
the standardize administrator, as it may affect the consequences of our model. At that point, with the
assistance of the Random Forest and generalized linear regression classifiers administrator characterize
the mark credits to be anticipated. Further, the required characteristics are chosen for including it to
foresee the properties. The accompanying figure portrays the choice tree.
During the formation of the model, the exactness parameters are chosen on the classifier,
promotion it is used for estimating the indicator's precision and their presentation. Different parameters
and profundity and set as default. For demonstrating a viable model for Zomato eatery information BFA,
the Random Forest is used. The underneath figure delineates the yield of the generalized linear regression
classifiers calculation
The following list represents the attributes for exploring the given data:
1) Restaurant name
2) Restaurant type
3) Location
4) Address
5) Menu
6) Cuisine and type of meals
7) Reviews etc.
The following figure represents the data exploration and preparation.
4 of 14