data science project presentation.

1. Shop
Recommendation
System
PBS Group No.:22
Guide .: Sapana Kolambe Mam

2. • SAARA BANSODE - S1951006
• GAURI NALAWADE - S1951050
• JAY TATHOD - S1951019
• VEDANT BHUGUL - S1951130
• AMEYA KANNURKAR - S1951042
Group Members:

3. Introduction
If one needed to find a particular service one can search it. A list of
services will be displayed nearby you or the location you along with it the
description of the same will be there whether it may be the location contact number
rating review and all so that you can easily verify which particular service is
convenient and better for you

4. RECOMMENDATION SYSTEM:
• SEARCH PARTICULAR SERVICE
• A LIST OF SERVICES WILL GET
DISPLAYED NEARBY YOU
• DESCRIPTION OF THAT PARTICULAR
SERVICE
• NEARBY SERVICES RELATED TO IT
WILL BE SHOWN

5. Literature Review

6. The problem of shop recommendation
hospital and it's resources recomendation
Offshop
Problems Defination
We often visit medical facilities like hospitals and once we do, the Next step is
to purchase medicine, get a blood test, etc.
In situations such as these, we aim to provide a system where just at one’s fingertip
they will have information on all the medical services nearby.
solution

7. Propose System Architecture

8. Proposed Algorithm and
Methodology
Decision Tree
Decision Tree is a Supervised learning technique that can be used for both
classification and Regression problems.
In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node.
WHY decision tree ?
Decision trees provide an effective method of Decision
Making because they:
Clearly lay out the problem
01
02
03
Allow us to analyze.
Provide a framework to quantify.

9. Decision Tree Figure

10. Work Flow

11. Conclusion
AS CONCLUSION,
THIS SYSTEM PROPOSES A MODEL
THAT COMBINES LOCALIZATION,
PERSONALIZATION AND CONTENT-
BASED RECOMMENDATION.
• THIS SYSTEM HAS TWO MAIN USER WHICH IS
SHOP STAFF/OWNER AND CUSTOMER.
• This system aimed to help customer in getting
better experience.
• The common problems have also been
addressed.
• The architecture is kept minimal and light.
• Recommender systems continue to evolve on a
daily basis, and we believe we have contributed
to this evolution.

12. FUTURE SCOPE
This is idea can be
implemented in
various other
services and stores
such as grocery,
beauty, textile,
automobile
services, etc.
We are also trying to
add an extra feature to
the system like Number
of people already
present on the venue .
Online booking
,reservation and
payment if required and
also lookout for offer
and discounts.
It can be used to
improve the
shopping
experience.

13. REFERENCES:
Wikipedia:-
https://en.wikipedia.org/wiki/Recommender_system#:~:text=A%20recomm
ender%20system%2C%20or%20a,would%20give%20to%20an%20item.
Reference Paper 1 :-
https://drive.google.com/file/d/1SluN7t4ohaOj0t7OBPPxBNsk18mv3ZIr/vi
ew?usp=drivesdk
Reference paper 2:-
https://scholar.google.co.in/scholar?q=Location+Based+Personalized+Re
staurant+Recommendation+System+for+Mobile+Environments&hl=en&
as_sdt=0&as_vis=1&oi=scholart#d=gs_qabs&u=%23p%3DhjHDl214W