Authors :
Sakshi Rane; Shraddha Deskhmukh; Nidhee Agarwal; Aditya Verma; Dr. Amol Dhakne
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4hxev4b7
DOI :
https://doi.org/10.5281/zenodo.8330773
Abstract :
Students in Various colleges travel from far
away places for a better education. Here they often face
problem while looking for an affordable and safe
accommodation. Additionally, users often have to check
out the places as there is very little information in
amenities , location and user feedback. Not only this but
also often, people are too tired to prepare home-cooked
meals due to the fast-paced and busy environment where
they live. Furthermore, even if you eat only homemade
food every day, you are still likely to want to go out to eat
for social or recreational purposes from time to time.
Despite this, it's common knowledge that food is an
important aspect of anyone's lifestyle, no matter where
they live. Assume, for example, someone has just moved
to a new home. Their preferences and tastes are already
established. If a student lives nearby her favourite outlet,
this will save her a lot of trouble and help her save money.
Students nowadays also have to take care of their health
and fitness. Hence, recommendation for affordable gyms
has also become a requirement. Based on incoming
students' preferences for facilities, budget, and proximity
to the location, this project uses K-Means Clustering to
find the most suitable accommodation, restaurant as well
as gyms for them in Akurdi (Pune).
Keywords :
Geolocation data analysis, k- means clustering, python, recommendation.
Students in Various colleges travel from far
away places for a better education. Here they often face
problem while looking for an affordable and safe
accommodation. Additionally, users often have to check
out the places as there is very little information in
amenities , location and user feedback. Not only this but
also often, people are too tired to prepare home-cooked
meals due to the fast-paced and busy environment where
they live. Furthermore, even if you eat only homemade
food every day, you are still likely to want to go out to eat
for social or recreational purposes from time to time.
Despite this, it's common knowledge that food is an
important aspect of anyone's lifestyle, no matter where
they live. Assume, for example, someone has just moved
to a new home. Their preferences and tastes are already
established. If a student lives nearby her favourite outlet,
this will save her a lot of trouble and help her save money.
Students nowadays also have to take care of their health
and fitness. Hence, recommendation for affordable gyms
has also become a requirement. Based on incoming
students' preferences for facilities, budget, and proximity
to the location, this project uses K-Means Clustering to
find the most suitable accommodation, restaurant as well
as gyms for them in Akurdi (Pune).
Keywords :
Geolocation data analysis, k- means clustering, python, recommendation.
