Authors :
A Varun Kiran; Dr. Jebakumar R.
Volume/Issue :
Volume 7 - 2022, Issue 1 - January
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/3GVWJuX
DOI :
https://doi.org/10.5281/zenodo.5897944
Abstract :
The aim of this research is to develop a model to
predict the price of a mobile when the specifications of a
mobile are given and to find the ML algorithm that
predicts the price most accurately. The usage of archival
data to accurately forecast forthcoming instances is the
essence of Predictive Analytics. One of the ways Predictive
Analytics can be performed is by using Machine Learning.
Predictive Machine Learning works by taking in data as
input to develop and train a prediction model and the
trained model is used to predict the outcome of future data
instances. Supervised Machine Learning algorithms make
use of data that contains a pre-defined class label, which is
the attribute that needs to be predicted. The class label is
the price of a mobile in our case. The Mobile Price Class
dataset sourced from the Kaggle data science community
website (https://www.kaggle.com/iabhishekofficial/mobileprice-classification) that categorizes mobiles into price
ranges was used to train the prediction model. Python is
used due to its readily accessible ML libraries. Various
classification algorithms were used to train the model to try
and find the algorithm that is able to predict the mobile
price class most accurately. Metrics like accuracy score,
confusion matrix, etc. are were used to evaluate the trained
model to determine the algorithm most suitable among the
ones used.
Keywords :
Machine Learning, Predictive Analytics, Supervised Machine Learning, Python.
The aim of this research is to develop a model to
predict the price of a mobile when the specifications of a
mobile are given and to find the ML algorithm that
predicts the price most accurately. The usage of archival
data to accurately forecast forthcoming instances is the
essence of Predictive Analytics. One of the ways Predictive
Analytics can be performed is by using Machine Learning.
Predictive Machine Learning works by taking in data as
input to develop and train a prediction model and the
trained model is used to predict the outcome of future data
instances. Supervised Machine Learning algorithms make
use of data that contains a pre-defined class label, which is
the attribute that needs to be predicted. The class label is
the price of a mobile in our case. The Mobile Price Class
dataset sourced from the Kaggle data science community
website (https://www.kaggle.com/iabhishekofficial/mobileprice-classification) that categorizes mobiles into price
ranges was used to train the prediction model. Python is
used due to its readily accessible ML libraries. Various
classification algorithms were used to train the model to try
and find the algorithm that is able to predict the mobile
price class most accurately. Metrics like accuracy score,
confusion matrix, etc. are were used to evaluate the trained
model to determine the algorithm most suitable among the
ones used.
Keywords :
Machine Learning, Predictive Analytics, Supervised Machine Learning, Python.
