Authors :
Katreddi.SAI SRINIVAS; Kadali.TARUN SAI; Kondaveeti.MOHAN SATYA SRIRAM; P.Srinu Vasa Rao
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/2ww35fx2
Scribd :
http://tinyurl.com/mrjxxfjr
DOI :
https://doi.org/10.5281/zenodo.10725482
Abstract :
Laptops are now became a most essential and
mostly used gadgets around the world . It is widely used
by the students, Employees and other working
professionals. Considering the present technical
advancements, the electronic gadgets like mobiles and
laptops are getting costly and many more companies and
models are evolving day by day. So it became difficult for
the customers and also the sellers in the aspect of
pricing.
To overcome this type of difficulties, we are building
a model to predict the price of laptops based on the
specifications they are made off. The price of the laptop
is predicted by taking some input values from the user
which were the specifications of the laptop such as
Company, Model name, Category, screen type, screen
resolution, CPU, RAM, Storage, GPU, Operating
System, Operating system Version and Weight.
This laptop price prediction model is developed by
using machine learning techniques. Leveraging historical
pricing data, features and market trends are
incorporated to train the model. The goal is to create an
accurate system capable of forecasting laptop prices and
helping the customers in making informed purchase
decisions. It also aims to investigate the impact of
external factors like technical advancements, economic
conditions and consumer preferences on laptop prices.
To build this model, various machine learning
algorithms such as linear regression, decision trees, and
ensemble methods will be explored to identify the most
suitable model for predicting laptop prices. Time-series
analysis may be incorporated to capture temporal
patterns in pricing fluctuations. The anticipated outcome
is a well-tuned machine learning model capable of
accurately predicting the laptop prices, offering a
valuable tool for both the customers and retailers in
understanding the pricing dynamics of the laptop
market.
This model helps the customers to find the price of
the laptop they are looking for according to their
required specifications and also to the retailers to fix the
price of the laptop according to the market trends and
other aspects.
Keywords :
Linear Regression, Decision Trees, Ensemble Methods, Time-Series Analysis, Machine Learning.
Laptops are now became a most essential and
mostly used gadgets around the world . It is widely used
by the students, Employees and other working
professionals. Considering the present technical
advancements, the electronic gadgets like mobiles and
laptops are getting costly and many more companies and
models are evolving day by day. So it became difficult for
the customers and also the sellers in the aspect of
pricing.
To overcome this type of difficulties, we are building
a model to predict the price of laptops based on the
specifications they are made off. The price of the laptop
is predicted by taking some input values from the user
which were the specifications of the laptop such as
Company, Model name, Category, screen type, screen
resolution, CPU, RAM, Storage, GPU, Operating
System, Operating system Version and Weight.
This laptop price prediction model is developed by
using machine learning techniques. Leveraging historical
pricing data, features and market trends are
incorporated to train the model. The goal is to create an
accurate system capable of forecasting laptop prices and
helping the customers in making informed purchase
decisions. It also aims to investigate the impact of
external factors like technical advancements, economic
conditions and consumer preferences on laptop prices.
To build this model, various machine learning
algorithms such as linear regression, decision trees, and
ensemble methods will be explored to identify the most
suitable model for predicting laptop prices. Time-series
analysis may be incorporated to capture temporal
patterns in pricing fluctuations. The anticipated outcome
is a well-tuned machine learning model capable of
accurately predicting the laptop prices, offering a
valuable tool for both the customers and retailers in
understanding the pricing dynamics of the laptop
market.
This model helps the customers to find the price of
the laptop they are looking for according to their
required specifications and also to the retailers to fix the
price of the laptop according to the market trends and
other aspects.
Keywords :
Linear Regression, Decision Trees, Ensemble Methods, Time-Series Analysis, Machine Learning.
