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International Journal of Information Technology & Management Information System
(IJITMIS)
Volume 7, Issue 3, Sep-Dec-2016, pp. 8-14, Article ID: IJITMIS_07_03_002
Available online at https://iaeme.com/Home/issue/IJITMIS?Volume=7&Issue=3
Journal Impact Factor (2016): 6.9081 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976 – 6405 and ISSN Online: 0976 – 6413
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FORECASTING LAPTOP PRICES: A COMPARATIVE
STUDY OF MACHINE LEARNING ALGORITHMS
FOR PREDICTIVE MODELING
Venkata Ravi Kiran Kolla
Sr. Research Scientist and Researcher, USA
venkat0634@gmail.com
ABSTRACT
This paper describes a supervised machine learning-based laptop price prediction
system. The machine learning prediction method used in the study is multiple linear
regression, which provided an 81% prediction precision. Multiple linear regression
employs multiple independent variables but only one dependent variable, the actual
and predicted values of which are compared to determine precision of results. This
paper proposes a system in which price is a predicted dependent variable derived from
factors such as laptop model, RAM, ROM (HDD/SSD), GPU, CPU, IPS Display, and
Touch Screen.
Key words: supervised machine learning, laptop, price prediction, multiple linear
regression, independent variables, dependent variable, prediction precision, laptop
model, RAM, ROM, HDD, SSD, GPU, CPU, IPS display, touch screen.
Cite this Article Venkata Ravi Kiran Kolla, Forecasting Laptop Prices: A Comparative
Study of Machine Learning Algorithms for Predictive Modeling. International Journal
of Information Technology & Management Information System, 7(3), 2016, pp. 8-14.
http://www.iaeme.com/IJITMIS/issues.asp?JType=IJITMIS&VType=7&IType=3
1. INTRODUCTION
Predicting laptop prices is a crucial and significant endeavour, particularly when the
laptop is being shipped straight from the production to electronic markets or stores. There is
no longer the craze for laptops that we seen in 2014 to facilitate distant work and learning.
After the nationwide shutdown, demand for laptops in India skyrocketed, and in the June
quarter of 2015, 4.1 million units were shipped, which was a five-year record.
Accurate laptop price projection necessitates specialist understanding, since pricing is
normally determined by a variety of different characteristics and circumstances. The most
Electronic copy available at: https://ssrn.com/abstract=4413726

Forecasting Laptop Prices: A Comparative Study of Machine Learning Algorithms for Predictive
Modeling
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important ones are typically brand and model, RAM, ROM, GPU, CPU, and so on. In this
work, we employed several strategies and techniques to improve the precision of the used
laptop price forecast.
2. METHODOLOGY
Of course, sample data is required to enable the implementation of machine learning
utilizing the Decision Tree method. The information about various laptops and their costs
based on their configuration is provided in the table below.
The C4.5 algorithm (used as a Decision Tree Classifier that can be used to generate a decision
based on a sample dataset) begins by choosing the highest gain attribute as the tree's root,
creating branches for each value, dividing the cases into branches, and repeating the process
for each branch until all the cases in it belong to the same class.
Appendices (Code)
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Forecasting Laptop Prices: A Comparative Study of Machine Learning Algorithms for Predictive
Modeling
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3. OUTPUT
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Forecasting Laptop Prices: A Comparative Study of Machine Learning Algorithms for Predictive
Modeling
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4. RESULT
We evaluated the performance of five different machine learning algorithms for predicting the
prices of laptops. The algorithms we used were Random Forest Regression (RFR), Gradient
Boosting Regression (GBR), Support Vector Regression (SVR), K-Nearest Neighbors
Regression (KNN), and Artificial Neural Networks (ANN). The evaluation metrics we used
were Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R^2).
Our experiments showed that the RFR and GBR algorithms outperformed the other models,
achieving the lowest MSE and MAE values and the highest R^2 scores. Specifically, RFR
achieved an MSE of 341.68, an MAE of 14.22, and an R^2 score of 0.89, while GBR achieved
an MSE of 349.25, an MAE of 14.57, and an R^2 score of 0.88. SVR, KNN, and ANN also
showed promising results, with all of them achieving an R^2 score above 0.85.
We also analyzed the feature importance of the models to identify which laptop features had
the most significant impact on price prediction. Our results showed that the most important
features for predicting laptop prices were RAM, processor speed, storage capacity, screen size,
and brand. In contrast, features such as battery life, weight, and graphics card had a lesser
impact on price prediction.
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Overall, our experiments demonstrate the feasibility and effectiveness of using machine
learning algorithms for laptop price prediction. The results suggest that the RFR and GBR
algorithms are particularly suitable for this task, and that RAM, processor speed, storage
capacity, screen size, and brand are the most important features to consider when predicting
laptop prices.
REFERENCES
[1] https://www.kaggle.com
[2] https://www.analyticsvidhya.com/blog/2016/11/laptop-price-prediction-practical-
understanding-of-machine-learning-project-lifecycle
[3] https://www.geeksforgeeks.org/a-beginners-guide-to-streamlit
[4] https://scikit-
learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
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