Abstract
References
- I.H. Sarker, "Machine learning: Algorithms, real-world applications and research directions." SN computer science 2.3 ,2021: 160.
- Agand, Pedram, et al. "Fuel consumption prediction for a passenger ferry using machine learning and in-service data: A comparative study." Ocean Engineering 284 (2023): 115271.
- S. Buyrukoğlu, and Y.Yılmaz,"An Approach for Airfare Prices Analysis with Penalized Regression Methods." Veri Bilimi 4.2 pp.57-61, 2021.
- M.Asghar, K.Mehmood, S.Yasin and Z. M.Khan,”Used Cars Price Prediction using Machine Learning with Optimal Features”. Pakistan Journal of Engineering and Technology, 4(2), pp:113-119, 2021.
- P.Venkatasubbu and M.Ganesh, “Used Cars Price Prediction using Supervised Learning Techniques”. Int . J. Eng. Adv. Technol. (IJEAT), 9(1S3),2019.
- P. Rane, D.Pandya, D.Kotak,“ Used car price prediction “International Research Journal of Engineering and Technology (IRJET),2021.
- P. Gajera, A. Gondaliya, and J.Kavathiya, “Old Car Price Predict ion with Machine Learning”. Int. Res. J. Mod. Eng. Technol. Sci, 3, pp:284-290,2021.
- S.Snehit, P.Borugadda, and N.Koshika. "Car Price Prediction: An Application of Machine Learning." 2023 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2023.
- Kaggle [Online] Available: https://www.kaggle.com/datasets/uciml/autompg-dataset [Accessed Sept. 11, 2023].
- O.G.,Uzut, S.Buyrukoglu, “Prediction of real estate prices with data mining algorithms”. Euroasia Journal of Mathematics, Engineering, Natural and Medical Sciences.pp:77-84,2020, https://doi.org/10.38065/euroasiaorg.81
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- G.James,D.Witten,T. Hastie, and R.Tibshirani, An introduction to statistical learning, Vol. 112, pp: 18,. New York: springer, 2013.
- J.Han, J.Pei, M.Kamber. Data mining: concepts and techniques. Amsterdam: Elsevier; 2011.
- F.Pedregosa, G.Varoquaux, A. Gramfort,V.Michel, B.Thirion. O.Grisel,M. Blondel, P.Prettenhofer, R.Weiss,V.Dubourg et al.”Scikit-learn: machine learning in python”. J Mach Learn Res.12:2825–30, 2011.
- J. S.Chou, and , A. D.Pham. “Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength.”, Construction and Building Materials,49, pp:554-563,2013.
- P. N.Reddy and J. A. Naqash. “Strength prediction of high early strength concrete by artificial intelligence” Int J Eng Adv Technol, 8(3), pp:330-334,2019
- D. C.Feng, Z. T.Liu, X. D., Wang, Y .Chen, J. Q. Chang, D. F.Wei and Z. M. Jiang, “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach”. Construction and Building Materials, 230, 117000, 2020.
- T. Fushiki,”Estimation of prediction error by using K-fold cross-validation”. Statistics and Computing, 21(2), pp:137-146, 2011.
Abstract
Fuel consumption and efficiency have emerged as pressing concerns in the context of growing energy sources and increasing environmental awareness. Machine learning, a subset of artificial intelligence, leverages intricate data structures and variable information to make predictions. These algorithms play a pivotal role in modeling and forecasting across diverse industries like healthcare, finance, banking, and energy.
This study offers a comprehensive overview of a typical machine learning project flow, with a particular focus on fuel prediction. The project encompasses key stages such as data collection, data preparation, model development, and evaluation. The methodologies and algorithms employed in this research hold the potential for broader applications in various forecasting projects and industry sectors.
In this investigation, fuel estimation was carried out using a set of features from the Auto MPG Data Set, sourced from the University of California. These features included Mpg (fuel consumption), Number of Cylinders, Engine Volume, Horsepower, Vehicle Weight, Acceleration, Model Year, Vehicle Origin, and Vehicle Name. Various regression algorithms, namely Linear Regression, Ridge Regression, Lasso Regression, and XGBoost, were applied to predict fuel consumption. The study's outcomes were generated by splitting the dataset into training and test data subsets. Notably, the Lasso Regression algorithm outperformed the others when evaluating the regression models using performance metrics.
References
- I.H. Sarker, "Machine learning: Algorithms, real-world applications and research directions." SN computer science 2.3 ,2021: 160.
- Agand, Pedram, et al. "Fuel consumption prediction for a passenger ferry using machine learning and in-service data: A comparative study." Ocean Engineering 284 (2023): 115271.
- S. Buyrukoğlu, and Y.Yılmaz,"An Approach for Airfare Prices Analysis with Penalized Regression Methods." Veri Bilimi 4.2 pp.57-61, 2021.
- M.Asghar, K.Mehmood, S.Yasin and Z. M.Khan,”Used Cars Price Prediction using Machine Learning with Optimal Features”. Pakistan Journal of Engineering and Technology, 4(2), pp:113-119, 2021.
- P.Venkatasubbu and M.Ganesh, “Used Cars Price Prediction using Supervised Learning Techniques”. Int . J. Eng. Adv. Technol. (IJEAT), 9(1S3),2019.
- P. Rane, D.Pandya, D.Kotak,“ Used car price prediction “International Research Journal of Engineering and Technology (IRJET),2021.
- P. Gajera, A. Gondaliya, and J.Kavathiya, “Old Car Price Predict ion with Machine Learning”. Int. Res. J. Mod. Eng. Technol. Sci, 3, pp:284-290,2021.
- S.Snehit, P.Borugadda, and N.Koshika. "Car Price Prediction: An Application of Machine Learning." 2023 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2023.
- Kaggle [Online] Available: https://www.kaggle.com/datasets/uciml/autompg-dataset [Accessed Sept. 11, 2023].
- O.G.,Uzut, S.Buyrukoglu, “Prediction of real estate prices with data mining algorithms”. Euroasia Journal of Mathematics, Engineering, Natural and Medical Sciences.pp:77-84,2020, https://doi.org/10.38065/euroasiaorg.81
- T.Hastie, R.Tibshirani, J. H.Friedman and J. H Friedman. The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer,2009.
- G.James,D.Witten,T. Hastie, and R.Tibshirani, An introduction to statistical learning, Vol. 112, pp: 18,. New York: springer, 2013.
- J.Han, J.Pei, M.Kamber. Data mining: concepts and techniques. Amsterdam: Elsevier; 2011.
- F.Pedregosa, G.Varoquaux, A. Gramfort,V.Michel, B.Thirion. O.Grisel,M. Blondel, P.Prettenhofer, R.Weiss,V.Dubourg et al.”Scikit-learn: machine learning in python”. J Mach Learn Res.12:2825–30, 2011.
- J. S.Chou, and , A. D.Pham. “Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength.”, Construction and Building Materials,49, pp:554-563,2013.
- P. N.Reddy and J. A. Naqash. “Strength prediction of high early strength concrete by artificial intelligence” Int J Eng Adv Technol, 8(3), pp:330-334,2019
- D. C.Feng, Z. T.Liu, X. D., Wang, Y .Chen, J. Q. Chang, D. F.Wei and Z. M. Jiang, “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach”. Construction and Building Materials, 230, 117000, 2020.
- T. Fushiki,”Estimation of prediction error by using K-fold cross-validation”. Statistics and Computing, 21(2), pp:137-146, 2011.
Details
| Primary Language | English |
|---|---|
| Subjects | Machine Learning (Other), Data Engineering and Data Science, Modelling and Simulation |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 15, 2023 |
| Submission Date | September 11, 2023 |
| Published in Issue | Year 2023 Volume: 3 Issue: 2 |
All articles published by JAIDA are licensed under a Creative Commons Attribution 4.0 International License.
