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Journals / Turkish Journal of Forecasting / 2026 / Volume: 9 Issue: 1

An Analysis of Market Size Trends Forecasting and Range Prediction in Electric Vehicles Using Machine Learning Algorithms

Hakan Kaya(İstanbul Okan Üniversitesi)0000-0002-0812-4839
Pages
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Publication Date
2025-03-24
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Random Forest Regression is identified as the premier model for predicting electric vehicle range due to its superior performance metrics, such as a higher R-Squared value and lower mean squared error in comparison to other regression methods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electric vehicles face fundamental challenges primarily related to battery and charging systems.
📊
Key Findings & Data:The article contains 177866 data the task of performing a market size analysis for the Electric Vehicles sector using Python.
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Conclusion & Impact:Random Forest Regression is identified as the premier model for predicting electric vehicle range due to its superior performance metrics, such as a higher R-Squared value and lower mean squared error in comparison to other regression methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Compound Annual Growth Rate · Machine Learning · Market Size Analysis

Abstract

Electric vehicles face fundamental challenges primarily related to battery and charging systems. Conducting a market size analysis is an essential component of market research as it provides insights into the potential sales volume within a specific market. This study focuses on conducting a comprehensive analysis of market size within a EV industry segment, alongside predictions for the range. By leveraging data-driven approaches and predictive modelling techniques, insights into market dynamics and future trends are explored. The article contains 177866 data the task of performing a market size analysis for the Electric Vehicles sector using Python. Range estimation of the electric vehicle has been conducted using Linear, Random Forest, Ridge, Lasso, and Elastic Net Regression model types. When predicting range, performance metrics such as R-Squared, Adjusted R-Squared, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are used, while Compound Annual Growth Rate (CAGR) is utilized for current and estimated EV market size. Based on the findings, the Tesla brand is predominantly preferred. A consistent annual growth rate of 51% has been noted. Random Forest Regression is identified as the premier model for predicting electric vehicle range due to its superior performance metrics, such as a higher R-Squared value and lower mean squared error in comparison to other regression methods.

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Cite This Article

Kaya, H. (2025). An Analysis of Market Size Trends Forecasting and Range Prediction in Electric Vehicles Using Machine Learning Algorithms. Turkish Journal of Forecasting, Volume: 9 Issue: 1. https://doi.org/10.34110/forecasting.1485136

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