An Optimal Approach on Electric Vehicle by using Functional Learning
Keywords:
EV cars, Logistic Learning, MLP, Deep Learning, SMOAbstract
This study measures EV attributes that affect consumer attitudes. This study measures consumer attitude and intent to buy. This is done to determine if EV attributes affect innovation attitudes and consumer purchase intent. The Logistic learning shows the highest efficiency compare with other models which is 91.26% accuracy, 0.91 precision value, 0.91 of recall value, 0.91 F-Measure value, 0.89 MCC value , 0.98 ROC value and 0.96 PRC value. The Logistic learning and MLP shows that the same value as well highest efficiency compare with other models which is 0.89 Kappa value. The SMO shows the least efficiency which is 88.27% accuracy. The DL4MLP shows the least efficiency compare with other models which is 0.88 precision value, 0.86 recall value,0.86 F-Measure value, 0.83 MCC value, 0.83 Kappa value and the SMO shows the least efficiency compare with other models which is 0.96 ROC value, and 0.85 PRC value. The SMO takes least time consumption for making its model; the DL4MLP takes huge time consumption for making its model. The study found that the selected attributes positively affect consumers' attitudes towards electric cars. The respondent's attitude was also found to be statistically significant for their future purchase intention. Attributes were unrelated to intent to buy.
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https://www.kaggle.com/datasets/geoffnel/evs-one-electric-vehicle-dataset
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