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---
license: mit
tags:
- tabular-classification
- pytorch
- electric-vehicles
- binary-classification
model-index:
- name: Electric Vehicle Type Classifier
results:
- task:
type: tabular-classification
name: Tabular Classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9989
---
# 🚗 Electric Vehicle Type Classifier
## Model Description
This is a PyTorch-based neural network designed to classify electric vehicles as either:
- **Battery Electric Vehicle (BEV)**
- **Plug-in Hybrid Electric Vehicle (PHEV)**
The model uses structured tabular data such as make, model, year, range, and price to predict the EV type. It is lightweight and optimized for fast inference on small-scale datasets.
---
## 🧠 Model Architecture
- **Input Layer**: 9 features (e.g., make, model, year, range, price, etc.)
- **Hidden Layers**: [128, 64, 32] neurons with ReLU activations
- **Output Layer**: 2 neurons (BEV vs PHEV), softmax activation
- **Loss Function**: CrossEntropyLoss
- **Optimizer**: Adam
- **Accuracy**: ~87% on test set (replace with actual)
---
## 📦 Usage
```python
import torch
from model import TabularModel # Ensure this matches your module structure
# Load model checkpoint
checkpoint = torch.load('ev_classifier_model.pth')
model = TabularModel(input_size=9, hidden_sizes=[128, 64, 32], output_size=2)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
# Example inference
sample = torch.tensor([[0.5, 0.3, 2022, 250, 35000, 1, 0, 0.8, 0.6]]) # Replace with actual feature values
output = model(sample)
predicted_class = torch.argmax(output, dim=1)
print("Predicted class:", predicted_class.item()) # 0 = BEV, 1 = PHEV
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