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--- |
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tags: |
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- sklearn |
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- regression |
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- automotive |
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- mpg |
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--- |
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# Auto MPG Predictor |
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This model predicts a vehicle's fuel efficiency (miles per gallon) based on its characteristics. |
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## Model Details |
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- Model type: Linear Regression |
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- Input features: cylinders, displacement, horsepower, weight, acceleration, model_year, origin_Japan, origin_USA |
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- Target: mpg (miles per gallon) |
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## How to Use |
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```python |
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from huggingface_hub import hf_hub_download |
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import joblib |
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# Download model and scaler |
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model_path = hf_hub_download(repo_id="your-username/auto-mpg-regressor", filename="auto_mpg_regressor.joblib") |
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scaler_path = hf_hub_download(repo_id="your-username/auto-mpg-regressor", filename="scaler.joblib") |
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model = joblib.load(model_path) |
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scaler = joblib.load(scaler_path) |
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# Prepare input data (same order as features in config) |
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import numpy as np |
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sample_input = np.array([[6, 225, 100, 3233, 15.4, 76, 0, 1]]) # Example input |
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# Preprocess |
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scaled_input = scaler.transform(sample_input) |
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# Predict |
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prediction = model.predict(scaled_input) |
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print(f"Predicted MPG: {prediction[0]}") |
