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---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- Ammok/laptop_price_prediction
---
# Model Trained Using AutoTrain
- Problem type: Tabular regression
## Validation Metrics
- r2: 0.7067895702353126
- mse: 10324863219600.982
- mae: 1934271.3093846152
- rmse: 3213232.518757549
- rmsle: 0.2620544321124841
- loss: 3213232.518757549
## Best Params
- learning_rate: 0.032035042723876625
- reg_lambda: 2.018311481741709e-06
- reg_alpha: 0.026605527978495237
- subsample: 0.7597204784105835
- colsample_bytree: 0.9197387798773331
- max_depth: 9
- early_stopping_rounds: 477
- n_estimators: 20000
- eval_metric: rmse
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
predictions = model.predict(data) # or model.predict_proba(data)
# predictions can be converted to original labels using label_encoders.pkl
```
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