---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- Ammok/laptop_price_prediction
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.7770511763473569
- mse: 7850730654540.005
- mae: 1734575.7588461537
- rmse: 2801915.5330844657
- rmsle: 0.23713967369435024
- loss: 2801915.5330844657

## Best Params

- learning_rate: 0.02229837095040035
- reg_lambda: 2.510764141176911
- reg_alpha: 0.001531565861357925
- subsample: 0.8214234508684097
- colsample_bytree: 0.3555990037002663
- max_depth: 5
- early_stopping_rounds: 355
- 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

```