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laptop_price_prediction_v1
SenecaCloudG4/laptop_price_prediction_v1
laptop_price_prediction_v1 is a tabular regression model from SenecaCloudG4. Use it for the tabular regression task on the model card, and read the license before you ship it in a product.
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Public
Updated Mar 30, 2024
Repo size
17.3 MB
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.joblib17.3 MB · 100%
At a glance
- Task
- Tabular Regression
- Access
- Public
- Created
- Mar 30, 2024
- Updated
- Mar 30, 2024
- SHA
- 545de37e
Datasets
Model card
From the Hugging Face model README
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
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
- Task
- Tabular Regression
- Created
- Mar 30, 2024
- Updated
- Mar 30, 2024