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metadata
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

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