SenecaCloudG4/laptop_price_prediction_v1

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