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🤖 GPT
We appreciate the careful attention you've given to our offer and hope you'll make a purchase from us soon. In exchange for your money, the Automatic Machine Learning Bot provides access to a robust optimization model capable of making precise predictions of future occurrences. The model has a wide range of predictability, from the gás price to the bitcoin index. It's a helpful resource for financ
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$3.99
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Over 1 month ago
Prompt Details
Model
Completion - GPT-3.5 (text-davinci-003)
Token size
420 ($0.00840 / call)
Example input
Data to Predict: the price of bitcoin
Programming Language: python
Example output
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from tpot import TPOTClassifier
# Load your dataset
data = pd.read_csv('bitcoin_data.csv')
# Split your dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data.drop('price', axis=1), data['price'], test_size=0.2, random_state=42)
# Initialize the TPOT classifier
tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2, scoring='roc_auc')
# Fit the classifier to the training data
tpot.fit(X_train, y_train)
# Get the best model from the TPOT classifier
best_model = tpot.fitted_pipeline_
# Get the AUC score for the model on the test set
auc_score = roc_auc_score(y_test, best_model.predict(X_test))
print(f'AUC score for the best model: {auc_score}')