save machine learning model
# fit the model
model.fit(X_train, y_train)
# save the model
import pickle
pickle.dump(model, open("model.pkl", "wb"))
# load the model
model = pickle.load(open("model.pkl", "rb"))
# use model to predict
y_pred = model.predict(X_input)sklearn save model
# save as object
import pickle
s = pickle.dumps(clf)
clf2 = pickle.loads(s)
# save to file
from joblib import dump
dump(clf, 'filename.joblib')
# load from file
from joblib import load
clf = load('filename.joblib')joblib
from sklearn.externals import joblib
joblib.dump(svc,'project21-carcomforttype.obj')
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model=joblib.load('project21-carcomforttype.obj')
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Final_predictions=model.predict(x_test)
Final_predictions=pd.DataFrame(Final_predictions,columns=['unacc(0),acc(1),good(2),vgood(3)'])
Final_predictions[:5]
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Final_predictions.sample(n=10)
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#Saving final predictions in file.csv format
Final_predictions.to_csv('E:\\final_predictionscarcomforttype',index=False)Copyright © 2021 Codeinu
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