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I successfully trained my model using the sklearn's multiple linear regression. This is the code I used:

import pandas as pd

dataset = pd.read_csv('C:\\mylocation\\myfile.csv')
dataset2 = pd.get_dummies(dataset)
y = dataset.iloc[:, 31:32].values
dataset2.pop('Target')
X = dataset2.iloc[:, :180].values

#Split the dataset
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 0)

#Feature Scaling
#from sklearn.preprocessing import StandardScaler

#sc_X = StandardScaler()
#X_train = sc_X.fit_transform(X_train)
#X_test = sc_X.transform(X_test)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

#Predicting the Test set results
y_pred = regressor.predict(X_test)

According to validation results my y_pred is a reasonable predictor. Now, I would like to take into production this model and I am wondering what are reasonable steps to apply this model to the whole dataset I have stored and future datasets if needed.

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You should first cross validate your pipeline, making sure that you get an homogeneous y_pred result.

Then you can retrain a model with the same parameters on your full dataset. Pickle the model as well as any preprocessor tools, and reuse them to predict on new data.

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  • $\begingroup$ Thanks. Could you possibly give a few pointers on how to do this specifically? $\endgroup$ – Taylrl Dec 31 '18 at 11:27
  • $\begingroup$ Check pickle, that's the only thing you need. $\endgroup$ – Matthieu Brucher Dec 31 '18 at 11:32
  • $\begingroup$ Ok thanks. Could you provide a bit more clarity about what I have to do to retrain the model with the same parameters, based on the code I have above? $\endgroup$ – Taylrl Dec 31 '18 at 14:23
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    $\begingroup$ regressor.fit(X, y) $\endgroup$ – Matthieu Brucher Dec 31 '18 at 14:28
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    $\begingroup$ No worries. If X is your new data, then yes, this will work. $\endgroup$ – Matthieu Brucher Dec 31 '18 at 14:33

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