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.