I'm doing a project on housing price prediction using machine learning, and want to submit it to a private firm for an application. I am working in the Jupiter notebook for this project, but I am unable to fix the bugs regarding conversion for word strings to numerical data from sklearn.model_selection import train_test_split X_train,X_test, Y_train, Y_test= train_test_split(X, Y, test_size=0.2, random_state=0) from sklearn.preprocessing import StandardScaler independent_scalar = StandardScaler() X_train = independent_scalar.fit_transform (X_train) #fit and transform X_test = independent_scalar.transform (X_test) # only transform print(X_train) I am expecting a training set data to be completely numerical Continue reading...
