Have a piece of code where i am cleaning the text from the 'Description' column and storing it as "cleaned"
Then i create a ML model using the above as one of my features.
X = data[['originalname','cleaned']] Y = data['Total score'] X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size=0.2,random_state=42) pipeline = Pipeline([('vect', TfidfVectorizer(ngram_range=(1, 2), stop_words="english", sublinear_tf=True)), ('chi', SelectKBest(chi2, k='all')), ('clf', LinearSVC(C=1.0, penalty='l1', max_iter=300, dual=False))]) X_train.shape--->(44, 2) y_train.shape--->(44,)
Trying to train the model gives me the above error
model = pipeline.fit(X_train, y_train)
How do i use both 'original description' and 'cleaned' as my feature to predict 'Total score' without the above error ?