ValueError: X has 70 features per sample; expecting 49793

[jyotirani3020]

I saved the classifier model created by your code. I'm Trying to predict the classes for other text but I'm little stuck. Please help.

Saving the model

import pickle
with open('/content/drive/My Drive/Text-Sum/classification_model.pkl', 'wb') as fid:
    pickle.dump(clf, fid)

text = '''
Good morning everyone. The financial results for Q1 have been reviewed by you
during the weekend and since we had a couple of days I will take a bit of a time
to explain the results also a little bit more in detail because you might have
already gone through that. So starting with the ECD segment, which has grown
by 24%, which is a followup on 30% growth in the financial year 2019. In fact in
the first quarter it was 40%. The compounded growth is thus close to 26%,
which is higher than industry. Fans have grown mid-teens while small domestic
appliances, water heaters, water purifiers have performed significantly better.
We have established a clear leadership in water heaters with impressive market
gains in small domestic appliances as well. Fans continue to grow and
consolidate its premium positioning. We feel that ECD would anchor superior
growth mantle for Havells.
'''
import pickle
loaded_model = pickle.load(open('/content/drive/My Drive/Text-Sum/classification_model.pkl', 'rb'))

tfidf_vec = TfidfVectorizer(tokenizer=preprocessing,
                            stop_words=stopwords.words('english'),
                            sublinear_tf=True,
                            use_idf=True,
                            max_df = 1, min_df = 1
                            )


X_test = tfidf_vec.fit_transform([text])
prediction = loaded_model.predict(X_test)

This is giving following error

 ---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-44-3b2e1324941c> in <module>()
----> 1 prediction = loaded_model.predict(X_test)

1 frames
/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_base.py in decision_function(self, X)
    271         if X.shape[1] != n_features:
    272             raise ValueError("X has %d features per sample; expecting %d"
--> 273                              % (X.shape[1], n_features))
    274 
    275         scores = safe_sparse_dot(X, self.coef_.T,

ValueError: X has 70 features per sample; expecting 49793