<jupyter_start><jupyter_text>This is the model accuracy socre = 0.9838565022421525<jupyter_code>msg = input ('\nEnter a message : \n')
msg_trnf = cv.transform([msg])
predict_val = spam_detection_model.predict(msg_trnf)

if(predict_val[0]==1):
    print("\n################### SPAM MESSAGE ################### ")
else:
    print("\n################### NOT SPAM MESSAGE ################### ")<jupyter_output>Enter a message : 
You have received a gift .please click on this link

################### SPAM MESSAGE ###################
{'fraction_non_alphanumeric': 0.30566037735849055, 'fraction_numerical': 0.035849056603773584, 'mean_word_length': 6.585714285714285, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '295965', 'license': 'Unlicense', 'n_tokens_mistral': 180, 'n_tokens_neox': 169, 'n_words': 40, 'path': 'Spamclassifier.ipynb', 'repo_name': 'sumantas98/Spam-detection-project', 'size': '530'}