<jupyter_start><jupyter_text>Load and test the best Model<jupyter_code>my_model = joblib.load("best_model_second_try.pkl")

# https://www.propertyvalue.com.au/property/505/2-st-georges-terrace-perth-wa-6000/13040441 
X_test = X_scaler.transform([[2021,2,2,1,107,86,2002,1,0,0,0,0,0,31.95691,115.86532]])
predictions = my_model.predict(X_test)
results = y_scaler.inverse_transform(predictions)
results
# https://www.propertyvalue.com.au/property/11/375-hay-street-perth-wa-6000/13032624
X_test = X_scaler.transform([[2021,1,1,1,84,52,2008,1,0,0,0,0,0,31.95697,115.86710]])
predictions = my_model.predict(X_test)
results = y_scaler.inverse_transform(predictions)
results
# https://www.propertyvalue.com.au/property/15-throssell-street-perth-wa-6000/13042748
X_test = X_scaler.transform([[2021,5,3,2,821,216,1945,1,0,0,0,0,0,31.93695,115.85955]])
predictions = my_model.predict(X_test)
results = y_scaler.inverse_transform(predictions)
results<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16597938144329896, 'fraction_numerical': 0.16288659793814433, 'mean_word_length': 8.247619047619047, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 3, 'lorem ipsum': 0, 'www.': 3, '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': '142949', 'license': 'MIT', 'n_tokens_mistral': 499, 'n_tokens_neox': 418, 'n_words': 31, 'path': 'Notebooks/ML-Load and test the best model.ipynb', 'repo_name': 'helenamin/Project3', 'size': '970'}