<jupyter_start><jupyter_text>AUC ROC for all the models: 0 - Logistic regression - 0.7062881 - Logistic with L1 regularisation - 0.7059352 - Logistic with L2 regularisation - 0.706100 3 - Logistic with elastic net - 0.705746 4 - Logistic with elastic and L1 ratio -0.705682Choosing l2 model due to higher AOC.<jupyter_code>#using L2 to predict test set 
#standardising test data 
df_test = scaler.fit_transform(df_test_original)

y_test = lr_l2.predict_proba(df_test)

print(y_test)
final_pred =pd.DataFrame(y_test[:,1], index=df_test_original['Id'])
final_pred.rename( columns={0 :'TARGET_5Yrs'}, inplace=True )
final_pred.to_csv('../data/processed/Assignment1.csv' )<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10744985673352435, 'fraction_numerical': 0.07163323782234957, 'mean_word_length': 5.6571428571428575, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '621842', 'license': 'FTL', 'n_tokens_mistral': 284, 'n_tokens_neox': 253, 'n_words': 62, 'path': 'notebooks/PriyaRamakrishnan_week1_NBACareerPrediction.ipynb', 'repo_name': 'pri-mdsi/NBACareerPrediction', 'size': '698'}