<jupyter_start><jupyter_text>Training Model<jupyter_code>#logistic regression
lr = LogisticRegression(max_iter=20000,penalty='l2')
model1=lr.fit(x_train, y_train)
print("train accuracy:",model1.score(x_train, y_train),"\n","test accuracy:",model1.score(x_test,y_test))
lrpred = lr.predict(x_test)
print("\n")
print("classification report for logistic regression")
print(classification_report(lrpred,y_test))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14874141876430205, 'fraction_numerical': 0.020594965675057208, 'mean_word_length': 14.642857142857142, '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': '21323', 'license': 'MIT', 'n_tokens_mistral': 168, 'n_tokens_neox': 157, 'n_words': 20, 'path': 'heart-failure-prediction-logistic-regression.ipynb', 'repo_name': 'mayurdalvi5/Heart-Failure-Prediction-Logistic_Regression-', 'size': '437'}