<jupyter_start><jupyter_text>ROC Curve<jupyter_code>ns_probs = [0 for _ in range(len(y_test))]
ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)
lr_fpr, lr_tpr, _ = roc_curve(y_test, y_pred)
plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')
plt.plot(lr_fpr, lr_tpr, marker='.', label='SVM')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.show()

import pickle

with open('svm.pkl','wb') as f:
    pickle.dump(svm,f)
    print('done...!!!')<jupyter_output>done...!!!
{'fraction_non_alphanumeric': 0.1944990176817289, 'fraction_numerical': 0.0019646365422396855, 'mean_word_length': 6.083333333333333, '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': '178967', 'license': 'Apache-2.0', 'n_tokens_mistral': 244, 'n_tokens_neox': 232, 'n_words': 39, 'path': 'Loan_Application/.ipynb_checkpoints/train_model-checkpoint.ipynb', 'repo_name': 'kalpeshsnaik09/data_science_portfolio', 'size': '509'}