<jupyter_start><jupyter_text>Apply SVM algorithm<jupyter_code>from sklearn.svm import SVR
class_svr=SVR(kernel='rbf')
class_svr.fit(X_train,y_train)
y_pred_ran=class_svr.predict(X_test)
#y_pred_ran=np.absolute(y_pred_ran)

from sklearn.metrics import mean_squared_log_error
svm_error=mean_squared_log_error(y_test,y_pred_ran,multioutput='raw_values')
final_ran_error=100*svm_error
final_ran_error
y_pred_svr=class_svr.predict(test_processed)
df_submission=pd.DataFrame({'id':test.index,'num_orders':y_pred_ran_output})
df_submission.to_csv('rand_reg.csv',index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11222780569514237, 'fraction_numerical': 0.005025125628140704, 'mean_word_length': 18.29032258064516, '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': '638967', 'license': 'MIT', 'n_tokens_mistral': 256, 'n_tokens_neox': 246, 'n_words': 20, 'path': 'Notebook1-SVM.ipynb', 'repo_name': 'Vkrntkmrsngh/Genpact-hackathon', 'size': '597'}