<jupyter_start><jupyter_text>Miner AlgorithmsPlease refer to the documentation here:https://pm4py.fit.fraunhofer.de/documentationitem-3-1<jupyter_code># Alpha Miner implementation

from pm4py.algo.discovery.alpha import algorithm as alpha_miner
net, initial_marking, final_marking = alpha_miner.apply(event_log)

# Inductive Miner implementation
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
net, initial_marking, final_marking = inductive_miner.apply(event_log)<jupyter_output><empty_output><jupyter_text>Visualisations<jupyter_code># Visualisation of IM

from pm4py.visualization.process_tree import visualizer as pt_visualizer
tree = inductive_miner.apply_tree(event_log)

gviz = pt_visualizer.apply(tree)
pt_visualizer.view(gviz)

# Converting process tree in petri net

from pm4py.objects.conversion.process_tree import converter as pt_converter
net, initial_marking, final_marking = pt_converter.apply(tree, variant=pt_converter.Variants.TO_PETRI_NET)
gviz = pt_visualizer.apply(tree)
pt_visualizer.view(gviz)
# Dotted chart
pm4py.view_dotted_chart(event_log, format="svg")<jupyter_output><empty_output><jupyter_text>Decision tree & Root Cause Analysis<jupyter_code>from pm4py.objects.log.util import get_class_representation , get_log_representation
from sklearn import tree
data, feature_names = get_log_representation.get_default_representation(event_log)

#changing into pandas data frame
dataframe = pd.DataFrame(data, columns=feature_names)

#creating decision tree
target, classes = get_class_representation.get_class_representation_by_trace_duration(event_log, 11007360)
clf = tree.DecisionTreeClassifier()
clf.fit(data, target)
#visualizing decision tree 


from pm4py.visualization.decisiontree import visualizer as dectree_visualizer
from pm4py.objects.log.util import get_class_representation
gviz = dectree_visualizer.apply(clf, feature_names, classes)
target, classes = get_class_representation.get_class_representation_by_trace_duration(event_log, 11007360)
dectree_visualizer.view(gviz)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07364341085271318, 'fraction_numerical': 0.01308139534883721, 'mean_word_length': 9.27363184079602, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 1, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '62589', 'license': 'MIT', 'n_tokens_mistral': 726, 'n_tokens_neox': 669, 'n_words': 127, 'path': 'notebooks/root-cause-analysis.ipynb', 'repo_name': 'sami5001/hosp-billing-PM4py', 'size': '2064'}