Sample code for doing Log Event correlation using ML model

import pandas as pd

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.cluster import DBSCAN

# Load log data into a pandas DataFrame

log_data = pd.read_csv('log_file.csv')

# Extract relevant features for correlation (e.g., log message content)

features = log_data['log_message']

# Convert log messages to feature vectors using TF-IDF vectorization

vectorizer = TfidfVectorizer()

X = vectorizer.fit_transform(features)

# Perform clustering to identify correlated log events

clustering_model = DBSCAN(eps=0.5, min_samples=2)

clusters = clustering_model.fit_predict(X)

# Add cluster labels to the log data

log_data['cluster_label'] = clusters

# Print the correlated log events

unique_clusters = log_data['cluster_label'].unique()

for cluster in unique_clusters:

    cluster_data = log_data[log_data['cluster_label'] == cluster]

    print("Cluster Label:", cluster)

    print(cluster_data['log_message'])

    print("------------------------------------")

Sample data for this is 

log_message

Application started

Invalid input detected

Processing completed

Low disk space

Connection timed out

Database connection failed